Multi-lead ECG Classification based on Independent Component Analysis and Support Vector Machine

An novel multi-lead Electrocardiogram (ECG) classification method is proposed in this paper. At the feature extracting stage, an improved Independent Component Analysis (ICA) method is introduced. In our method, a heartbeat is intercepted into 3 segments (P wave, QRS interval, ST segment). ICA is used to extract the features of each segment separately. These three feature vectors construct the feature of single lead firstly. Then, twelve single lead feature vectors are combined to generate a multi-lead feature vector one by one. At last, the Support Vector Machine (SVM) is used for multi-classification and 2-classification experiments. All available data in MIT-BIH Arrhythmia Database and the number of 2500 practical data gathered from about 500 persons is used in experiments simultaneously. For MIT-BIH data, multi-classification result is discussed. The final average accuracy of the testing data is 98.18% and the average sensitivity is 98.68%. For practical data, 2-classification experiment result is discussed. The accuracy of testing data is 90.47% and the sensitivity is 90.01%.