A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree - Chou, Lijuan; Liu, Jicheng; Gong, Shengrong; Chou, Yongxin (Frontiers in Physiology, 2022)
Extreme bradycardia (EB), Extreme tachycardia (ET), ventricular tachycardia (VT) and ventricular flutter (VF) are the four types of life-threatening arrhythmias, which are symptoms of cardiovascular diseases. Therefore, in this study, a method of life-threatening arrhythmias recognition is proposed based on pulse rate variability (PRV). Firstly, noise and interference are wiped out from the arterial blood pressure (ABP) and PRV signal is extracted. Then, nineteen features are extracted from PRV signal and fifteen features with highly importance andsignificant variation were selected by random forest (RF). Finally, the back-propagation neural network (BPNN), extreme learning machine (ELM) and decision tree (DT) are exploited to build, train and test classifiers to detect life-threatening arrhythmias. The experimental data is obtained from the MIMIC/Fantasia and the 2015 Physiology Net/CinC Challenge databases. The experimental results show that the DT classifier has the best average performance with accuracy and kappa coefficient (kappa) of 98.76 ± 0.08% and 97.59 ± 0.15%, which is higher than the BPNN (accuracy=94.85 ± 1.33%, kappa=89.95 ± 2.62%) and ELM (accuracy =95.05 ± 0.14%, kappa=90.28 ± 0.28%) classifiers. The proposed method has better performance in identifying four life-threatening arrhythmias compared to existing methods and has potential to be employed for home monitoring of patients with life-threatening arrhythmias.