MULTICHANNEL PHONOCARDIOGRAPHY SYSTEM FOR CARDIAC DIAGNOSIS

Abstract

The current research aims to develop a multichannel phonocardiography system to improve the efficiency in detecting low-frequency cardiac auscultation and diagnosis of heart failures. In particular, the research work has been carried out with respect to two aspects. Firstly, a hardware prototype of a MEMS-based multichannel phonocardiography system has been developed, along with the placement of microphones and methods for heart sound localization to enhance signal quality. Secondly, proposed the signal processing algorithm for heart sound segmentation to identify the low-frequency components and neural network-based data analytic technique for feature extraction and implemented the proposed algorithms on SoC-FPGA to differentiate the normal and abnormal heart sound components. Cardiac auscultation is one of the non-invasive methods to diagnose heart abnormalities. Along with four significant heart sounds (S1, S2, S3, and S4), other murmur sounds are generated due to pathological conditions. These anomalies help in proper diagnosis and prevent the possibility of heart failure. MEMS are becoming the most popular due to their size and free ambient noises and are vital in developing noninvasive diagnostic instruments. In addition, The development of cardiac auscultation devices advantages significantly from using MEMS microphones. This research has developed the MEMS-based phonocardiography instrument to capture and analyze the heart sound S3, S4, and murmur components at cardiac auscultation points. The main contribution of this thesis is developing the mathematical model for source localization algorithms to derive microphone positions with a high signal-to-noise ratio (SNR). The proposed cross-correlation method improves the system's sensitivity in detecting low-frequency cardiac sound components (S1, S2, S3, S4 and stenosis, regurgitation murmurs). The segmentation approach combining wavelet and Shannon energy was implemented on Field Programmable Gate Arrays (FPGAs) to categorize the heart sound components. The proposed segmentation algorithm revealed an excellent sensitivity of 99.17 percent and a detection error rate of 1.5 percent. Accurate measurements of the cardiac components were obtained using a combination of traditional statistical approaches and neural network based algorithms from multiple signals received simultaneously from several PCG systems. The proposed multichannel phonocardiography system analysis the cardiac sound components using artificial neural networks (ANN). The Inverse delayed (ID) function model of a neuron is used to compute synaptic weights after being simulated in MATLAB. The proposed ANN model was implemented in a Field Programmable Gate Array (FPGA). An SoC FPGA (ZYNQ SoC) performs most of the required data processing, eliminating the need for robust and expensive computer systems. Using regression analysis, a statistical model was developed to identify abnormal heart sounds from the captured signals. The receiver operating characteristic (ROC) curve has been used to evaluate the performances of the proposed model. The ANN system examined both abnormal and normal samples, and experimental results revealed a good sensitivity of 99.1% and an accuracy of 0.9. The research concluded that signal acquisition from multiple sensors and source localization methods produces a high-quality signal suitable for analyzing low-frequency cardiac sounds (S1, S2, S3, S4, stenosis, regurgitation murmurs). In addition, the proposed ANN classification method, based on the inverse delayed (ID) function model of neuron, can resolve combinatorial optimization problems. The negative resistance of the ID model can destabilize a neural network's stable equilibrium points, reducing the possibility of unknown values in suboptimal synaptic weight solutions obtained using an ANN based on a traditional neuron model. Furthermore, the repeatability and reproducibility measurements are used for the performance analysis.