Key result
ECG signal analysis using variational mode decomposition and machine learning achieved a highest classification accuracy of 98.30% in differentiating dilated from ischemic cardiomyopathy.
Why the study?
ICM and DCM have similar clinical manifestations but differing treatments and prognoses, and the use of ECG signals based on VMD as a noninvasive alternative to coronary angiography remains underexplored.
Does ECG signal processing using variational mode decomposition (VMD) and machine learning improve the classification accuracy between dilated cardiomyopathy and ischemic cardiomyopathy?
Does ECG signal processing using variational mode decomposition (VMD) and machine learning improve the classification accuracy between dilated cardiomyopathy and ischemic cardiomyopathy?
Advanced ECG signal processing using variational mode decomposition and machine learning can highly accurately differentiate between dilated and ischemic cardiomyopathy, potentially offering a non-invasive alternative to coronary angiography.
May support noninvasive ICM-DCM differentiation via VMD-ECG; hypothesis-generating and requires prospective validation before clinical use.
The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified.
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Han et al. (2024) studied Dilated cardiomyopathy and Ischemic cardiomyopathy (n=87). ECG signal analysis using variational mode decomposition (VMD) vs. Empirical mode decomposition (EMD) and previous approaches was evaluated on Classification accuracy between DCM and ICM. ECG signal analysis using variational mode decomposition and machine learning achieved a highest classification accuracy of 98.30% in differentiating dilated from ischemic cardiomyopathy.
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