Signal Quality Analysis of Single-Arm Electrocardiography

Area of Science:

• Biomedical Engineering
• Cardiovascular Monitoring
• Digital Health

Background:

• Increased mental stress post-COVID-19 pandemic necessitates accessible monitoring solutions.
• Mental health disorders are linked to physical diseases and mental workload.
• Wearable devices use heart rate variability (HRV), measured by R-to-R wave interval (RRI) from electrocardiogram (ECG), for daily mental health assessment.

Purpose of the Study:

• To develop and evaluate a simplified, single-arm ECG measurement method for HRV analysis.
• To compare the performance of the single-arm ECG against the gold standard lead I ECG.

Main Methods:

• Developed a novel single-arm ECG measurement technique.
• Conducted static and dynamic experiments with 30 subjects to assess morphological features (RRI, QRS duration, R wave amplitude) and signal-to-noise ratio (SNR).
• Utilized lead I ECG as the gold standard for comparison.

Main Results:

• The single-arm ECG demonstrated high cross-correlation (R = 0.9942) with lead I ECG for RRI.
• QRS complex duration showed the lowest cross-correlation (R = 0.2201).
• Signal-to-noise ratio (SNR) ranged from 26.1 ± 5.9 dB (resting) to 12.5 ± 5.1 dB (dynamic arm movements).

Conclusions:

• The single-arm ECG method provides a simpler, more accessible approach to ECG measurement.
• It is effective for accurate RRI measurement, supporting daily HRV monitoring for mental stress.
• This technique is suitable for detecting irregular RRI, aiding in mental health assessments.