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Arief Marwanto
Universitas Sultan Agung Semarang
Chemical Engineering, Chemistry & Bioengineering Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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ANALISIS DETEKSI ARITMIA JANTUNG BERBASIS SPEKTROGRAM DAN CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK ASUHAN KEPERAWATAN Koko Setiyanto; Arief Marwanto
MEDIKA TRADA : Jurnal Teknik Elektomedik Polbitrada Vol 7 No 1 (2026): MEDIKA TRADA: Jurnal Teknik Elektromedik Polbitrada Vol 7 No.1 (2026)
Publisher : LPPM POLBITRADA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59485/jtemp.v7i1.190

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.
Co-Authors Koko Setiyanto