Title: Cardiac arrhythmia classification of imbalanced data using convolutional autoencoder and LSTM techniques

Authors: Rekha Rajagopal; V. Shyam Kumar

Addresses: Department of Information Technology, PSG College of Technology, Coimbatore, India ' Department of Information Technology, PSG College of Technology, Coimbatore, India

Abstract: Cardiovascular diseases (CVD) can be identified by medical professionals with the help of electrocardiogram (ECG) signals. The ECG signals shows the heart rhythm and any irregularity in heart rhythm is called arrhythmia. The arrhythmias can be broadly classified into five categories: 1) class N; 2) class S; 3) class V; 4) class F; 5) class Q. The proposed research work automatically categorises the ECG beats into one of the five classes using long short-term memory (LSTM). The ECG waveform is divided into individual ECG beats and is provided as input to the convolutional autoencoders. The compressed representation of the encoder is used as features for further classification by LSTM. The class imbalance problem in the dataset is overcome using ADASYN technique. The proposed research work gives an overall accuracy of 99.12%.

Keywords: arrhythmia; long short-term memory; LSTM; autoencoder; ADASYN; deep learning; disease classification; convolutional neural network; CNN.

DOI: 10.1504/IJMEI.2025.143281

International Journal of Medical Engineering and Informatics, 2025 Vol.17 No.1, pp.54 - 62

Received: 23 Feb 2022
Accepted: 22 Jul 2022
Published online: 12 Dec 2024 *

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