HEART SOUND AND ECG PROCESSING

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Welcome to Advisors, we are business & financial experts, marketing consultants based in Berlin, Germany.

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This Free online course will teach you how to crush it with YT!Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.


Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.



Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.



Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.



Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.


Recently, new advances and emerging technologies in healthcare and medicine are growing rapidly allowing automatic disease diagnosis. The healthcare technology involves both in monitoring devices and signal processing procedure in a store-and-forward (e.g. cloud-based), real-time (embedded) or offline manner. Advanced signal processing and analysis techniques have been successfully applied in various science areas, thus adoption of such methods for biomedical signal processing is an important field of research—especially in developing countries. In this paper, the signal processing techniques are more specifically applied to ECG and heart sound (PCG) signals as part of biomedical signals. However, other technologies such as echocardiography, MRI, and computed tomography can provide a more precise diagnosis of heart disease. These technologies require a specialized cardiologist to operate. Additionally, these equipments are not available in all hospitals due to their high cost. Therefore, it is very convenient to use the easily accessible tools such as ECG and heart sound (PCG) for monitoring and early detection of cardiac diseases. ECG and heart sound (PCG) analysis methods with acceptable accuracies are required to reduce the need for using costly diagnosis procedure.

Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph.




















Variations from the normal electrical pattern make a big damage to the heart, these variations are manifested as heart attack or heart disease. Both ECG and heart sound (PCG) are non-invasive tests which play important roles in heart abnormality detection; however, diagnosis based on ECG signal or heart sound (PCG) signal alone cannot detect all cases of heart symptoms. In other words, the ECG signal is assumed to be a more efficient diagnosis tool than heart sound (PCG). There are heart defects that cannot be detected using ECG but can be detected with heart sound (PCG); mainly the problems are related to heart valves and heart murmurs. Moreover, heart sound (PCG) could reveal some heart abnormalities before they can be manifested on the ECG graph. 





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ECG PROCESSING RELATED PROBLEM

Automatic detection of life-threatening cardiac arrhythmias has been a subject of interest for many decades. The automatic ECG signal analysis methods are mainly aiming for interpretation of long-term ECG recordings. In fact, the experienced cardiologists perform the ECG analysis using a strip of ECG graph paper in an event-by-event manner. This manual interpretation becomes more difficult, time-consuming, and more tedious when dealing with long-term ECG recordings. Rather, an automatic computerized ECG analysis system will provide a valuable assist to the cardiologists to deliver a fast or remote medical advice and diagnosis to the patients [2]. However, achieving accurate automated arrhythmia diagnosis is a challenging task that has to account for all the ECG characteristics and possessing steps [3].

ECG recordings exhibit a wide variety of morphological changes, especially with the existence of different types of artifacts. In the preprocessing phase, particularly, the ECG noise filtering, denoising and enhancement approaches must be carefully designed, so that the clinically relevant information is not destroyed and reserved for the next processing steps. The process of finding the onset and offset of ECG waves are difficult tasks due to: (1.) the non-stationarity nature of ECG and dynamic change of heart behavior, (2.) the unpredictable nature of high peaked noises, baseline drifts, muscular and motion artifacts. In some cases, ECG components such as P and T waves tend to show high amplitudes compared to QRS complex. Hence, a fully adaptive approach can perform better in almost all cardiological conditions.


ECG recordings exhibit a wide variety of morphological changes, especially with the existence of different types of artifacts. In the preprocessing phase, particularly, the ECG noise filtering, denoising and enhancement approaches must be carefully designed, so that the clinically relevant information is not destroyed and reserved for the next processing steps. The process of finding the onset and offset of ECG waves are difficult tasks due to: (1.) the non-stationarity nature of ECG and dynamic change of heart behavior, (2.) the unpredictable nature of high peaked noises, baseline drifts, muscular and motion artifacts. In some cases, ECG components such as P and T waves tend to show high amplitudes compared to QRS complex. Hence, a fully adaptive approach can perform better in almost all cardiological conditions.

The feature extraction is a process of extracting a lower dimensional data that represents the raw ECG signals. The selection of the optimal feature set from the mass of features to improve the classification performance is a challenging task and still an open-end problem. This is because, in clinical practice, different diseases are characterized by different features (symptoms). Thus, the performance of ECG classification highly depends on the preprocessing and feature extraction stages.  In addition, the ECG classification performance measure is strongly relying on the classification method and the evaluation scheme of the classification process. So far, there is no universal standard for the best automatic method that fit the need of clinical monitoring applications.

