Key result
The proposed algorithm based on signal abstraction and parameter extraction from relative maximum peaks successfully detected and delimited cardiac events, including murmurs, outperforming traditional threshold-based methods.
The proposed signal processing algorithm accurately detects and separates cardiac events in phonocardiograms, overcoming limitations of traditional threshold-based methods in the presence of murmurs.
May aid automated murmur detection in phonocardiograms; leaves open prospective clinical validation before practice adoption.
The auscultation of the heart is still the first basic analysis tool used to evaluate the functional state of the heart, as well as the first indicator used to submit the patient to a cardiologist. In order to improve the diagnosis capabilities of auscultation, signal processing algorithms are currently being developed to assist the physician at primary care centers for adult and pediatric population. A basic task for the diagnosis from the phonocardiogram is to detect the events (main and additional sounds, murmurs and clicks) present in the cardiac cycle. This is usually made by applying a threshold and detecting the events that are bigger than the threshold. However, this method usually does not allow the detection of the main sounds when additional sounds and murmurs exist, or it may join several events into a unique one. In this paper we present a reliable method to detect the events present in the phonocardiogram, even in the presence of heart murmurs or additional sounds. The method detects relative maxima peaks in the amplitude envelope of the phonocardiogram, and computes a set of parameters associated with each event. Finally, a set of characteristics is extracted from each event to aid in the identification of the events. Besides, the morphology of the murmurs is also detected, which aids in the differentiation of different diseases that can occur in the same temporal localization. The algorithms have been applied to real normal heart sounds and murmurs, achieving satisfactory results.
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Martínez-Alajarín et al. (2005) studied Heart murmurs and abnormal heart sounds. Event detection algorithm based on signal abstraction and envelopes vs. Traditional threshold-based methods was evaluated on Accurate detection and delimitation of cardiac events (sounds and murmurs). The proposed algorithm based on signal abstraction and parameter extraction from relative maximum peaks successfully detected and delimited cardiac events, including murmurs, outperforming traditional threshold-based methods.
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