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Related Experiment Videos

An Intelligent IoT-Compatible Arrhythmia Detection System Using a Hybrid Vision Transformer-LSTM Framework.

S Selva Birunda1, V Vaissnave2, K Abirami3

  • 1Department of Artificial Intelligence and Data Science, Ramco Institute of Technology, Rajapalayam, India. selvabirunda89@gmail.com.

Cardiovascular Engineering and Technology
|July 8, 2026
PubMed
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This summary is machine-generated.

A new hybrid deep learning model combining Long Short-Term Memory (LSTM) and Vision Transformer (ViT) accurately classifies electrocardiogram (ECG) signals for arrhythmia detection. This advanced system achieves 99.17% accuracy, proving effective for IoT healthcare applications.

Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • The Internet of Things (IoT) enables advanced medical signal processing for electrocardiogram (ECG) signal classification.
  • Detecting and classifying arrhythmias from large, complex ECG datasets remains a significant challenge.

Purpose of the Study:

  • To propose a hybrid deep learning (DL) approach combining Long Short-Term Memory (LSTM) and Vision Transformer (ViT) for automatic arrhythmia classification from ECG signals.
  • To develop an efficient and accurate system for real-time ECG analysis in IoT-compatible healthcare settings.

Main Methods:

  • Utilized the MIT-BIH database for training the DL model.
  • Applied preprocessing techniques including min-max normalization and Stockwell transform for signal-to-spectrogram conversion.
Keywords:
Arrhythmia detectionDeep learningHeart disease monitoringInternet of thingsLong short-term memoryMIT-BIH databaseVision transformer

Related Experiment Videos

  • Employed the K-Means centered Adaptive Synthetic Sampling (KMADASYN) technique to address data imbalance.
  • Implemented a Hybrid Optimized ViT with Long Short-Term Memory (HOVLSTM) for extracting spatial and temporal features.

    • Main Results:

    • The proposed HOVLSTM model achieved a classification accuracy of 99.17%.
    • The model demonstrated superior performance compared to existing ECG classification systems.

    Conclusions:

    • The developed technique is well-suited for IoT-compatible healthcare systems.
    • The model's evaluation in a simulated IoT environment highlights its potential for future clinical decision-support systems.