Cepstral Analysis for Scoring the Quality of Electrocardiograms for Heart Rate Variability - Castiglioni, Paolo; Parati, Gianfranco; Faini, Andrea (Frontiers in Physiology, 2022)
Mobile-health solutions based on heart rate variability often require electrocardiogram (ECG) recordings by inexperienced operators or real-time automatic analyses of long-term recordings by wearable devices in free-moving individuals. In this context, it is useful to associate a quality index with the ECG, scoring the adequacy of the recording for heart rate variability to identify noise or arrhythmias. Therefore, this work aims to propose and validate a computational method for assessing the adequacy of single-lead ECGs for heart rate variability analysis that may run in real time on wearable systems with low computational power. The method quantifies the ECG pseudo-periodic structure employing cepstral analysis. The cepstrum (spectrum of log-spectrum) is estimated on a running ECG window of 10 s before and after “liftering” (filtering in the cepstral domain) to remove slower noise components. The ECG periodicity generates a dominant peak in the liftered cepstrum at the “quefrency” of the mean cardiac interval. The Cepstral Quality Index (CQI) is the ratio between the cepstral-peak power and the total power of the unliftered cepstrum. Noises and arrhythmias reduce the relative power of the cepstral peak decreasing CQI. We analyzed a public dataset of 6072 single-lead ECGs manually classified in normal rhythm or inadequate for heart rate variability analysis because of noise or atrial fibrillation, and the CQI = 47% cut-off identified the inadequate recordings with 79% sensitivity and 85% specificity. We showed that the performance is independent of the lead considering a public dataset of 1,000 12-lead recordings with quality classified as “acceptable” or “unacceptable” by visual inspection. Thus, the cepstrum describes the ECG periodic structure effectively and concisely and CQI appears to be a robust score of the adequacy of ECG recording for heart rate variability analysis, evaluable in real-time on wearable devices.