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Arrhythmia classification on ECG using Deep Learning

Publication Type : Conference Paper

Publisher : IEEE

Source : 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS), IEEE, Coimbatore, India (2019)

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Keywords : Arrhythmia, Classification algorithms, CNN, Databases, ECG, Electrocardiography, Feature extraction, Heart beat, lead, MIT-BIH Database

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2019

Abstract : In this paper, an intellectual based electrocardiogram (ECG) signal classification approach utilizing Deep Learning (DL) is being developed. ECG plays important role in diagnosing various Cardiac ailments. The ECG signal with irregular rhythm is known as Arrhythmia such as Atrial Fibrillation, Ventricular Tachycardia, Ventricular Fibrillation, and so on. The main aspire of this task is to screen and distinguish the patient with various cardio vascular arrhythmia. This examination encourages us to recognize diverse kinds of arrhythmia utilizing Deep Learning algorithm. Here we use Convolutional Neural Network (CNN) a DL algorithm which is efficient in classifying signals. Utilizing CNN, features are learned Automatically from the time domain ECG signals which are acquired from MIT-BIH Database from The feature adapted specifically replaces manually extracted features and this analysis will help the Cardiologists in screening the patient with Cardiac illness effectively. The CNN is trained, tested using ECG Dataset obtained from MIT-BIH Database and from the signal 7 of arrhythmia were classified. The proposed system is compared for Various Activation function by varying the number of epochs. From the result obtained we came to know that ELU activation function gives better result with an accuracy of 93.6% and with a loss of 0.2.

Cite this Research Publication : A. Rajkumar, Ganesan, M., and Dr. Lavanya R., “Arrhythmia classification on ECG using Deep Learning”, in 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS), Coimbatore, India, 2019.

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