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Electrocardiography is a common tool for detecting cardiovascular system diseases. In clinical, as the individual difference is an intrinsic feature of ECG, data distribution difference between training and testing data impacts on the accuracy of classifier. Automatic ECG classification satisfied clinical demand is urgently required. QRS is a main waves in a heartbeat. In this paper, we propose a...
In this paper, an expert experience based Electrocardiogram (ECG) classification method using domain knowledge and morphology information is presented. Firstly, the process of ECG interpretation by physicians is analyzed. Then, the construction method of classification model based on Support Vector Machine (SVM) is discussed and morphology information extraction approach through Principal Component...
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...
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 assigned to different weights in each lead for...
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