Bivariate markov model based analysis of ecg for accurate identification and classification of premature heartbeats and irregular beat-patterns

Title

Bivariate markov model based analysis of ecg for accurate identification and classification of premature heartbeats and irregular beat-patterns

Creator

Gawde PR; Bansal AK; Nielson JA; Khan JI

Publisher

Intelligent Systems And Applications, INTELLISYS, Vol 2

Date

2019
1905-07

Description

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.

Subject

Machine learning; ECG analysis; Intelligent system; Irregular beat pattern; Markov model; Medical diagnosis; Premature beat classification; Real-time system; Signal analysis

Rights

Article information provided for research and reference use only. All rights are retained by the journal listed under publisher and/or the creator(s).

Format

journalArticle

Search for Full-text

Users with a NEOMED Library login can search for full-text journal articles at the following url: https://libraryguides.neomed.edu/home

Pages

265-285

Volume

869

NEOMED College

NEOMED College of Medicine

NEOMED Department

Department of Emergency Medicine

Update Year & Number

January 2021 List

Affiliated Hospital

Summa Health Akron City Hospital

Citation

Gawde PR; Bansal AK; Nielson JA; Khan JI, “Bivariate markov model based analysis of ecg for accurate identification and classification of premature heartbeats and irregular beat-patterns,” NEOMED Bibliography Database, accessed December 8, 2021, https://neomed.omeka.net/items/show/11535.

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