[[abstract]]Electrocardiogram (ECG) is a representation of the electrical activity of heartbeats and it is quite an important signal for doctors to diagnose cardiac disease and monitor patient conditions. The shape of each ECG beat cycle as well as the interval time between two successive beats is commonly used for identifying the types of heart diseases. In this thesis, we propose a high performance ECG recognition system which adaptively selects the most important features from 446 candidate parameters and identifies the heart condition based on modified support vector machines (SVM). With tested by MIT-BIH Arrhythmia database, the final classification result can achieve 98.09% which is believed to be the best one in the published literatures. On the other hand, we also design a hardware engine dedicated for extracting wavelet transform based features and classification by SVM. The engine may help to speed up the recognition process and integrated into a portable device.
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