Variable Self-Optimizing Cochlear Model for Heart Murmur Detection/Classification

Abstract

Accurate detection and classification of heart murmurs by auscultation is suboptimal and not always definitive. The murmur information perceived by the physician brain is the combined effect of both patient's (human) heart and the physician's ear. The information containing the murmur characterization which is retrieved by the human brain resides in the electrical signal coming out of the cochlea. For the very reasons described here, cochlea-like processing has been successfully applied to multiple speech recognition related technologies. This had not, before our prior work, been applied to human heart murmur analysis. Our prior research consisted of three steps: (1) capturing heart sounds, (2) processing the sounds using a cochlea-like filter, and then, (3) classifying each sound as being normal or a murmur using an artificial neural network (ANN). Previously in our research, we used a static cochlea-like filter model in step 2 as described above, which resulted a significant improvement in terms of accuracy of heart murmur classification. Our cochlear filter analysis helped identify information-rich frequency segments in human heart sound. We want to advance the cochlear filter model from a static to a variable frequency selective model with feedback from ANN for better optimization of the heart murmur classification. The heart sounds will be processed in ways more closely replicating the human cochlea than the static cochlear filter. A variable self optimizing cochlear filter will better reproduce the mechanism of the human cochlea in that it will contain a feedback system from ANN to cochlear processing to automatically select the most useful frequencies based upon a threshold mechanism filtering out those frequencies which do not contain significantly useful information about classification of heart murmur. The output of the sounds in the frequency range remaining (variable self-optimizing cochlear filtered sounds) may then be used by the neural network to make a final decision about murmur classification. Our hypothesis is that a variable self-optimizing cochlear filter will significantly improve the accuracy in classification of heart sounds as normal or murmur when compared to a static cochlear filter. Using this approach, we plan to develop an AI based system which will classify heart sounds with a success rate significantly better than the static cochlear filter previously developed.