Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records

Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records

Background and objective
Arrhythmia constitute a common clinical problem in cardiology. The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG interpretation by experts is expensive and time-consuming. In this work, we developed and validated an arrhythmia classification model based on handcrafted features, which was more computationally efficient than traditional deep learning models.

Material and method
The classification model comprised (i) a specific feature extraction function based on the homeomorphically irreducible tree (HIT) graph pattern, (ii) multilevel feature generation based on maximum absolute pooling, (iii) Chi2 feature selector, and (iv) standard support vector machine classifier. We trained and validated the model on a large dataset comprising 12-leads ECGs acquired from more than 10,000 subjects. Performance metrics were reported for seven- (Case 1) and four-class (Case 2) arrhythmia diagnosis.

Results
High classification accuracy rates of 92.95% and 97.18% were attained for Case 1 and Case 2, respectively, that were comparable with those of deep learning on the same ECG dataset.

Conclusion
The model achieved excellent classification results at low computational cost, which underscores the potential for real world application of the proposed HIT-based ECG classification model.