Abstract: A process for pre-processing recorded and digitized heart sounds and corresponding digitized ECG signals is provided. The pre-processed signals (203) are suitable for input into an automatic decision support system implemented by means of a diagnostic decision network (205), used for diagnosing and differentiation between normal/functional (206) and pathological (207) heart murmurs, particularly in pediatric patients. The process includes identifying or predicting locations of individual heart beats within the heart sound signal, identifying the positions of the S1 and S2 heart pulses within the respective heart beats, predicting and identifying the locations and durations of the systole and diastole segments of the heart beats (302), determining if segmentation of the heart sound signal is possible based on the selected and isolated heart beats (305), and the segmentation of the respective heart beats into segments for allowing better feature extraction.

Abstract: A method and system for electronically classifying a pre-processed heart sound signal of a patient as functional (normal) or pathological is provided. The pre-processed patient heart sound signal is segmentised and features are extracted therefrom (104) to build up a feature vector which is representative of the heart sound signal. The feature vector is then fed to a diagnostic decision support network (105) comprising a plurality of artificial neural networks, each relating to a known heart pathology, which is in turn used to conduct the classification.

Abstract: A method and system for electronically classifying a pre-processed heart sound signal of a patient as functional (normal) or pathological is provided. The pre-processed patient heart sound signal is segmentised and features are extracted therefrom (104) to build up a feature vector which is representative of the heart sound signal. The feature vector is then fed to a diagnostic decision support network (105) comprising a plurality of artificial neural networks, each relating to a known heart pathology, which is in turn used to conduct the classification.

Abstract: A method and system for electronically classifying a pre-processed heart sound signal of a patient as functional (normal) or pathological is provided. The pre-processed patient heart sound signal is segmentised and features are extracted therefrom (104) to build up a feature vector which is representative of the heart sound signal. The feature vector is then fed to a diagnostic decision support network (105) comprising a plurality of artificial neural networks, each relating to a known heart pathology, which is in turn used to conduct the classification.

Abstract: A process for pre-processing recorded and digitised heart sounds and corresponding digitised ECG signals is provided. The pre-processed signals (203) are suitable for input into an automatic decision support system implemented by means of a diagnostic decision network (205), used for diagnosing and differentiation between normal/functional (206) and pathological (207) heart murmurs, particularly in paediatric patients. The process includes identifying or predicting locations of individual heart beats within the heart sound signal, identifying the positions of the S1 and S2 heart pulses within the respective heart beats, predicting and identifying the locations and durations of the systole and diastole segments of the heart beats (302), determining if segmentation of the heart sound signal is possible based on the selected and isolated heart beats (305), and the segmentation of the respective heart beats into segments for allowing better feature extraction.