کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10345677 | 698362 | 2012 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
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چکیده انگلیسی
This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. Then, higher order statistics of WPC are derived. Finally, the obtained feature set is used as input to a classifier, which is based on k-NN algorithm. The MIT-BIH arrhythmia database is used to obtain the ECG records used in this study. All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods and Programs in Biomedicine - Volume 105, Issue 3, March 2012, Pages 257-267
Journal: Computer Methods and Programs in Biomedicine - Volume 105, Issue 3, March 2012, Pages 257-267
نویسندگان
Yakup Kutlu, Damla Kuntalp,