(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 11, 2018 383 | Page www.ijacsa.thesai.org *Corresponding Author. Features Optimization for ECG Signals Classification Alan S. Said Ahmad 1, * Department of Physics, Faculty of Science University of Zakho Kurdistan Region, Iraq Majd Salah Matti 2 , Adel Sabry Essa 3 Department of Computer Science, Faculty of Science University of Zakho Kurdistan Region, Iraq Omar A.M. ALhabib 4 College of Science International University of Erbil Kurdistan Region, Iraq Sabri Shaikhow 5 Department of Cardiology Azadi Teaching Hospital Duhok Kurdistan Region, Iraq Abstract—A new method is used in this work to classify ECG beats. The new method is about using an optimization algorithm for selecting the features of each beat then classify them. For each beat, twenty-four higher order statistical features and three timing interval features are obtained. Five types of beat classes are used for classification in this work, atrial premature contractions (APC), normal (NOR), premature ventricular contractions (PVC), left bundle branch (LBBB) and right bundle branch (RBBB). Cuttlefish algorithm is used for feature selection which is a new bio-inspired optimization algorithm. Four classifiers are used within CFA, Scaled Conjugate Gradient Artificial Neural Network (SCG-ANN), K-Nearest Neighborhood (KNN), Interactive Dichotomizer 3 (ID3) and Support Vector Machine (SVM). The final results show an accuracy of 97.96% for ANN, 95.71% for KNN, 94.69% for ID3 and 93.06% for SVM, these results were tested on fourteen signal records from MIT-HIH database, where 1400 beats were extracted from these records. Keywords—Features optimization; cuttlefish; ECG; ANN-SCG; ID3; KNN; SVM I. INTRODUCTION Automatic diagnosis of electrocardiogram (ECG) it is very important in the field of heart disease diagnosis, that is why feature extraction and classification it is an important step to achieve a good diagnosis [1, 2]. Many techniques have been proposed to classify ECG beat using data preprocessing, feature extraction, and classification algorithms. Some of these techniques are, Ali Kraiem and Faiza Charfi have used C4.5 technique to classify ECG beats using morphological features from signals denoised by band pass filter [3]. Ataollah Ebrahimzadeh and Ali Khazaee they have used wavelet transform and time interval features with radial base function for classification of five types of beats [4]. Ataollah Ebrahim and Ali Khazaee they have proposed a method for using morphological and time features with support vector machine for classification of 5 beat types [5]. Yakup Kutlua and Damla Kuntalp used nearest neighborhood (KNN) for classification of 5 beat types and higher order statistic features [6]. Ali Khazaee have used genetic algorithm with radial base function for classification of 3 beat types and morphological and timing interval features [7]. Ebrahimzadeh, Shakiba and Khazaee used higher order statistics with time interval features with radial base function and bees algorithm for classification of 5 beats [1]. Raju, Rao and Jagadesh used discrete wavelet transform as features and PCA with ANN to classify 5 beat types [8]. Inbalatha and Kalaivani have used wavelet transform with principle component analysis for features and K-nearest neighborhood for classification of 2 beat types [9]. Alan and Majd have proposed a method of using higher order statistics with time intervals as features and Artificial Neural Networks to classify 5 arrhythmia beat types [10]. Another strategy has been proposed in this work. This technique is comprising of four stages. To start with, ECG flag preprocessing utilizing denoising dependent on Discrete Wavelet Transform (DWT). The database of the flag records that are utilized is MIT-BIH database [11] in which two atrial untimely constrictions (APC) records are chosen, three ordinary (NOR) records, three untimely ventricular compressions (PVC) records, three remaining group branch (LBBB) records and three right package branch (RBBB) records are utilized. Second, highlights extraction from each flag's beat and standardized for advancement and grouping, these twenty-four higher request factual and three planning interim highlights will be utilized. Third, include choice by utilizing Cuttlefish improvement calculation. Fourth, grouping utilizing Artificial Neural Network Scaled Conjugate Gradient (ANN-SCG) classifier calculation. Figure 1 delineates this work. The remainder of this paper is as follows: section 2 contains an overview of the preprocessing technique used, Section 3 talks about the feature extraction process, and section 4 explains the usage of optimization with classification, while section 5 describes the datasets used, and sections 6 illustrated the results and discussion. Finally, section 7 describes the final conclusions.