The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals. (June 2021)

• Record Type: Journal Article
• Authors: Chen, Zheng; Ono, Naoaki; Chen, Wei; Tamura, Toshiyo; Altaf-Ul-Amin, MD; Kanaya, Shigehiko; Huang, Ming
• Is Part Of: Computer methods and programs in biomedicine. Volume 205(2021)
• Publication Date: 2021-06
• Subjects: Malignant Ventricular Arrhythmias -- Prediction -- Heartbeat Interval -- Signal Complexity -- Machine-learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106102
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Malignant ventricular arrhythmias prediction. Prediction using short heartbeat time series. Intrinsically compatible with wearable personal heart tracking sensor. Abstract: Background and Objective: Malignant ventricular arrhythmias (MAs) occur unpredictably and lead to emergencies. A new approach that uses a timely tracking device e.g., photoplethysmogram (PPG) solely to predict MAs would be irreplaceably valuable and it is natural to expect the approach can predict the occurrence as early as possible. Method: We assumed that with an appropriate metric based on signal complexity, the heartbeat interval time series (HbIs) can be used to manifest the intrinsic characteristics of the period immediately precedes the MAs (preMAs). The approach first characterizes the patterns of preMAs by a new complexity metric (the refined composite multi-scale entropy). The MAs detector is then constructed by checking the discriminability of the MAs against the sinus rhythm and other prevalent arrhythmias (atrial fibrillation and premature ventricular contraction) of three machine-learning models (SVM, Random Forest, and XGboost). Results: Two specifications are of interest: the length of the HbIs needed to delineate the preMAs patterns sufficiently ( l s p e c ) and how long before the occurrence of MAs will the HbIs manifest specific patterns that are distinct enough to predict the impending MAs ( t s p e c ). Our experimental results confirmed the best performance came from a Random-Forest model with an average precision of 99.99% and recall of 88.98% using a HbIs of 800 heartbeats (the l s p e c ), 108 seconds (the t s p e c ) before the occurrence of MAs. Conclusion: By experimental validation of the unique pattern of the preMAs in HbIs and using it in the machine learning model, we showed the high possibility of MAs prediction in a broader circumstance, which may cover daily healthcare using the alternative sensor in HbIs monitoring. Therefore, this research is theoretically and practically significant in cardiac arrest prevention.