Arrhythmia classification using Mahalanobis distance based improved Fuzzy C-Means clustering for mobile health monitoring systems

详细信息    查看全文

• 作者：Nur Al Hasan Haldar^aAuthor Vitae ; Farrukh Aslam Khan^aAuthor Vitae ; Aftab Ali^aAuthor Vitae ; Haider Abbas^a ; ^b ; ^{haidera@kth.se}Author Vitae
• 关键词：E-Health ; Arrhythmia ; Fuzzy C-Means clustering ; Body area network ; Mahalanobis-Taguchi System (MTS)
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：12 January 2017
• 年：2017
• 卷：220
• 期：Complete
• 页码：221-235
• 全文大小：2062 K
• 卷排序：220

文摘

An improved electrocardiogram (ECG) beats classification system is proposed based on Fuzzy C-Means (FCM) clustering algorithm. The attribute selection model is based on Mahalanobis-Taguchi System (MTS) which can dynamically choose the most relevant ECG features. The concept of initial cluster centroid is applied in order to reduce the number of program iterations of Fuzzy Clustering Techniques. Mahalanobis Distance based FCM (FCM-M) produced the significantly better results as compared to conventional Euclidian distance based FCM in case of Arrhythmia detection.