Floating search methods in feature selection
Pattern Recognition Letters
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ECG beat classification using neuro-fuzzy network
Pattern Recognition Letters
Neural Computing and Applications
Computers in Biology and Medicine
Adaptive wavelet network for multiple cardiac arrhythmias recognition
Expert Systems with Applications: An International Journal
Detection of valvular heart disorders using wavelet packet decomposition and support vector machine
Expert Systems with Applications: An International Journal
Artificial Intelligence in Medicine
IEEE Transactions on Information Technology in Biomedicine
An arrhythmia classification system based on the RR-interval signal
Artificial Intelligence in Medicine
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
ECG beat classification using a cost sensitive classifier
Computer Methods and Programs in Biomedicine
Real-time CHF detection from ECG signals using a novel discretization method
Computers in Biology and Medicine
Computer Methods and Programs in Biomedicine
Multistage approach for clustering and classification of ECG data
Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine
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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.