Motion Artifact Removal using Cascade Adaptive Filtering for Ambulatory ECG Monitoring System Hyejung Kim, Sunyoung Kim, Nick Van Helleputte, Torfinn Berset, Julien Penders, Chris Van Hoof, and Refet Firat Yazicioglu
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ne of the major problems in ambulatory ECG monitoring system is the presence of artifacts, so the artifact thus needs to be suppressed or cancelled for achieving reliable and high integrity recording quality to provide higher level of physical activity to the subjects. Among the many artifacts on ECG signal, we focus on removing two dominant artifacts. The first one is baseline wandering containing very low frequency component below 0.5 Hz. The other one is high frequency noise caused by running or twisting the skin, which is overlapped with the ECG signal bandwidth. In this work, in order to relax this requirement, a motion artifact removal method with a two-stage cascade LMS filter is proposed as shown in Figure 1. The first LMS stage consisting of analog feedback prevents the signal saturation. The estimated motion artifact is computed by digital signal processor, fed back to the analog ASIC [1] through a DAC, and then subtracted from the sampled ECG in the analog domain prior to final amplification, so that it increases the equivalent input dynamic range. An adaptive step-size LMS algorithm is employed for the second LMS stage in the digital domain. Adaptive step-size algorithm (Figure 2) can achieve the fast convergence to track large sudden motion artifact quickly, while preventing the distortion of the ECG component. The skin-electrode impedance signal is used [2], which provides the high correlation with the motion artifact. The impedance signal can be recorded simultaneously together with ECG signal by sharing the electrode, so that the monitoring system can be implemented in a low power and small form-factor. The filtering performance is evaluated by using imec database with the heart beat detection performance, measured by sensitivity (Se) and positive predictivity (+p), and the performance is increased 9.8% and 6.48%, respectively, at the worst case with -25dB SNR as shown in Figure 3. The proposed method is implemented on the ambulatory ECG monitoring module. In the measurement results of Figure 4, we found that the 1st stage filtered ECG output, we can see that the lost QRS complex is presented around at t=2s (gray region) after suppressing the signal saturation after the 1st LMS filtering. The final result from the 2nd stage LMS filter with adaptive step-size algorithm achieves significant motion artifact removal. The real-time measurement result clearly shows the significant performance improvement of the proposed solution. 4000
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REFERENCES [1] N. Van Helleputte, et al., “A 160μA Biopotential Acquisition ASIC with Fully Integrated IA and Motion-Artifact Suppression”, IEEE ISSCC 2012 [2] S. Kim, et al., “A 2.4 μA Continuous-time Electrode-Skin Impedance Measurement Circuit for Motion Artifact Monitoring in ECG Acquisition Systems,” IEEE Symp. VLSI, 2010 Hyejung Kim, Sunyoung Kim, Nick Van Helleputte, Chris Van Hoof, and Refet Firat Yazicioglu are with imec, Kapeldreef 75, 3000 Leuven, Belgium. Torfinn Berset, Julien Penders, and Chris Van Hoof are with imec-nl/Holst center, Eindhoven, The Netherlands. (corresponding author: Hyejung Kim, phone: +32-16-28-7877; e-mail:
[email protected])
