EMD-Based Electrocardiogram Delineation for a Wearable Low-Power ECG Monitoring Device

A novel algorithm for subtracting the artifacts from an electrocardiogram (ECG) signal and detecting the QRS complex based on empirical mode decomposition has been designed, tested, and evaluated. This method can remove both the noise of the power line interference and baseline wander from the ECG signal with minimum distortion, and ${R}$ peaks can be exactly detected. The method is tested and evaluated using the records from the MIT-BIH arrhythmia database and the ECG signal data acquired from a wearable low-power ECG monitoring device. In the experiments, the correlation coefficient between the clean signal and denoised signal can be up to 0.997, and it is greater than 0.970 even in severely contaminated situations. The detection algorithm for the wearable monitoring device shows that the QRS detection rate is over 99.8% and that the sensitivity is over 99.9%. When compared with other detection methods, this proposed algorithm holds the best performance in severely noise-contaminated situations. The experiment’s results demonstrate that the algorithm can effectively delineate the ECG signal under different sampling rates as expected.