A new BAT optimization algorithm based feature selection method for electrocardiogram heartbeat classification using emp
- PDF / 1,711,031 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 102 Downloads / 214 Views
ORIGINAL ARTICLE
A new BAT optimization algorithm based feature selection method for electrocardiogram heartbeat classification using empirical wavelet transform and Fisher ratio Atul Kumar Verma1 · Indu Saini1 · Barjinder Singh Saini1 Received: 1 June 2018 / Accepted: 10 April 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In this paper, a novel feature selection method is proposed for the categorization of electrocardiogram (ECG) heartbeats. The proposed technique uses the Fisher ratio and BAT optimization algorithm to obtain the best feature set for ECG classification. The MIT-BIH arrhythmia database contains sixteen classes of the ECG heartbeats. The MIT-BIH ECG arrhythmia database divided into intra-patient and inter-patient schemes to be used in this study. The proposed feature selection methodology works in following steps: firstly, features are extracted using empirical wavelet transform (EWT) and then higher-order statistics, as well as symbolic features, are computed for each decomposed mode of EWT. Thereafter, the complete feature vector is obtained by the conjunction of EWT based features and RR interval features. Secondly, for feature selection, the Fisher ratio is utilized. It is optimized by using BAT algorithm so as to have maximal discrimination of the between classes. Finally, in the classification step, the k-nearest neighbor classifier is used to classify the heartbeats. The performance measures i.e., accuracy, sensitivity, positive predictivity, specificity for intra-patient scheme are 99.80%, 99.80%, 99.80%, 99.987% and for inter-patient scheme are 97.59%, 97.589%, 97.589%, 99.196% respectively. The proposed feature selection technique outperforms the other state of art feature selection methods. Keywords Heartbeats feature selection · BAT based feature selection · Heartbeat classification · Arrhythmia classification · EWR with BAT feature selection
1 Introduction The analysis of the electrocardiogram (ECG) signal provides inexpensive and non-invasive methods to analyze the function of the heart for different cardiac disorders. According to the report of the American Heart Association (AHA) [1], the major cause for the death of human beings is due to cardiac heart diseases. So, the accurate diagnosis of heart diseases is vital for the correct treatment of cardiac patients. The ECG signal includes lots of pathological information about the * Atul Kumar Verma [email protected] Indu Saini [email protected] Barjinder Singh Saini [email protected] 1
Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab 144011, India
patient’s heart activities which is important for the diagnosis of heart arrhythmias [2, 3]. The cardiac arrhythmias are a group of disorders in which the electrical activity of the heart is irregular, slower or faster than normal. The exploration of cardiac arrhythmia is very important for doctors to make accurate clinical diagnosis. For the classification of cardiac arrhythmia, the ECG heartbeat segmentation is very importa
Data Loading...
