Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals. (December 2022)

• Record Type: Journal Article
• Authors: Nesaragi, Naimahmed; Sharma, Ashish; Patidar, Shivnarayan; Acharya, U. Rajendra
• Is Part Of: Medical engineering & physics. Volume 110(2022)
• Publication Date: 2022-12
• Subjects: Coronary artery disease -- Machine learning -- Scalogram -- Tensor-factorization -- Heart rate signals -- Model-based diagnosis; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2022.103811
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Automated detection of coronary artery disease using heart rate signals. This work applies time-frequency tensor analysis on heart rate variability. Latent features are extracted from core of canonical polyadic (CP) decomposition. The CP decomposition decipher higher order ternary interrelations for classification. The results obtained are on par with existing state-of-the-art performances. Abstract: Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection task. A third-order tensor representing a time-frequency relationship is constructed by fusing scalograms as vertical slices of the tensor. Each scalogram is computed from the considered time frame of a given HR signal. The derived scalogram represents the heterogeneity of data as a two-dimensional map. These two-dimensional maps are stacked one after the other horizontally along the z -axis to form a 3- way tensor for each HR signal. Each two-dimensional map is represented as a vertical slice in the xy - plane. Tensor factorization of such a fused tensor for every HR signal is performed using canonical polyadic (CP) decomposition. Only the core factor is retained later, excluding the three unitary matrices to provide the latent feature set for the detection task. The resultant latent features are then fed to machine learning classifiers for binary classification. Bayesian optimization is performed in a five-fold cross-validation strategy in search of the optimal machine learning classifier. The experimental results yielded the accuracy, sensitivity, and specificity of 96.62%, 96.53%, and 96.67%, respectively, with the bagged trees ensemble method. The proposed tensor decomposition deciphered higher-order interrelations among the considered time-frequency representations of HR signals.