Key result
The banded kernel ensemble method accurately converted photoplethysmography to electrocardiogram signals, enabling heart disease detection with an average correctness rate of 81%.
Why the study?
Can a banded kernel ensemble learning method accurately convert PPG signals to ECG signals for heart disease detection?
Can a banded kernel ensemble learning method accurately convert PPG signals to ECG signals for heart disease detection?
A novel banded kernel ensemble learning method can accurately reconstruct ECG signals from PPG data, potentially enabling low-cost, continuous monitoring for heart disease detection.
Electrocardiography (ECG) is generally deemed the golden standard for diagnosing cardiovascular diseases and photoplethysmography (PPG) is unobtrusive, low-cost, and convenient for continuous monitoring. However, PPG contains insufficient information to diagnose diseases. In this study, we propose a novel method to accurately convert PPG to ECG. The banded kernel ensemble method converts a low-quality source (PPG) to a high-quality destination (ECG). Unlike neural network solutions, our algorithm requires no computation burden in the conversion task after a trained model is obtained. The proposed algorithm is then tested on a publicly available MIMIC III database. Our prediction shows excellent accuracy in the validation dataset. It offers the testing performance of under 0.314 and above 0.55 in rrmse (relative root mean squared error) and KGE (Kling–Gupta efficiency), respectively, under the scenarios of three prevalent heart diseases. The reconstructed ECG can be further used to perform heart disease detection and we obtained an average correctness rate of 81%. Our method can help a large population of high-risk, believed-healthy persons to walk into doctors’ offices before the situation becomes irreversible.
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Ho et al. (2022) studied Heart diseases (Acute Myocardial Infarction, Atrial Fibrillation, Heart Failure) (n=547). Banded Kernel Ensemble Learning Method vs. Deep convolutional neural network (VGG19) and other baseline methods was evaluated on Average correctness rate for heart disease detection using reconstructed ECG. The banded kernel ensemble method accurately converted photoplethysmography to electrocardiogram signals, enabling heart disease detection with an average correctness rate of 81%.
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