Premature Ventricular Contraction Coupling Interval Variability Destabilizes Cardiac Neuronal and Electrophysiological Control

Abstract Background Premature ventricular contraction (PVC) is one of common arrhythmias and only some patients complain of PVC-related symptoms, however the mechanisms which cause the symptoms remain unclear in patients with PVCs. Purpose We investigated whether the left ventricular (LV) stiffness assessed by diastolic wall strain (DWS) relate symptoms or not in patients with PVC. Methods We studied 109 patients (48 males, age 60±19) with frequent monomorphic PVCs who underwent 12-leads electrocardiogram (ECG), signal-averaged electrocardiogram (SAECG), 24h-Holter ECG recording, and transthoracic echocardiography (TTE). Patients with structural heart disease or other arrhythmias such as atrial fibrillation were excluded. Clinical factors, blood samples for atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP), and filtered QRS duration (f-QRS) and root mean square voltage of the terminal 40ms of the QRS complex (RMS40) obtained by SAECG were evaluated. We assessed PVC-SV (stroke volume during PVC), PVC-CI (CI between the previous sinus beat and VPC), and left ventricular (LV) stiffness assessed by diastolic wall strain (DWS). DWS was calculated from the M-mode echocardiographic measurement of the LV posterior wall thickness at end-systole (PWs) and end-diastole (PWd) during sinus rhythm, and DWS was defined as (PWs − PWd)/PWs. Results 31patients (28%) had PVC-related symptoms (18 palpitation and 13 pulse deficit). Patients with PVC-related symptoms showed shorter PVC coupling interval index (52±10 vs. 58±11%, p=0.0140), reduced PVC-SV (21±12 vs. 29±17ml, p=0.0103) and decreased DWS (0.38±0.06 vs. 0.42±0.06, p=0.0011). Meanwhile, the level of BNP and ANP, f-QRS, RMS40, QRS morphology of PVC and the total number of PVC per day were not associated with PVC-related symptoms. On multivariate analysis, decreased DWS was only independently associated with PVC-related symptoms (p=0.0357, OR 2.3629 for each 0.1 decrease in DWS 95% CI 1.0583–5.5815). Conclusion The reduced diastolic wall strain relates with PVC-related symptoms. The increased left ventricular stiffness might cause symptoms in patients with PVC. Funding Acknowledgement Type of funding source: None

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CARDIAC AFFERENT SIGNALING PARTIALLY UNDERLIES PREMATURE VENTRICULAR CONTRACTION-INDUCED CARDIOMYOPATHY

Journal of the American College of Cardiology ◽

10.1016/s0735-1097(21)01938-0 ◽

2021 ◽

Vol 77 (18) ◽

pp. 579

Author(s):

Yuichi Hori ◽

Taro Temma ◽

Christian Wooten ◽

Christopher O Sobowale ◽

Christopher Chan ◽

...

Keyword(s):

Premature Ventricular Contraction ◽

Ventricular Contraction ◽

Afferent Signaling

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Robust deep learning pipeline for PVC beats localization

Technology and Health Care ◽

10.3233/thc-218045 ◽

2021 ◽

Vol 29 ◽

pp. 475-486

Author(s):

Bohdan Petryshak ◽

Illia Kachko ◽

Mykola Maksymenko ◽

Oles Dobosevych

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Premature Ventricular Contraction ◽

Ecg Signal ◽

Ventricular Contraction ◽

Localization Task ◽

Robust Performance ◽

Extensive Evaluation ◽

Decoder Architecture ◽

Public Datasets

BACKGROUND: Premature ventricular contraction (PVC) is among the most frequently occurring types of arrhythmias. Existing approaches for automated PVC identification suffer from a range of disadvantages related to hand-crafted features and benchmarking on datasets with a tiny sample of PVC beats. OBJECTIVE: The main objective is to address the drawbacks described above in the proposed framework, which takes a raw ECG signal as an input and localizes R peaks of the PVC beats. METHODS: Our method consists of two neural networks. First, an encoder-decoder architecture trained on PVC-rich dataset localizes the R peak of both Normal and anomalous heartbeats. Provided R peaks positions, our CardioIncNet model does the delineation of healthy versus PVC beats. RESULTS: We have performed an extensive evaluation of our pipeline with both single- and cross-dataset paradigms on three public datasets. Our approach results in over 0.99 and 0.979 F1-measure on both single- and cross-dataset paradigms for R peaks localization task and above 0.96 and 0.85 F1 score for the PVC beats classification task. CONCLUSIONS: We have shown a method that provides robust performance beyond the beats of Normal nature and clearly outperforms classical algorithms both in the case of a single and cross-dataset evaluation. We provide a Github1 repository for the reproduction of the results.

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