January 24, 2019IEEE Sensors Journal

Joint Time-Frequency Domain-Based CAD Disease Sensing System Using ECG Signals

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Why the study?

Does an IEVDHM-HT-based time-frequency feature extraction method improve the detection of CAD from ECG signals?

Population

ECG beats of CAD and normal classes

Comparison

Features extracted from complete TFR vs local regions of the same TFR across random tree and J48 classifiers

Design

Algorithm development and diagnostic classification study

Authors

RSRishi Raj SharmaDefence Institute of Advanced Technology MKMohit KumarAndhra University RPRam Bilas PachoriIndian Institute of Management Indore

Discussion

Member takes

Implication

Proposed ECG features should not yet guide CAD diagnosis; leaves open prospective validation of diagnostic performance.

Structured PICO

Does an IEVDHM-HT-based time-frequency feature extraction method improve the detection of CAD from ECG signals?

Population

ECG beats from subjects with coronary artery disease (CAD) and normal subjects

Intervention

Improved eigenvalue decomposition of Hankel matrix and Hilbert transform (IEVDHM-HT)-based method with time-frequency matrix-based modified features and random tree/J48 classifiers

Outcome

Accuracy in separating CAD and normal ECG beatssurrogate

A novel time-frequency domain-based feature extraction method using ECG signals achieved 99.93% accuracy in detecting coronary artery disease.

Cite This Study

Sharma et al. (2019) studied this question.

synapsesocial.com/papers/6a8fbc44c18b457d1fd2c1df https://doi.org/10.1109/jsen.2019.2894706

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Bookmark

Joint Time-Frequency Domain-Based CAD Disease Sensing System Using ECG Signals

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Joint Time-Frequency Domain-Based CAD Disease Sensing System Using ECG Signals

Discussion

Member takes

Implication

Structured PICO

Cite This Study

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Also Consider