Reduction of Artifacts in Capacitive Electrocardiogram Signals of Driving Subjects

• Published in: Entropy (Basel, Switzerland) Switzerland), 2021-12, Vol.24 (1), p.13
• Main Author: Škorić, Tamara
• Format: Article
• Language: English
• Subjects: Algorithms; binarized approximate entropy; cECG filter; Condition monitoring; DDNN; Driver behavior; Driver fatigue; Electrocardiography; Electrodes; Electroencephalography; Health services; Internet of Things; KNN; movement artefacts; Noise; Signal processing; Smart cars; Spectral bands; Time series; Wavelet transforms
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24010013

The development of smart cars with e-health services allows monitoring of the health condition of the driver. Driver comfort is preserved by the use of capacitive electrodes, but the recorded signal is characterized by large artifacts. This paper proposes a method for reducing artifacts from the ECG signal recorded by capacitive electrodes (cECG) in moving subjects. Two dominant artifact types are coarse and slow-changing artifacts. Slow-changing artifacts removal by classical filtering is not feasible as the spectral bands of artifacts and cECG overlap, mostly in the band from 0.5 to 15 Hz. We developed a method for artifact removal, based on estimating the fluctuation around linear trend, for both artifact types, including a condition for determining the presence of coarse artifacts. The method was validated on cECG recorded while driving, with the artifacts predominantly due to the movements, as well as on cECG recorded while lying, where the movements were performed according to a predefined protocol. The proposed method eliminates 96% to 100% of the coarse artifacts, while the slow-changing artifacts are completely reduced for the recorded cECG signals larger than 0.3 V. The obtained results are in accordance with the opinion of medical experts. The method is intended for reliable extraction of cardiovascular parameters to monitor driver fatigue status.