caa a22 4500 606206566 CHVBK 20210128101012.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00034-014-9864-8 doi (NATIONALLICENCE)springer-10.1007/s00034-014-9864-8 Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases [Elektronische Daten] [Emina Alickovic, Abdulhamit Subasi] Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition method to diagnose heart diseases. The proposed method consists of three main modules: de-noising module, feature extraction module, and classifier module. In the de-noising module, multiscale principal component analysis (MSPCA) is used for noise reduction of the ECG signals. In the feature extraction module, autoregressive (AR) modeling is used for extracting features. In the classifier module, different classifiers are examined such as simple logistic, k-nearest neighbor, multilayer perceptron, radial basis function networks, and support vector machines. Different experiments are carried out using the MIT-BIH arrhythmia database to classify different ECG heart beats and the performance of the proposed method is evaluated in terms of several standard metrics. The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods. The numerical results indicated that the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling. Springer Science+Business Media New York, 2014 AR modeling nationallicence Support vector machines (SVM) nationallicence Simple logistic nationallicence k-Nearest neighbor (k-NN) nationallicence Multilayer perceptron (MLP) nationallicence Radial basis function networks (RBFN) nationallicence Alickovic Emina Faculty of Engineering and Information Technologies, International Burch University, Francuske Revolucije bb. Ilidza, 71000, Sarajevo, Bosnia and Herzegovina aut Subasi Abdulhamit Faculty of Engineering and Information Technologies, International Burch University, Francuske Revolucije bb. Ilidza, 71000, Sarajevo, Bosnia and Herzegovina aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/2(2015-02-01), 513-533 0278-081X 34:2<513 2015 34 34 https://doi.org/10.1007/s00034-014-9864-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-014-9864-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Alickovic Emina Faculty of Engineering and Information Technologies, International Burch University, Francuske Revolucije bb. Ilidza, 71000, Sarajevo, Bosnia and Herzegovina aut NATIONALLICENCE 700 1- Subasi Abdulhamit Faculty of Engineering and Information Technologies, International Burch University, Francuske Revolucije bb. Ilidza, 71000, Sarajevo, Bosnia and Herzegovina aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/2(2015-02-01), 513-533 0278-081X 34:2<513 2015 34 34