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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal INFOTEL Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Lailatul Qadri Zakaria
CAIT Research Group, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Health Professions Materials Science & Nanotechnology

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Implementation of Discrete Wavelet Transform and Xception for ECG Image Classification of Arrhythmic Heart Disease Patients Irhamsyah, Muhammad; Melinda, Melinda; Yunidar, Yunidar; Muttaqin, Ikram; Zakaria, Lailatul Qadri
JURNAL INFOTEL Vol 17 No 2 (2025): May
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i2.1341

Abstract

The electrocardiogram (ECG) is one of the most important methods in the process of diagnosing heart disease. Visualizes the voltage and time relationship of the electrical activity of the heart. Cardiovascular or heart disease can be classified into several types, one of which is arrhythmia, a condition that involves changes in heartbeat rhythm, either too fast or too slow at rest. This study aims to develop a cardiac arrhythmia classification model using Deep Wavelet Transform (DWT) and Xception. It was evaluated on 2,200 spectrogram samples from the MIT-BIH dataset, containing normal and arrhythmia classes. The process compared epochs 30, 50, and 100 with learning rates of 0.001 and 0.0001 using cross-validation. Data were converted into spectrogram images for classification with Xception. The highest accuracy, 99.79%, was achieved at epoch 100 with a 0.0001 learning rate. Then, the highest precision occurs when the epoch is 50 with a learning rate of 0.001 and 0.0001, which is 100%. Lastly, Xception performed very well in the ECG image classification. This advantage demonstrates the ability of the model to recognize complex patterns in ECG data more effectively, increasing the reliability of arrhythmia detection. In addition, using DWT as a feature extraction technique allows better signal processing,which contributes to optimal results.
EEG Performance Signal Analysis for Diagnosing Autism Spectrum Disorder using Butterworth and Empirical Mode Decomposition Fathur Rahman, Imam; Melinda, Melinda; Irhamsyah, Muhammad; Yunidar, Yunidar; Nurdin, Yudha; Wong, W.K.; Zakaria, Lailatul Qadri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 3 (2025): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i3.788

Abstract

Electroencephalography (EEG) is a technique used to measure electrical activity in the brain by placing electrodes on the scalp. EEG plays an essential role in analyzing a variety of neurological conditions, including autism spectrum disorder (ASD). However, in the recording process, EEG signals are often contaminated by noise, hindering further analysis. Therefore, an effective signal processing method is needed to improve the data quality before feature extraction is performed. This study applied the Butterworth Band-Pass Filter (BPF) as a preprocessing method to reduce noise in EEG signals and then used the Empirical Mode Decomposition (EMD) method to extract relevant features. The performance of this method was evaluated using three main parameters, namely Mean Square Error (MSE), Mean Absolute Error (MAE), and Signal-to-Noise Ratio (SNR). The results showed that EMD was able to retain important information in EEG signals better than signals that only passed through the BPF filtration stage. EMD produces lower MAE and MSE values than Butterworth, suggesting that this method is more accurate in maintaining the original shape of the signal. In subject 3, EMD recorded the lowest MAE of 0.622 compared to Butterworth, which reached 20.0, and the MSE value of 0.655 compared to 771.5 for Butterworth. In addition, EMD also produced a higher SNR, with the highest value of 23,208 in subject 5, compared to Butterworth, which reached only 1,568. These results prove that the combination of BPF as a preprocessing method and EMD as a feature extraction method is more effective in maintaining EEG signal quality and improving analysis accuracy compared to the use of the Butterworth Band-Pass Filter alone.
Implementation of Vision Transformer for Early Detection of Autism Based on EEG Signal Heatmap Visualization Rafiki, Aufa; Melinda, Melinda; Oktiana, Maulisa; Dewi Meutia, Ernita; Afnan, Afnan; Mulyadi, Mulyadi; Zakaria, Lailatul Qadri
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/40n05b64

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social interaction, communication, and repetitive behavioral patterns. Early detection of ASD is crucial for improving the quality of life of affected individuals and alleviating the burden on their families. This study proposes a computer-aided diagnostic system for ASD by applying a pre-trained Vision Transformer (ViT-B/16) architecture to EEG signal data obtained from King Abdul Aziz University. The dataset comprises EEG recordings from 16 subjects (8 normal and 8 ASD) that have undergone preprocessing—including filtering using the Discrete Wavelet Transform (DWT), segmentation (windowing), and conversion into heatmap representations—and were subsequently partitioned into training, validation, and testing subsets. The ViT model was trained for 100 epochs with a batch size of 16, using the AdamW optimizer and the CrossEntropy loss function, while two learning rate configurations (0.0001 and 0.00001) were evaluated; the best-performing weights were selected based on the lowest validation loss. Test results indicate that the model trained with a learning rate of 0.00001 achieved a testing accuracy of 99.53%, accompanied by excellent precision, specificity, recall, and f1-score, thereby demonstrating strong generalization capabilities and minimal overfitting. Future research is recommended to incorporate locally sourced datasets and to further customize the ViT architecture through comprehensive hyperparameter tuning, with the aim of developing a mobile application to support clinical ASD diagnosis.
Co-Authors Afnan, Afnan Dewi Meutia, Ernita Fathur Rahman, Imam Melinda Melinda Muhammad Irhamsyah Mulyadi Mulyadi Muttaqin, Ikram Oktiana, Maulisa Rafiki, Aufa Wong, W.K. Yudha Nurdin Yunidar