Dataset Information

Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.

ABSTRACT: Early prediction of the occurrence of ventricular tachyarrhythmia (VTA) has a potential to save patients' lives. VTA includes ventricular tachycardia (VT) and ventricular fibrillation (VF). Several studies have achieved promising performances in predicting VT and VF using traditional heart rate variability (HRV) features. However, as VTA is a life-threatening heart condition, its prediction performance requires further improvement. To improve the performance of predicting VF, we used the QRS complex shape features, and traditional HRV features were also used for comparison. We extracted features from 120-s-long HRV and electrocardiogram (ECG) signals (QRS complex signed area and R-peak amplitude) to predict the VF onset 30 s before its occurrence. Two artificial neural network (ANN) classifiers were trained and tested with two feature sets derived from HRV and the QRS complex shape based on a 10-fold cross-validation. The prediction accuracy estimated using 11 HRV features was 72%, while that estimated using four QRS complex shape features yielded a high prediction accuracy of 98.6%. The QRS complex shape could play a significant role in performance improvement of predicting the occurrence of VF. Thus, the results of our study can be considered by the researchers who are developing an application for an implantable cardiac defibrillator (ICD) when to begin ventricular defibrillation.

SUBMITTER: Taye GT

PROVIDER: S-EPMC6764170 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.

Taye Getu Tadele GT Shim Eun Bo EB Hwang Han-Jeong HJ Lim Ki Moo KM

Frontiers in physiology 20190920

Early prediction of the occurrence of ventricular tachyarrhythmia (VTA) has a potential to save patients' lives. VTA includes ventricular tachycardia (VT) and ventricular fibrillation (VF). Several studies have achieved promising performances in predicting VT and VF using traditional heart rate variability (HRV) features. However, as VTA is a life-threatening heart condition, its prediction performance requires further improvement. To improve the performance of predicting VF, we used the QRS com ...[more]

PMID: 31616311

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Project description:BackgroundThe elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. There is therefore the need to identify those patients with COVID-19 who are at highest risk of developing incident AF. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main effect modeling and secondly, a machine-learning (ML) approach, accounting for the complex dynamic relationships among comorbidity variables.MethodsWe studied a prospective elderly US cohort of 280,592 patients from medical databases in an 8-month investigation of with/without newly incident COVID19 cases. Incident AF outcomes were examined in relationship to diverse multi-morbid conditions, COVID-19 status and demographic variables, with ML accounting for the dynamic nature of changing multimorbidity risk factors.ResultsMulti-morbidity contributed to the onset of confirmed COVID-19 cases with cognitive impairment (OR 1.69; 95%CI 1.52-1.88), anemia (OR 1.41; 95%CI 1.32-1.50), diabetes mellitus (OR 1.35; 95%CI 1.27-1.44) and vascular disease (OR 1.30; 95%CI 1.21-1.39) having the highest associations. A main effect model (C-index value 0.718) showed that COVID-19 had the highest association with incident AF cases (OR 3.12; 95%CI 2.61-3.710, followed by congestive heart failure (1.72; 95%CI 1.50-1.96), then coronary artery disease (OR 1.43; 95%CI 1.27-1.60) and valvular disease (1.42; 95%CI 1.26-1.60). The ML algorithm demonstrated improved discriminatory validity incrementally over the statistical main effect model (training: C-index 0.729, 95%CI 0.718-0.740; validation: C-index 0.704, 95%CI 0.687-0.72). Calibration of the ML based formulation was satisfactory and better than the main-effect model. Decision curve analysis demonstrated that the clinical utility for the ML based formulation was better than the 'treat all' strategy and the main effect model.ConclusionCOVID-19 status has major implications for incident AF in a cohort with diverse cardiovascular/non-cardiovascular multi-morbidities. Our ML approach accounting for dynamic multimorbidity changes had good prediction for new onset AF amongst incident COVID19 cases.

Dataset Information

Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.

Publications

Machine Learning Approach to Predict Ventricular Fibrillation Based on QRS Complex Shape.

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