Breathing rate and heart rate as confounding factors in measuring T wave alternans and morphological variability in ECG.

OBJECTIVE High morphological variability magnitude (MVM) and microvolt T-wave alternans (TWA) within an electrocardiogram (ECG) signifies increased electrical instability and risk of sudden cardiac death. However, the influence of breathing rate (BR), heart rate (HR), and signal-to-noise ratio (SNR) is unknown and may inflate measured values. APPROACH We synthesize ECGs with morphologies derived from the Physikalisch-Technische Bundesanstalt Database. We calculate MVM and TWA at varying BRs, HRs and SNRs. We compare the MVM and TWA of signal with vs. without breathing at varying HRs and SNRs. We then quantify the percentage of MVM and TWA estimates affected by BR and HR in a healthy population and assess the effect of removing these affected estimates on a method for classifying individuals with and without post-traumatic stress disorder (PTSD). MAIN RESULTS For signals with high SNR (>15 dB), MVM is significantly increased when BRs are > 9 respirations/minute (rpm) and HRs are 47 uV decreases by 23% after excluding regions with significant BR and HR effect. Adding HR and BR to a morphological variability feature increases the classification performance by 6% for individuals with and without PTSD. SIGNIFICANCE Physiological BR and HR significantly increase MVM and TWA, indicating that BR and HR should be considered separately as confounders. The code for this work has been released as part of an open-source toolbox.