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Abstract
Auscultation, the technique of listening to heart sounds, remains a primary detection tool for diagnosing heart valve disorders. Other techniques, e.g. electrocardiography, ultrasound, etc, are accurate as well as informative; but expensive. With major advancement in the speed of computers, heart sound signals can be processed with ease by memory efficient digital signal processing and pattern recognition algorithms. This paper presents a digital signal processor (DSP) implementation of such a technique by using new threshold criteria. The proposed work can detect whether a heart sound recording belongs to a person suffering from valvular heart disease or not by giving ‘diseased’ or ‘not diseased’ decisions. The algorithm is tested for nine commonly occurring pathological problems and normal heart sound. The robustness of the algorithm is also checked against synthetically injected additive white Gaussian noise (AWGN) with different SNR levels. It is found to give an accuracy of 96.67% up to SNR values of 15 dB and 93.33% up to SNR values of 5 dB.