Effective and efficient detection of premature ventricular contractions based on variation of principal directions

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• 作者：Roozbeh Zarei^a ; ^{roozbeh.zarei@live.vu.edu.au" class="auth_mail" title="E-mail the corresponding author} ; ^{roozbeh.zarei@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jing He^b ; ^cAuthor Vitae ; Guangyan Huang^dAuthor Vitae ; Yanchun Zhang^a ; ^eAuthor Vitae
• 关键词：Electrocardiogram (ECG) ; Premature Ventricular Contraction (PVC) ; Principal Component Analysis (PCA) ; MIT-BIH arrhythmia database
• 刊名：Digital Signal Processing
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：50
• 期：Complete
• 页码：93-102
• 全文大小：1236 K

文摘

Classification of electrocardiogram (ECG) data stream is essential to diagnosis of critical heart conditions. It is vital to accurately detect abnormality in the ECG in order to prevent possible beginning of life-threatening cardiac symptoms. In this paper, we focus on identifying premature ventricular contraction (PVC) which is one of the most common heart rhythm abnormalities. We use “Replacing” strategy to check the effects of each individual heartbeat on the variation of principal directions. Based on this idea, an online PVC detection method is proposed to classify the new arriving PVC beats in the real-time and online manner. The proposed approach is tested on the MIT-BIH arrhythmia database (MIT-BIH-AR). The PVC detection accuracy was 98.77%, with the sensitivity and positive predictivity of 96.12% and 86.48%, respectively. These results are an improvement on previous reported results for PVC detection. In addition, our proposed method is effective in terms of computation time. The average execution time of our proposed method was 3.83 s for a 30 min ECG recording. It shows the capability of the classifier to detect abnormal PVCs in online manner.