An improved method to detect coronary artery disease using phonocardiogram signals in noisy environment. (July 2020)

• Record Type: Journal Article
• Authors: Pathak, Akanksha; Samanta, Pranab; Mandana, Kayapanda; Saha, Goutam
• Is Part Of: Applied acoustics. Volume 164(2020)
• Publication Date: 2020-07
• Subjects: CAD Coronary artery disease -- ICPSD Imaginary part of cross power spectral density -- MA Midaxillary -- MCPSD Magnitude of cross power spectral density -- MIT Mitral -- PCG Phonocardiogram -- PUL Pulmonary -- TRI Tricuspid; Coronary artery disease (CAD) -- Cross power spectral density (CPSD) -- Environmental noise -- Multichannel -- Phonocardiogram (PCG) -- Subband features; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2020.107242
• Languages: English
• ISSNs: 0003-682X

Abstract

Abstract: Identification of coronary artery disease (CAD) from phonocardiogram (PCG) signal is a low signal to noise ratio (SNR) problem. This study proposes a PCG based CAD detection system robust against the environmental noise that does not require additional reference signals for noise acquisition and PCG segmentation. Here, the experiments are conducted on 40 CAD and 40 normal subjects. PCG signals are recorded from a multichannel data acquisition system from four auscultation sites on the left anterior chest. While heart sounds are propagated to different auscultation sites with a certain delay, the ambient noise appearing at microphone array are not mutually time-lagged. Thus, we propose to use the imaginary part of cross power spectral density (ICPSD) to capture the spectrum of heart sounds as it is unresponsive to zero time-lagged signals. Subband based spectral features obtained from ICPSD are classified in a machine learning framework. The performance of the system is studied in the presence of babble, vehicle and white noise in which useful information were extracted from both systolic and diastolic phases of cardiac cycle. The proposed method achieves accuracy, sensitivity and specificity of 74.98%, 76.50% and 73.46%, respectively in absence of ambient noise for k-fold ( k = 5 ) cross-validation. The accuracy for 0 dB SNR in presence of white, babble and vehicle noise were 71.13 %, 66.47 % and 69.60 %, respectively. The proposed method was found to be superior in CAD classification when compared with existing noise removal based approach. The present work shows the potential of developing a PCG-based multichannel CAD detection system as an affordable point of care device for real-life use, where a certain amount of ambient noise is expected.