Deep Learning based Arrhythmia Classification with an ECG Acquisition System
Roshan Badrinath1, Abhay Navada2, Harshith Narahari3, Ankit Datta4, Dr. Sarojadevi H.5
1Roshan Badrinath*, Completed his Bachelor of Engineering Degree in Computer Science and Engineering from Nitte Meenakshi Institute of Technology (NMIT), Bengaluru, India.
2Abhay Navada, Bachelor of Engineering Degree in Computer Science and Engineering from NMIT, Bengaluru, India.
3Harshith Narahari, has Completed his Bachelor of Engineering degree in Computer Science and Engineering from NMIT, Bengaluru.
4Ankit Datta, Completed his Bachelor of Engineering degree in Computer Science and Engineering from NMIT, Bengaluru.
5Dr. Sarojadevi H., Professor in the Department Of CSE at NMIT, Bengaluru, India.
Manuscript received on November 16, 2019. | Revised Manuscript received on 27 November, 2019. | Manuscript published on December 10, 2019. | PP: 3849-3852 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7498129219/2019©BEIESP | DOI: 10.35940/ijitee.B7498.129219
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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: One of the issues that the human body faces is arrhythmia, a condition where the human heartbeat is either irregular, too slow or too fast. One of the ways to diagnose arrhythmia is by using ECG signals, the best diagnostic tool for detection of arrhythmia. This paper describes a deep learning approach to check whether signs of arrhythmia, in a given input signal, are present or not. A batch normalized CNN is used to classify the ECG signals based on the different types of arrhythmia. The model has achieved 96.39% training accuracy and 97% testing accuracy. The ECG signals are classified into five classes namely: Normal beats, Premature Ventricular Contraction (PVC) beats, Right Bundle Branch Block (RBBB) beats, Left Bundle Branch Block (LBBB) beats and Paced beats. A peak detection algorithm with six simple steps is designed to detect R-peaks from the ECG signals. A hardware device is built using Raspberry Pi to acquire ECG signals, which are then sent to the trained CNN for classification. The data-set for training is obtained from the MIT-BIH repository. Keras and Tensorflow libraries are used to design and develop the CNN and an application is designed using ’MEAN’ stack and ’Flask’ based servers.
Keywords: ECG Classification, Arrhythmia, Convolutional Neural Network, Batch Normalization, Peak Detection, IoT
Scope of the Article: Classification