HEART SOUND(PCG)PROCESING RELATED PROBLEMS

The computer-based heart sound (PCG) segmentation and classification methods are still not an end-to-end task, the process involves several tasks and challenges to overcome. First, the raw heart sound (PCG) signals contain a variety of noise components that may destroy the clinically related information leading to false alarms diagnosis. Second, an accurate localization of heart sound (PCG) fundamental components, mislocating the heart sounds will lead to wrong segmentation of heart sounds or cardiac cycles which in turn results in a less precise diagnosis. Third, the extraction of relevant features that best represent the heart sound (PCG) properties, the features should provide distinct measures towards the diagnostic of pathologic heart sound (PCG) signals. Finally, the conducted classification method must involve various cases of heart sound (PCG) signal abnormalities which requires a large amount of database to build a strong classifier. In fact, a normal to abnormal heart database is usually imbalanced, a careful design of a classifier may reduce the amount of bias and over/under-fitting.

The The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.



Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.


Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.



Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.


The process of heart sound (PCG) data collection is usually implemented in noisy environments, allowing various types of noise to be added to the base heart sound (PCG) signal. The spectral contents of these noises show significant overlaps with that of heart sound (PCG) components which make the elimination of such noise to be a more difficult task. Unfortunately, the noise sometimes destroys the useful information of heart sounds making the diagnosis to be almost impossible even for expert cardiologists.

Heart sound (PCG) segmentation is essential and perhaps the most difficult step in heart sound (PCG) processing. Basically, the fundamental heart sounds can be found by locating the R-peak and T-wave offset in the ECG signal. Unfortunately, using the ECG signal as a reference to segment heart sound (PCG) is not always an easy task due to: (1.) it requires a synchronous recording of ECG and heart sound (PCG) signals, (2.) an accurate detection of T-wave offset is difficult sometimes, (3.) and the temporal alignment of ECG-PCG is not constant in all cases. The use of machine learning methods in heart sound (PCG) segmentation requires various and large amount of features extracted in both univariate or multivariate forms. Which will lead to another issue on selecting the best set of features that give the best heart sound (PCG) segmentation performance. Featureless methods based on strong statistical models could solve the problem of feature extraction and reduce the overall computational cost of the heart sound (PCG) segmentation approach.

HS SEGMENTATION

HS SEGMEMENTATION


Similar to ECG classification, the heart sound (PCG) segments classification relies on how the involved database represents the varying heart sound (PCG) morphologies. The selection of the database has a direct impact on ensuring the computational transportability of the proposed machine learning methods. Previous studies have been conducted on several self-collected and non-publicly accessible databases which could be chosen with minimal noise effects. Recently, the Physionet computing in cardiology (CinC) challenge 2016 released the largest publicly heart sound (PCG) real-world noisy database labeled by an expert cardiologist into normal and abnormal classes. Regardless of the type of selected classifier, many classification approaches have been reported with high accuracies (over 90%) on different datasets, but this does not directly mean that these methods will produce a satisfactory performance on the standard noisy database. The conducted evaluation scheme of the classifier also has a significant impact on the reliability of the proposed method; for example, (1.) online or offline evaluation of the experiments, (2.) class-oriented or subject-oriented train and test data partitioning, (3.) balanced or imbalanced samples in each class, (4.) the unclassifiable heart sounds must be considered prior or within the classification stage, (5.) the classification basis is either performed in beat-level, recording-level or both.



HS SEGMEMENTATION


Similar to ECG classification, the heart sound (PCG) segments classification relies on how the involved database represents the varying heart sound (PCG) morphologies. The selection of the database has a direct impact on ensuring the computational transportability of the proposed machine learning methods. Previous studies have been conducted on several self-collected and non-publicly accessible databases which could be chosen with minimal noise effects. Recently, the Physionet computing in cardiology (CinC) challenge 2016 released the largest publicly heart sound (PCG) real-world noisy database labeled by an expert cardiologist into normal and abnormal classes. Regardless of the type of selected classifier, many classification approaches have been reported with high accuracies (over 90%) on different datasets, but this does not directly mean that these methods will produce a satisfactory performance on the standard noisy database. The conducted evaluation scheme of the classifier also has a significant impact on the reliability of the proposed method; for example, (1.) online or offline evaluation of the experiments, (2.) class-oriented or subject-oriented train and test data partitioning, (3.) balanced or imbalanced samples in each class, (4.) the unclassifiable heart sounds must be considered prior or within the classification stage, (5.) the classification basis is either performed in beat-level, recording-level or both.


