Sensitivity and Adjustment Model of Electrocardiographic Signal Distortion Based on the Electrodes' Location and Motion Artifacts Reduction for Wearable Monitoring Applications

Sensors (Basel). 2021 Jul 15;21(14):4822. doi: 10.3390/s21144822.

Abstract

Wearable vital signs monitoring and specially the electrocardiogram have taken important role due to the information that provide about high-risk diseases, it has been evidenced by the needed to increase the health service coverage in home care as has been encouraged by World Health Organization. Some wearables devices have been developed to monitor the Electrocardiographic in which the location of the measurement electrodes is modified respect to the Einthoven model. However, mislocation of the electrodes on the torso can lead to the modification of acquired signals, diagnostic mistakes and misinterpretation of the information in the signal. This work presents a volume conductor evaluation and an Electrocardiographic signal waveform comparison when the location of electrodes is changed, to find a electrodes' location that reduces distortion of interest signals. In addition, effects of motion artifacts and electrodes' location on the signal acquisition are evaluated. A group of volunteers was recorded to obtain Electrocardiographic signals, the result was compared with a computational model of the heart behavior through the Ensemble Average Electrocardiographic, Dynamic Time Warping and Signal-to-Noise Ratio methods to quantitatively determine the signal distortion. It was found that while the Einthoven method is followed, it is possible to acquire the Electrocardiographic signal from the patient's torso or back without a significant difference, and the electrodes position can be moved 6 cm at most from the suggested location by the Einthoven triangle in Mason-Likar's method.

Keywords: bioelectromagnetism; electrocardiography; instrumentation and measurement; motion artifacts; sensitivity analysis.

MeSH terms

  • Artifacts*
  • Electrocardiography
  • Electrodes
  • Humans
  • Signal Processing, Computer-Assisted
  • Wearable Electronic Devices*