Myocardial Infarction Detection and Localization Using Optimal Features Based Lead Specific Approach - 05/10/19
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Abstract |
Objectives |
Objective of this paper is to present a reliable and accurate technique for Myocardial Infarction (MI) detection and localization.
Material and methods |
Stationary wavelet transform has been used to decompose the ECG signal. Energy, entropy and slope based features were extracted at specific wavelet bands from selected lead of ECG. k-Nearest Neighbors (kNN) with Mahalanobis distance function has been used for classification. Sensitivity (Se), specificity (Sp), positive predictivity (+P), accuracy (Acc), and area under the receiver operating characteristics curve (AUC) analyzed over 200 subjects (52 health control, 148 with MI) from Physikalisch-Technische Bundesanstalt (PTB) database has been used for performance analysis. To handle the imbalanced data adaptive synthetic (ADASYN) sampling approach has been adopted.
Results |
For detection of MI, the proposed technique has shown an AUC = 0.99, Se = 98.62%, Sp = 99.40%, PPR = 99.41% and Acc = 99.00% using 12 top ranked features, extracted from multiple leads of ECG and AUC = 0.99, Se = 98.34%, Sp = 99.77%, PPR = 99.77% and Acc = 99.05% using 12 features extracted from a single ECG lead (i.e. lead V5). For localization of MI, the proposed technique has an AUC = 0.99, Se = 98.78%, Sp = 99.86%, PPR = 98.80%, and Acc = 99.76% using 5 top ranked features from multiple leads of ECG and AUC = 0.98, Se = 96.47%, Sp = 99.60%, PPR = 96.49% and Acc = 99.28% using 8 features extracted from a single ECG lead (i.e. lead V3).
Conclusion |
Thus for MI detection and localization, the proposed technique is independent of time-domain ECG fiducial markers and can work using specific leads of ECG.
Le texte complet de cet article est disponible en PDF.Graphical abstract |
Highlights |
• | This work presents a technique for both MI detection and localization. |
• | A short duration ECG segment has been used, for early detection of MI. |
• | Proposed technique is free from time-domain fiducial markers detection errors. |
• | In this work disparity in the data size has been considered and handled. |
• | MI detection and localization using features extracted from single leads of ECG. |
Keywords : Electrocardiogram (ECG), Myocardial Infarction (MI), Stationary wavelet transform (SWT), Feature extraction, Feature Selection, k-Nearest Neighbors (kNN)
Plan
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