HS SEGMEMENTATION


Similar to ECG classification, the heart sound (PCG) segments classification relies on how the involved database represents the varying heart sound (PCG) morphologies. The selection of the database has a direct impact on ensuring the computational transportability of the proposed machine learning methods. Previous studies have been conducted on several self-collected and non-publicly accessible databases which could be chosen with minimal noise effects. Recently, the Physionet computing in cardiology (CinC) challenge 2016 released the largest publicly heart sound (PCG) real-world noisy database labeled by an expert cardiologist into normal and abnormal classes. Regardless of the type of selected classifier, many classification approaches have been reported with high accuracies (over 90%) on different datasets, but this does not directly mean that these methods will produce a satisfactory performance on the standard noisy database. The conducted evaluation scheme of the classifier also has a significant impact on the reliability of the proposed method; for example, (1.) online or offline evaluation of the experiments, (2.) class-oriented or subject-oriented train and test data partitioning, (3.) balanced or imbalanced samples in each class, (4.) the unclassifiable heart sounds must be considered prior or within the classification stage, (5.) the classification basis is either performed in beat-level, recording-level or both.


HS SEGMEMENTATION


Similar to ECG classification, the heart sound (PCG) segments classification relies on how the involved database represents the varying heart sound (PCG) morphologies. The selection of the database has a direct impact on ensuring the computational transportability of the proposed machine learning methods. Previous studies have been conducted on several self-collected and non-publicly accessible databases which could be chosen with minimal noise effects. Recently, the Physionet computing in cardiology (CinC) challenge 2016 released the largest publicly heart sound (PCG) real-world noisy database labeled by an expert cardiologist into normal and abnormal classes. Regardless of the type of selected classifier, many classification approaches have been reported with high accuracies (over 90%) on different datasets, but this does not directly mean that these methods will produce a satisfactory performance on the standard noisy database. The conducted evaluation scheme of the classifier also has a significant impact on the reliability of the proposed method; for example, (1.) online or offline evaluation of the experiments, (2.) class-oriented or subject-oriented train and test data partitioning, (3.) balanced or imbalanced samples in each class, (4.) the unclassifiable heart sounds must be considered prior or within the classification stage, (5.) the classification basis is either performed in beat-level, recording-level or both.


HS SEGMEMENTATION


Similar to ECG classification, the heart sound (PCG) segments classification relies on how the involved database represents the varying heart sound (PCG) morphologies. The selection of the database has a direct impact on ensuring the computational transportability of the proposed machine learning methods. Previous studies have been conducted on several self-collected and non-publicly accessible databases which could be chosen with minimal noise effects. Recently, the Physionet computing in cardiology (CinC) challenge 2016 released the largest publicly heart sound (PCG) real-world noisy database labeled by an expert cardiologist into normal and abnormal classes. Regardless of the type of selected classifier, many classification approaches have been reported with high accuracies (over 90%) on different datasets, but this does not directly mean that these methods will produce a satisfactory performance on the standard noisy database. The conducted evaluation scheme of the classifier also has a significant impact on the reliability of the proposed method; for example, (1.) online or offline evaluation of the experiments, (2.) class-oriented or subject-oriented train and test data partitioning, (3.) balanced or imbalanced samples in each class, (4.) the unclassifiable heart sounds must be considered prior or within the classification stage, (5.) the classification basis is either performed in beat-level, recording-level or both.

REFERENCES

[1]            WHO, “Cardiovascular diseases (CVDs),” 2017. [Online]. Available: http://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). [Accessed: 21-May-2018].

[2]            S. Raj, K. C. Ray, and O. Shankar, “Cardiac arrhythmia beat classification using DOST and PSO tuned SVM,” Comput. Methods Programs Biomed., vol. 136, 2016.

[3]            J. A. Gutiérrez-Gnecchi et al., “DSP-based arrhythmia classification using wavelet transform and probabilistic neural network,” Biomed. Signal Process. Control, vol. 32, 2017.

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