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Text
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<a href="http://doi.org/10.1007/978-3-030-01057-7_22" target="_blank" rel="noreferrer noopener">http://doi.org/10.1007/978-3-030-01057-7_22</a>
Pages
265-285
Volume
869
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Update Year & Number
January 2021 List
NEOMED College
NEOMED College of Medicine
NEOMED Department
Department of Emergency Medicine
Affiliated Hospital
Summa Health Akron City Hospital
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Title
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Bivariate markov model based analysis of ecg for accurate identification and classification of premature heartbeats and irregular beat-patterns
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Intelligent Systems And Applications, INTELLISYS, Vol 2
Date
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2019
1905-07
Subject
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Machine learning; ECG analysis; Intelligent system; Irregular beat pattern; Markov model; Medical diagnosis; Premature beat classification; Real-time system; Signal analysis
Creator
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Gawde PR; Bansal AK; Nielson JA; Khan JI
Description
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This paper describes a novel intelligent analysis technique based upon bivariate Markov model that integrates morphological and temporal features with a rule-based interval analysis of ECG signals to localize and accurately classify the premature beats to four major classes: (1) Premature Atrial Complex (PAC), (2) Blocked PAC (B-PAC), (3) Premature Ventricular Complex (PVC), and (4) Premature Junctional Complex (PJC). The paper also describes a beat-pattern classification algorithm to sub classify premature beat-patterns into bigeminy, trigeminy and quadrigeminy. The approach utilizes two phases: (1) a training phase that builds bivariate Markov model from standardized databases of ECG signals, and (2) a dynamic phase that detects embedded P and R waves in T-waves of premature beats using a combination of area subtraction and clinically significant rule-based analysis of R-R intervals. It detects and classifies premature beats using graph matching based upon the forward-backward algorithm and performs a look ahead pattern analysis for the sub-classification of beat-patterns. The algorithms have been presented. The software has been implemented that uses a combination of MATLAB and C++ libraries. Performance results show that processing time is realistic for real-time detection with 98%-99% sensitivity for the premature beat classification and 95%-98% sensitivity for the beat pattern identification.
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<a href="http://doi.org/10.1007/978-3-030-01057-7_22" target="_blank" rel="noreferrer noopener">10.1007/978-3-030-01057-7_22</a>
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journalArticle
2019
Bansal AK
Department of Emergency Medicine
ECG analysis
Gawde PR
Intelligent system
Intelligent Systems And Applications
Intelligent Systems And Applications, INTELLISYS, Vol 2
INTELLISYS
Irregular beat pattern
January 2021 List
journalArticle
Khan JI
Machine learning
Markov model
Medical diagnosis
NEOMED College of Medicine
Nielson JA
Premature beat classification
Real-time system
Signal analysis
Summa Health Akron City Hospital
Vol 2