Diagnosis of long QT syndrome via support vector machines classification

@inproceedings{Bisgin2011DiagnosisOL,
  title={Diagnosis of long QT syndrome via support vector machines classification},
  author={Halil Bisgin and Orhan U. Kilinc and Ahmet Ugur and Xiaowei Xu and Volkan Tuzcu},
  year={2011}
}
Congenital Long QT Syndrome (LQTS) is a genetic disease and associated with significant arrhythmias and sudden cardiac death. We introduce a noninva-sive procedure in which Discrete Wavelet Trans-form (DWT) is used to extract features from elec-trocardiogram (ECG) time-series data first, then the extracted features data is classified as either abnormal or unaffected using Support Vector Machines (SVM). A total of 26 genetically identified patients with LQTS and 19 healthy controls were studied… CONTINUE READING

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