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. 2022 Jul 23;22(15):5493.
doi: 10.3390/s22155493.

Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal

Xiang An  1   2   3 , Yanzhong Liu  1 , Yixin Zhao  1 , Sichao Lu  1 , George K Stylios  3 , Qiang Liu  1
Affiliations

Adaptive Motion Artifact Reduction in Wearable ECG Measurements Using Impedance Pneumography Signal

Xiang An et al. Sensors (Basel). .

Abstract

Noise is a common problem in wearable electrocardiogram (ECG) monitoring systems because the presence of noise can corrupt the ECG waveform causing inaccurate signal interpretation. By comparison with electromagnetic interference and its minimization, the reduction of motion artifact is more difficult and challenging because its time-frequency characteristics are unpredictable. Based on the characteristics of motion artifacts, this work uses adaptive filtering, a specially designed ECG device, and an Impedance Pneumography (IP) data acquisition system to combat motion artifacts. The newly designed ECG-IP acquisition system maximizes signal correlation by measuring both ECG and IP signals simultaneously using the same pair of electrodes. Signal comparison investigations between ECG and IP signals under five different body motions were carried out, and the Pearson Correlation Coefficient |r| was higher than 0.6 in all cases, indicating a good correlation. To optimize the performance of adaptive motion artifact reduction, the IP signal was filtered to a 5 Hz low-pass filter and then fed into a Recursive Least Squares (RLS) adaptive filter as a reference input signal. The performance of the proposed motion artifact reduction method was evaluated subjectively and objectively, and the results proved that the method could suppress the motion artifacts and achieve minimal distortion to the denoised ECG signal.

Keywords: adaptive filtering; electrocardiogram; impedance pneumography; motion artifact reduction.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Principle of the adaptive filter in noise cancellation.
Figure 2
Figure 2
Samples of denoised ECG waveform: (a) denoised by RLS adaptive filter; (b) denoised by RLS adaptive filter with a 5 Hz low-pass filter.
Figure 3
Figure 3
The proposed adaptive motion artifact reduction method.
Figure 4
Figure 4
The analog front-end (AFE) circuit board: (a) schematic diagram; (b) circuit board.
Figure 5
Figure 5
The IP signal recorded by the ECG -IP acquisition system: (a) the IP signal contains cardiac-related artifacts; (b) the IP signal contains cardiac-related artifacts and motion artifacts.
Figure 6
Figure 6
The chest band for wearable ECG measurement.
Figure 7
Figure 7
Typical motion types to trigger the motion artifacts.
Figure 8
Figure 8
The typical ECG and IP signals recorded under different body motions: (a) motion type I, (b) motion type II, (c) motion type III, (d) motion type IV, (e) motion type V.
Figure 9
Figure 9
The correlation between low-pass filtered ECG and IP signal.
Figure 10
Figure 10
The denoised ECG signal: (a) motion type I, (b) motion type II, (c) motion type III, (d) motion type IV, (e) motion type V.
Figure 10
Figure 10
The denoised ECG signal: (a) motion type I, (b) motion type II, (c) motion type III, (d) motion type IV, (e) motion type V.
Figure 11
Figure 11
The m_MSE of the denoised ECG signal processed by the proposed method.
Figure 12
Figure 12
The m_R2 of the denoised ECG signal processed by the proposed method.
Figure 13
Figure 13
The performance of the IIR filter and the proposed method.
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