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Myocardial infarction evaluation from stopping time decision toward interoperable algorithmic states in reinforcement learning

Authors
 Jong-Rul Park  ;  Sung Phil Chung  ;  Sung Yeon Hwang  ;  Tae Gun Shin  ;  Jong Eun Park 
Citation
 BMC MEDICAL INFORMATICS AND DECISION MAKING, Vol.20(1) : 99, 2020-06 
Journal Title
BMC MEDICAL INFORMATICS AND DECISION MAKING
Issue Date
2020-06
MeSH
Algorithms ; Electrocardiography* ; Heart Rate ; Humans ; Myocardial Infarction* / diagnosis ; Neural Networks, Computer*
Abstract
Background: The Elliot wave principle commonly characterizes the impulsive and corrective wave trends for both financial market trends and electrocardiograms. The impulsive wave trends of electrocardiograms can annotate several wave components of heart-beats including pathological heartbeat waveforms. The stopping time inquires which ordinal element satisfies the assumed mathematical condition within a numerical set. The proposed work constitutes several algorithmic states in reinforcement learning from the stopping time decision, which determines the impulsive wave trends. Each proposed algorithmic state is applicable to any relevant algorithmic state in reinforcement learning with fully numerical explanations. Because commercial electrocardiographs still misinterpret myocardial infarctions from extraordinary electrocardiograms, a novel algorithm needs to be developed to evaluate myocardial infarctions. Moreover, differential diagnosis for right ventricle infarction is required to contraindicate a medication such as nitroglycerin.

Methods: The proposed work implements the stopping time theory to impulsive wave trend distribution. The searching process of the stopping time theory is equivalent to the actions toward algorithmic states in reinforcement learning. The state value from each algorithmic state represents the numerically deterministic annotated results from the impulsive wave trend distribution. The shape of the impulsive waveform is evaluated from the interoperable algorithmic states via least-first-power approximation and approximate entropy. The annotated electrocardiograms from the impulsive wave trend distribution utilize a structure of neural networks to approximate the isoelectric baseline amplitude value of the electrocardiograms, and detect the conditions of myocardial infarction. The annotated results from the impulsive wave trend distribution consist of another reinforcement learning environment for the evaluation of impulsive waveform direction.

Results: The accuracy to discern myocardial infarction was found to be 99.2754% for the data from the comma-separated value format files, and 99.3579% for those containing representative beats. The clinical dataset included 276 electrocardiograms from the comma-separated value files and 623 representative beats.

Conclusions: Our study aims to support clinical interpretation on 12-channel electrocardiograms. The proposed work is suitable for a differential diagnosis under infarction in the right ventricle to avoid contraindicated medication during emergency. An impulsive waveform that is affected by myocardial infarction or the electrical direction of electrocardiography is represented as an inverse waveform.
Files in This Item:
T999202207.pdf Download
DOI
10.1186/s12911-020-01133-x
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Emergency Medicine (응급의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Laboratory Medicine (진단검사의학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Thoracic and Cardiovascular Surgery (흉부외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Tae Hoon(김태훈) ORCID logo https://orcid.org/0000-0003-3987-0057
Song, Suk Won(송석원) ORCID logo https://orcid.org/0000-0002-9850-9707
Yoo, Kyung Jong(유경종) ORCID logo https://orcid.org/0000-0002-9858-140X
Chung, Sung Phil(정성필) ORCID logo https://orcid.org/0000-0002-3074-011X
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184949
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