Multi-lead ECG heartbeat classification of heart disease based on HOG local feature descriptor

ECG data play an important role in the diagnostics of various cardiovascular diseases. Classification of multi-lead ECG signals could be challenging even for well-trained physicians. In this study we propose a new approach for multi-lead ECG classification. Five-types of 15-lead ECG data namely healthy control, bundle branch block, cardiomyopathy, Dysrhythmia, and myocardial infarction patients from two types of datasets, 5319 and 6647 heartbeats from Baqiyatallah and PTB Diagnostic ECG database, were used, respectively. One-dimensional total variation regularization was used to denoising ECG data. Heartbeats were extracted by one cardiologist and saved as images with jpg format. Histogram of oriented gradients method was used to extract feature of images. for classification task support vector machine and fully connected neural network were used. Five-fold cross validation was used for validating the models. For 15-lead ECG PTB Diagnostic database, the best classification models are SVM model with cubic (accuracy: 99.9%, Range: 99.77% - 100%) and quadratic (accuracy: 99.88%, Range: 99.77%-100%) kernel function, for this dataset fully connected accuracy is 99.4% with range of 99.02%- 99.70%. Regarding to the Baqyatallah dataset SVM with cubic (accuracy: 99.83%, Range:99.72%-100%) and quadratic (accuracy: 99.77%, Range: 99.62%-99.9%) were the best classification model and the accuracy for fully connected neural network was 99.1% with the range of 98.59%-99.62% based on HOG descriptors. Expected sigmodal kernel all classification method have accuracy more than 99%. simultaneous use of HOG feature extraction method and appropriate classification algorithm such as SVM or fully connected neural network can classify 15-lead ECG heart-beat for different heart disease with high accuracy and adding other relevant patients’ information can be easily done in order to increase the method performance.