đ
07 June 2026
DOI: 10.59485/jtemp.v7i1.190
Analysis of Cardiac Arrhythmia Detection Using Spectrograms and Convolutional Neural Networks (CNN) for Nursing Care
MEDIKA TRADA : Jurnal Teknik Elektomedik Polbitrada
Politeknik Bina Trada Semarang
đ Abstract
Cardiac arrhythmia is a leading cause of sudden death, requiring rapid detection in emergency department nursing care. Manual interpretation of electrocardiograms (ECGs) is often time-consuming and prone to human error. This study aims to develop a lightweight and accurate automated arrhythmia classification system using a deep learning approach. The proposed method transforms one-dimensional ECG signals into two-dimensional visual representations in the form of spectrograms using the Short-Time Fourier Transform (STFT). The 48 x 48 pixel resolution spectrogram images are then classified using a 4-block Convolutional Neural Network (CNN) architecture to detect four rhythm classes: Normal, Atrial Fibrillation (AFib), Ventricular Tachycardia (VTach), and Supraventricular Tachycardia (SVT). Test results show that the model achieves 100% accuracy, precision, recall, and F1-score on the test data. The novelty of this research lies in the integration of Confidence Score Thresholds (>90%, 70-90%, <70%) as a clinical decision support system for nurses. With minimal computational overhead, this system can operate in real time on standard devices, supporting accelerated medical response without requiring extensive hardware infrastructure.
đ Keywords
#Arrhythmia; CNN; AI; Spectrogram; Aritmia; CNN; AI; Spektrogram
âšī¸ Informasi Publikasi
Tanggal Publikasi
07 June 2026
Volume / Nomor / Tahun
Volume 7, Nomor 1, Tahun 2026
đ HOW TO CITE
Setiyanto, Koko; Marwanto, Arief; Setiyanto, Koko; Marwanto, Arief, "Analysis of Cardiac Arrhythmia Detection Using Spectrograms and Convolutional Neural Networks (CNN) for Nursing Care," MEDIKA TRADA : Jurnal Teknik Elektomedik Polbitrada, vol. 7, no. 1, Jun. 2026.
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