Classification of Abnormal and Normal Heart Sounds Using the MEMS based High Performance PhonoCardioGraphy System

Anumukonda, Madhubabu; Lakkamraju, Prasada Raju; Chowdhury, Shubhajit Roy

doi:10.1109/aisp48273.2020.9073020

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“…A great deal of research has been conducted in the field of heart sound segmentation and classification in order to detect normal and abnormal components. Many signal processing algorithms are proposed to extract heart sound components for detecting pathological events (5)(6)(7). Artificial neural networks (ANNs) are widely used in cardiology (8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

FPGA-Based High-Performance Phonocardiography System for Extraction of Cardiac Sound Components Using Inverse Delayed Neuron Model

Anumukonda¹,

Lakkamraju²,

Chowdhury³

2021

Front. Med. Technol.

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The study focuses on the extraction of cardiac sound components using a multi-channel micro-electromechanical system (MEMS) microphone-based phonocardiography system. The proposed multi-channel phonocardiography system classifies the cardiac sound components using artificial neural networks (ANNs) and synaptic weights that are calculated using the inverse delayed (ID) function model of the neuron. The proposed ANN model was simulated in MATLABR and implemented in a field-programmable gate array (FPGA). The proposed system examined both abnormal and normal samples collected from 30 patients. Experimental results revealed a good sensitivity of 99.1% and an accuracy of 0.9.

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