Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals
Article
Nesaragi, Naimahmed, Sharma, Ashish, Patidar, Shivnarayan and Acharya, U. Rajendra. 2022. "Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals." Medical Engineering and Physics. 110. https://doi.org/10.1016/j.medengphy.2022.103811
| Article Title | Automated diagnosis of coronary artery disease using scalogram-based tensor decomposition with heart rate signals |
|---|---|
| ERA Journal ID | 5057 |
| Article Category | Article |
| Authors | Nesaragi, Naimahmed, Sharma, Ashish, Patidar, Shivnarayan and Acharya, U. Rajendra |
| Journal Title | Medical Engineering and Physics |
| Journal Citation | 110 |
| Article Number | 103811 |
| Number of Pages | 9 |
| Year | 2022 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 1350-4533 |
| 1873-4030 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.medengphy.2022.103811 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1350453322000601 |
| 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. |
| Keywords | Coronary artery disease; Machine learning ; Scalogram; Tensor-factorization ; Heart rate signals ; Model-based diagnosis |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | National Institute of Technology Goa, India |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Singapore University of Social Sciences (SUSS), Singapore |
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