PMID- 33933712
OWN - NLM
STAT- MEDLINE
DCOM- 20210518
LR  - 20210518
IS  - 1872-7565 (Electronic)
IS  - 0169-2607 (Linking)
VI  - 205
DP  - 2021 Jun
TI  - The feasibility of predicting impending malignant ventricular arrhythmias by 
      using nonlinear features of short heartbeat intervals.
PG  - 106102
LID - S0169-2607(21)00177-2 [pii]
LID - 10.1016/j.cmpb.2021.106102 [doi]
AB  - 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(spec)) and how 
      long before the occurrence of MAs will the HbIs manifest specific patterns that 
      are distinct enough to predict the impending MAs (t(spec)). 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(spec)), 108 seconds (the t(spec)) 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.
CI  - Copyright (c) 2021 Elsevier B.V. All rights reserved.
FAU - Chen, Zheng
AU  - Chen Z
AD  - Graduate School of Science and Technology, Nara Insitute of Science and 
      Technology, Japan.
FAU - Ono, Naoaki
AU  - Ono N
AD  - Graduate School of Science and Technology, Nara Insitute of Science and 
      Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, 
      Japan.
FAU - Chen, Wei
AU  - Chen W
AD  - Center for Intelligent Medical Electronics, Department of Electronic Engineering, 
      School of Information Science and Technology, Fudan University, Shanghai 200433, 
      China.
FAU - Tamura, Toshiyo
AU  - Tamura T
AD  - Institute for Healthcare Robotics, Waseda university, Japan.
FAU - Altaf-Ul-Amin, M D
AU  - Altaf-Ul-Amin MD
AD  - Graduate School of Science and Technology, Nara Insitute of Science and 
      Technology, Japan.
FAU - Kanaya, Shigehiko
AU  - Kanaya S
AD  - Graduate School of Science and Technology, Nara Insitute of Science and 
      Technology, Japan; Data Science Center, Nara Insitute of Science and Technology, 
      Japan.
FAU - Huang, Ming
AU  - Huang M
AD  - Graduate School of Science and Technology, Nara Insitute of Science and 
      Technology, Japan. Electronic address: alex-mhuang@is.naist.jp.
LA  - eng
PT  - Journal Article
DEP - 20210415
PL  - Ireland
TA  - Comput Methods Programs Biomed
JT  - Computer methods and programs in biomedicine
JID - 8506513
SB  - IM
MH  - *Atrial Fibrillation
MH  - Feasibility Studies
MH  - *Heart Arrest
MH  - Heart Rate
MH  - Humans
MH  - *Ventricular Premature Complexes/diagnosis
OTO - NOTNLM
OT  - Heartbeat Interval
OT  - Machine-learning
OT  - Malignant Ventricular Arrhythmias
OT  - Prediction
OT  - Signal Complexity
EDAT- 2021/05/03 06:00
MHDA- 2021/05/19 06:00
CRDT- 2021/05/02 20:47
PHST- 2020/07/18 00:00 [received]
PHST- 2021/04/05 00:00 [accepted]
PHST- 2021/05/03 06:00 [pubmed]
PHST- 2021/05/19 06:00 [medline]
PHST- 2021/05/02 20:47 [entrez]
AID - S0169-2607(21)00177-2 [pii]
AID - 10.1016/j.cmpb.2021.106102 [doi]
PST - ppublish
SO  - Comput Methods Programs Biomed. 2021 Jun;205:106102. doi: 
      10.1016/j.cmpb.2021.106102. Epub 2021 Apr 15.