Detection of multi-class arrhythmia using heuristic and deep neural network on edge device

Arief Kurniawan, Eko Mulyanto Yuniarno, Eko Setijadi, Mochamad Yusuf Alsagaff, Gijsbertus Jacob Verkerke, I. Ketut Eddy Purnama*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class.

Original languageEnglish
Pages (from-to)429-444
Number of pages16
JournalInternational Journal of Advances in Intelligent Informatics
Issue number3
Publication statusPublished - Nov 2023


  • Arrhythmia classification
  • Arrhythmia detection
  • Deep learning
  • Edge computing
  • Electrocardiogram


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