Automated Arrhythmia Detection Using War Strategy Optimization Enabled with Archimedes Optimization Algorithm and Rule-Based Classifiers

Heart arrhythmia is a life-threatening cardiological disorder that attacks due to imbalances in the heart's pulse rhythms. In this paper, we proposed a hybrid improved search ability-based integrated optimization algorithm named WSO-AOA: war search optimization and archimedes optimization algor...

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Veröffentlicht in:SN computer science Jg. 6; H. 1; S. 70
Hauptverfasser: Sahoo, Prakash Chandra, Pattnaik, Binod Kumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Singapore Springer Nature Singapore 07.01.2025
Springer Nature B.V
Schlagworte:
Accuracy
Adaptive systems
Algorithms
Arrhythmia
Automation
Cardiac arrhythmia
Classification
Computer Imaging
Computer Science
Computer Systems Organization and Communication Networks
Data Structures and Information Theory
Datasets
Decomposition
Deep learning
Electrocardiography
Embedded systems
Entropy
Feature extraction
Fuzzy logic
Health care facilities
Heart failure
Independent component analysis
Information Systems and Communication Service
Internet of medical things
Machine learning
Military strategy
Neural networks
Optimization
Optimization algorithms
Original Research
Patients
Pattern Recognition and Graphics
Sinuses
Software Engineering/Programming and Operating Systems
Support vector machines
Unleashing the Advances in ICT for Digital Transformation through Data Engineering and Data Analytics
Vision
Wavelet transforms
F-1 score and AUC
ELANFIS
Archimedes optimization
War search optimization
Wavelet packet transform
ISSN:2661-8907, 2662-995X, 2661-8907
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Zusammenfassung:Heart arrhythmia is a life-threatening cardiological disorder that attacks due to imbalances in the heart's pulse rhythms. In this paper, we proposed a hybrid improved search ability-based integrated optimization algorithm named WSO-AOA: war search optimization and archimedes optimization algorithm. In this work, three publicly available ECG databases, namely MIT-BIH Arrhythmia, MIT-BIH normal sinus rhythm, and BIDMC Congestive Heart Failure Databases from the Physio Net health center server are used for whole experimental analysis. For conducting this research, 12 features are applied to select the signal, and independent component analysis (ICA) is used for feature selection to choose the main feature components. Features are extracted with wavelet packet transform (WPT) and classified with well-known machine learning classifiers such as support vector machine (SVM), least square SVM (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and extreme learning ANFIS (ELANFIS). The proposed WSO-AOA-ELANFIS methodology outperforms in terms of accuracy for ARR (99.8%), CHF (100%), NSR (100%), sensitivity for ARR (100.%), CHF (100%), NSR (100%), Specificity for ARR (99.68%), CHF (100%), NSR (100%), and similarly G-mean, selectivity, F1-score, AuC are calculated. Our proposed algorithm has the potential for integration with the Internet of Medical Things (IoMT) and can be further evaluated using other publicly available ECG datasets.
Bibliographie:ObjectType-Article-1
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ISSN:2661-8907
2662-995X
2661-8907
DOI:10.1007/s42979-024-03604-8