Cardiovascular disease (CVD) is dangerous and has a high mortality rate. One of the signs of CVD is arrhythmia. The method to find out the symptoms of arrhythmia is an electrocardiogram (ECG) long-term monitoring. The duration of ECG analysis on long-term monitoring is 6 hours, 12 hours, or 24 hours. The device used for longterm ECG is a Holter device. Arrhythmia analysis has followed: detect QRS complex and continue arrhythmia classification. Holter data is stored in local memory or on the server. Storage on local storage makes it difficult for cardiologists to perform analysis. However, using server storage causes access times to slow if patient data increases. Because of this, the cardiologist needs a powerful processing unit to analyze arrhythmia. This study proposes Arrhythmia analysis in the long-term ECG monitoring system. ECG acquisition is sent and stored in the processing unit. We use a single-board computer (SBC) Raspberry Pi as a processing unit. Besides storing data, SBC is also analyzing arrhythmias. The analysis steps are detecting R-peak using the Pan-Tompkins (PTK) algorithm, removing P and T waves using the Gaussian filter, and arrhythmia classification using the multi-layer perceptron (MLP). MLP is a low computational deep learning, which is suitable for SBC. The total storage delay consists of sending and storing data in the database. In the experiment, the propagation data to the broker is 0.023 s., and the storage time to the SQLite database is 15.16 s. The limited recording time for acquisition data is 21 hours and 36 minutes. The success rate of the device in detecting the QRS complex has a precision (+P) of 98.61% and a sensitivity (Se) of 99.8%. Our classification has good in Acc, Se, Spe, +P, and F1-scores are 99.77, 99.55, 99.55,99.85, and 99.55, respectively. The method is superior to several other arrhythmia classification studies.

Original languageEnglish
Pages (from-to)289-300
Number of pages12
JournalInternational Journal of Intelligent Engineering and Systems
Issue number5
Publication statusPublished - 2023


  • Arrhythmia classification
  • ECG
  • Holter
  • IOT
  • Telemedicine


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