ECG denoising method based on an improved VMD algorithm

Electrocardiogram (ECG) acquisition is easily contaminated by interferences, and denoising is the most important task in ECG detection. Variational mode decomposition (VMD) algorithm is widely used in ECG denoising, which can overcome mode aliasing between intrinsic mode function components existed in the traditional empirical mode decomposition (EMD) algorithm, but the mode decomposition number K and penalty factor α in VMD must be optimized to obtain the best signal decomposition accuracy. This paper proposes an improved VMD denoising algorithm that overcomes the shortcomings of slow parameter selection and poor generalization in the traditional VMD algorithm. The algorithm presented firstly adopts EMD algorithm to remove the low-frequency baseline drift noise, and then employs the adaptive particle swarm optimization algorithm to optimize the parameter pair ( K , α) for VMD. To validate the denoising performance of the improved VMD algorithm, the No.103 record from the MIT arrhythmia database is first selected as the pure ECG signal, then both 20dB Gaussian white noises and 0.3 Hz baseline drift are added to simulate the noisy ECG signal. Second, the ECG signals of nine subjects are collected by a customized ECG detection platform based on AD8232 and ADALM1000. The ECG denoising results in simulation and actual experiments show that the improved VMD algorithm achieves the highest signal-to-noise ratio, correlation coefficient and minimum mean square error compared with the traditional EMD and VMD algorithm, which demonstrates the proposed denoising algorithm has stronger denoising ability and better retains morphological characteristics of the original ECG signals.