Baseline wander and power‐line interference elimination of ECG signals using efficient signal‐piloted filtering

Abstract

A signal-piloted linear phase filtering tactic for removing baseline wander and power-line interference from the electrocardiogram (ECG) signals is suggested. The system is capable of adjusting its parameters by following the incoming signal variations. It renders the processing of lesser samples by inferior order filters. The applicability is demonstrated by using the MIT-BIH ECG database. The precision of the approach is also studied regarding the signal-to-noise ratio (SNR). Results showed that the proposed method achieves a 2.18-fold compression gain and notable computational efficiency over conventional counterpart while securing an analogous output SNR. A comparison of the designed solution is made with the contemporary empirical mode decomposition with Kalman filtering and eigenvalue decomposition based tactics. Results show that the suggested method performs better in terms of output SNR for the studied cases.

Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification

“…The functioning of traditional methods for digitizing and analysing ECG signals is time-invariant [ 7 , 8 ]. Due to the static temperament, selection of the worst-case system parameterization is necessary [ 8 ]. In the case of time varying ECG signals, this results in an inefficient and computationally-complex realization.…”

“…A low-pass finite impulse response (FIR) filter based on the Parks–McClellan algorithm is used for denoising resulting in the removal of the muscle artifacts and power line interference from the ECG signal [ 8 ]. The outcome of denoising is the efficient decomposition of subbands and improved feature extraction and classification.…”

“…The functioning of traditional methods for digitizing and analysing ECG signals is time-invariant [7,8]. Due to the static temperament, selection of the worst-case system parameterization is necessary [8].…”

“…The ECG signal may be changed by interference and physiological artifacts, which reduces the efficacy of the automatic diagnostic. Several signal processing techniques have been used to address these limitations, such as wavelet transform [ 7 ], adaptive-rate filtering [ 8 ], eigenvalue decomposition [ 9 ], extended Kalman filtering [ 10 ] and Fourier decomposition [ 11 ]. To identify attributes that can help in automated diagnosis, the denoised ECG signals are analysed.…”

“…The realization of cloud-connected wireless ECG wearables is constrained due to strict limits on the volume, weight and power consumption. Hence methods with signal compression [ 20 , 21 , 26 ] irregular sampling [ 6 , 27 ] and adaptive rate processing [ 6 , 7 , 8 ] have been conducted in this area. The low computational complexity approaches are required for efficient wearable ECG gadgets and cloud-based mobile healthcare solutions.…”