A Novel Method for Automatic Identification of Motion Artifact Beats in ECG Recordings

Abstract

This paper presents a novel method for automatic identification of motion artifact beats in ECG recordings. The proposed method is based on the ECG complexes clustering, fuzzy logic and multi-parameters decision. Firstly, eight simulated datasets with different signal-to-noise ratio (SNR) were built for identification experiments. Results show that the identification sensitivity of our method is sensitive to SNR levels and acts like a low-pass filter that matches the cardiologists' recognition, while the Norm FP rate and PVB FP rate keep significantly low regardless of SNR. Furthermore, a simulated dataset including random durations of motion activities superimposed segments and two clinical datasets acquired from two different commercial recorders were adopted for the evaluation of accuracy and robustness. The overall identification results on these datasets were: sensitivity >94.69%, Norm FP rate <0.60% and PVB FP rate <2.65%. All the results were obtained without any manual threshold adjustment according to the priori information, thus dissolving the drawbacks of previous published methods. Additionally, the total cost time of our method applied to 24 h recordings is less than 1 s, which is extremely suitable in the situation of magnanimity data in long-term ECG recordings.

Two-stage motion artefact reduction algorithm for electrocardiogram using weighted adaptive noise cancelling and recursive Hampel filter

“…Traditional digital signal processing filters such as band-pass filter, and Discrete Wavelet Transform (DWT) [ 12 – 14 ] are useless for removing motion artefacts from a contaminated ECG signal, due to the overlapping of the motion artefact with the desired ECG signal [ 25 , 26 ]. According to [ 27 ] the bandwidth of a motion artefact is less than 5 Hz and that it can be filtered through oversampling, such as 2 KHz, for better time resolution.…”

A Baseline Wander Tracking System for Artifact Rejection in Long-Term Electrocardiography

“…The ideal reconstruction as proposed in (7) would introduce signal errors if the offset reversal takes place at the same sample index as the offset shift is detected. To overcome this issue, we rather reconstruct the offset shift at the sample index , where depicts the interpolation index otherwise (16) and interpolate if . We use the backward difference to calculate for , where is the total number of samples of .…”

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