The feasibility of predicting impending malignant ventricular arrhythmias by using nonlinear features of short heartbeat intervals.
Zheng Chen, Naoaki Ono, Wei Chen, Toshiyo Tamura, M D Altaf-Ul-Amin, Shigehiko Kanaya, Ming Huang
Author Information
Zheng Chen: Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan.
Naoaki Ono: Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan.
Wei Chen: Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China.
Toshiyo Tamura: Institute for Healthcare Robotics, Waseda university, Japan.
M D Altaf-Ul-Amin: Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan.
Shigehiko Kanaya: Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, Japan.
Ming Huang: Graduate School of Science and Technology, Nara Insitute of Science and Technology, Japan. Electronic address: alex-mhuang@is.naist.jp.
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) and how long before the occurrence of MAs will the HbIs manifest specific patterns that are distinct enough to predict the impending MAs (t). 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), 108 seconds (the t) 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.
