Reduction of Artifacts in Capacitive Electrocardiogram Signals of Driving Subjects.

Tamara Škorić

doi:10.3390/e24010013

Abstract

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.

Highlights

The automotive industry has been making efforts to develop smart health systems, as part of supporting smart cars that can communicate with each other, transmit data to the cloud, and use smart e-health service systems [1,2,3,4,5]
In addition to the capacitive electrodes that are built into the car seat, in Reference [15], a steering wheel covered with a conductive fabric-based dry electrode material is proposed, while Reference [5] describes a solution that uses sensors built into the steering wheel and belt
These results are in excellent accordance for all three groups of cECG1, cECG2, and cECG3 time series recorded while lying on the bed (Figure 8a)

Summary

Introduction

The automotive industry has been making efforts to develop smart health systems, as part of supporting smart cars that can communicate with each other, transmit data to the cloud, and use smart e-health service systems [1,2,3,4,5]. The driver’s ECG, as well as the parameters derived from this signal, enable the assessment of alarming traffic situations such as driver fatigue [6], drowsiness [7], prediction of infarction development (of particular importance for the older group of drivers) [8], and EEG signal measurement contributes detection of driver fatigue [9], as well as prediction of emergency braking situations to activate the brake pedal when drivers are not able to react at the appropriate speed [10,11]. A car seat equipped with Internet of Things (IoT) sensors for measuring ECG and EEG signals with a suitable transmitter that sends physiological data to the database for further processing and prediction of the driver’s health has been developed [3,4]

Objectives

Methods

Results

Conclusion