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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.(More)
In this paper, a method based on cascade Support Vector Machine (SVM) to classify electrocardiogram (ECG) has been proposed. First, we extract features by threshold based method and Independent Component Analysis (ICA) method. And then we discuss the construction of the model. When using SVM, we focus on how to choose the parameters, how to structure these(More)
An experience-based multi-lead (12 standard leads) decision model was presented for locating the ECG wave boundary. After getting 12 single-lead ECG boundary results from any single-lead detector (used threshold based method), the model first applied a data selecting and alignment algorithm to filter invalid records in each beat. Then valid data were(More)
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