Non-Direct Contact ECG Signal Classification Using a Hybrid Deep Learning Framework with Validation in Bedside Heart Rate Variability Analysis.

In recent years, the demand for smart healthcare solutions have heightened the need for accuracy, reliability, and comfort in bedside ECG recording and analysis. This study presents a bedside non-direct contact ECG recording system based on capacitive coupling electrocardiography (cECG) and verifies its performance in accurately capturing Heart Rate Variability (HRV) during the night. Firstly, cECG collects ECG data through clothing, avoiding skin irritation from conventional wet electrodes. Secondly, leveraging the unique characteristics of cECG signals, a deep learning framework assesses the quality of cECG, filtering noise and identifying off-bed information, enhancing HRV analysis precision. Subsequently, the system was employed to recording sleep data from 6 subjects overnight, with our proposed algorithm utilized for signal quality assessment (SQA) and HRV analysis. Finally, HRV features were compared with synchronously collected wet electrode ECG signals, encompassing time domain features, frequency domain features, and nonlinear features, totaling 13 HRV features. Experimental findings demonstrate that for the SQA task, the model achieved a classification accuracy of 94.7%, with a Recall of 0.941, Precision of 0.940, F1 score of 0.941, and Cohen's Kappa of 0.927. The accuracy of on/off-bed monitoring reached 99.79%. Additionally, HRV features showed a strong correlation with the reference ECG. In the time-domain metrics, the largest mean absolute percentage error (MAPE) is for PNN50, with a value of 8.148%. In the frequency-domain features, the largest MAPE is for HF, with a value of 13.253%. For nonlinear features, the largest MAPE is for SD1, with a value of 5.182%. Generally, the system exhibited a reliable solution for cECG recording, on/off-bed status detection, and bedside HRV analysis.