| Summary: | 碩士 === 國立中正大學 === 電機工程研究所 === 104 === This thesis proposes a Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) identification method and its application to Sudden Cardiac Death (SCD) prediction based on electrocardiogram (ECG). These two systems used ECG of 5 seconds and 60 seconds to separately identify VT, VF and predict SCD. The research of SCD prediction were based on signals in specific period of time before the onset of VF.
This study is divided into two parts. The first part is arrhythmia (VT and VF) identification. The ECG signals used in the experiment were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). The second part is SCD prediction. The ECG signals were obtained from the MIT-BIH Sudden Cardiac Death Holter Database (SDDB). The system architecture was divided into pre-processing, feature extraction, feature normalization, feature selection and classification. Firstly, the original signals were preprocessed to remove unnecessary noise. Secondly, feature extraction and feature normalization were used to obtain the features for classification. Thirdly, traditional genetic algorithm (GA), modified genetic algorithm (MGA), multi-objective genetic algorithm (NSGA-II) and P value were used for feature selection to reduce the feature dimensions. Their performance in improving the identification and prediction accuracy rate was compared. Strategies for selecting features that are suitable for using both in the training set and test set were discussed. Finally, support vector machine (SVM) was used to classify these heart rhythms, and the five-fold scheme was used for cross-validation.
In the first part, a sensitivity (Se) of 93.05%, a specificity (Sp) of 96.84%, and an accuracy (Ac) of 95.6% were obtained on the training dataset. And, an Se of 92.55%, an Sp of 96.43%, and an Ac of 95.52% were obtained on the test dataset.
In the second part, this study can predict the SCD ten minutes before its onset with an Se of 76.25%, an Sp of 82.5%, and an Ac of 79.38% on the training dataset, and an Se of 72.5%, an Sp of 82.5%, and an Ac of 77.5% on the test dataset. According to the results, the systems for arrhythmia identification and SCD prediction proposed in this study imposing accuracy rates, and demonstrate their capability to be used in medical care to assist health care workers handle their patient’s condition.
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