Pavel Leinveber

Aims: Electrical ventricular dyssynchrony is a hallmark of intraventricular conduction disease in heart failure with reduced ejection fraction. Although cardiac resynchronization therapy (CRT) is effective, a proportion of patients with electrical dyssynchrony evidenced by an electrocardiogram (ECG) remain non-responders. Besides the QRS duration, visualization and quantification of the depolarization sequence could contribute to improving CRT response. The study aimed to evaluate the CineECG-derived mean temporo-spatial isochrone trajectory of depolarization in CRT patients. Methods and results: Among the adult cohort of 121 patients who underwent CRT implantation, there were 93 (33% female) responders and 28 (7% female) non-responders. ECGs recorded before and immediately after CRT implantation were collected and analysed using the CineECG modality with a generic heart/torso model. The CineECG-derived path directions were computed for the QRS on anteroposterior, interventricular, and apicobasal axes within the cardiac anatomy. Spatial depolarization characteristics were tested for the association with left ventricle reverse-remodelling at 6-month follow-up. The pre-implantation depolarization sequence was predominantly from the right to the left ventricle, and after the CRT implant, it has been transformed into an apicobasal direction. In multivariate logistic regression, the pre-implant QRS duration, apicobasal depolarization gradient, and CRT-induced QRS shortening independently predicted CRT response. The Akaike information criterion indicated that combining all three yielded the strongest predictive model. Conclusion: CineECG-derived depolarization gradients, which reflect ventricular activation sequence, changed significantly after CRT, and the pre-implantation apicobasal gradient independently predicted echocardiographic outcome, with a less pronounced gradient associated with better haemodynamic response.

Background: Obstructive sleep apnea (OSA) imposes an afterload burden on the left ventricle and increases the pressure gradient across the aortic wall. Thus, OSA may increase the risk for aortic dissection (AD).Methods: This study enrolled 40 subjects with acute AD from four institutions; 37 completed the modified Berlin Questionnaire and 31 underwent attended overnight polysomnography. Aortic diameter was measured on a computed tomography scan at seven locations from the sinotubular junction to the diaphragm.Results: Twenty‐seven subjects had type A dissection; 13 had type B. In those who had polysomnography apnea–hypopnea index (AHI) ranged from 0.7 to 89. Prevalence of OSA (AHI ≥ 5) was 61%. Nocturnal presentation (10 p.m.–7 a.m.) did not differ by presence/absence of OSA. The modified Berlin Questionnaire was not predictive of the presence of OSA. Among type A subjects with polysomnography (n = 23), aortic diameters at all locations were greater in the OSA group though differences were not statistically significant. Summating aortic diameters at the seven locations also yielded a numerically larger mean value in the OSA group versus the non‐OSA group.Conclusions: In this sample of patients with acute dissection, OSA was prevalent but was not associated with a nocturnal presentation. The presence of underlying OSA may be associated with larger aortic diameters at the time of dissection compared to patients without OSA. Though differences did not meet statistical significance the current series is limited by small numbers.

Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.