Does fusing synchronously acquired wrist pulse and ECG data enhance the identification of coronary heart disease using an FS-RF model?
Synchronous acquisition of wrist pulse and ECG signals improves machine learning identification of coronary heart disease, supporting the potential use of wearable devices for early cardiovascular disease detection.
This study highlights the importance of synchronously acquiring of PPW and ECG signal, along with feature selection, in enhancing the performance of the FS-RF model for identifying CHD and its associated conditions. These findings provide a scientific basis for the application of wearable devices in clinical settings, highlighting their potential to aid in the early detection and management of cardiovascular disease.
Hong et al. (Tue,) studied this question.
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