Optimal Neuro Fuzzy Classification for Arrhythmia Data Driven System
Hela Lassoued1, Raouf Ketata2, Hajer Ben Mahmoud3
1Hela Lassoued*, National Institute of Applied Science and Technology INSAT, Tunis, Tunisia.
2Raouf Ketata, National Institute of Applied Science and Technology INSAT, Tunis, Tunisia.
3Hajer Ben Mahmoud, National Institute of Applied Science and Technology INSAT, Tunis, Tunisia.
Manuscript received on November 23, 2021. | Revised Manuscript received on November 28, 2021. | Manuscript published on November 30, 2021.. | PP: 70-80 | Volume-11, Issue-1, November 2021 | Retrieval Number: 100.1/ijitee.A96281111121 | DOI: 10.35940/ijitee.A9628.1111121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a data driven system used for cardiac arrhythmia classification. It applies the Neuro-Fuzzy Inference System (ANFIS) to classify MIT-BIH arrhythmia database electrocardiogram (ECG) recordings into five (5) heartbeat types. In fact, in order to obtain the input feature vector from recordings, a time scale method based on a Discrete Wavelet Transform (DWT) was investigated. Then, the time scale features are selected by applying the Principal Component Analysis (PCA). Therefore, the selected input feature vectors are classified by the Neuro-Fuzzy method. However, the ANFIS configuration needs mainly the choice of an initial Fuzzy Inference System (FIS) and the training algorithm. Indeed, two clustering algorithms which are the fuzzy c-means (FCM) and the subtractive ( SUBCLUST) algorithms, are applied to generate the initial FIS. Besides, for tuning the ANFIS membership function and rule base parameters, Gradient descent and evolutionary training algorithms are also evaluated. Gradient descent consists of the backpropagation (BP) method and its hybridization with the least square algorithm (Hybrid). However, the evolutionary training methods involve the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA). Therefore, eight (8) ANFIS are configured and assessed. Accordingly, a comparison study between their obtained Root Mean Square Error (RMSE) is analyzed. At the end, we have selected an optimal ANFIS which uses the SUBTRUCT algorithm to generate the initial FIS and the GA to tune its parameters. Moreover, to guarantee the effectiveness of this work, a comparison study with related works is done.
Keywords: Data driven system, classification, arrhythmias Neuro-Fuzzy, gradient descent, evolutionary algorithms, optimization.