2025
doi: 10.12720/jait.16.9.1217-1225
Automated Sleep Apnea Detection Using CNNs: Insights into the Impact of FFT Feature Extraction on EEG Signals
Mera Kartika Delimayanti1,*
,
Asep Taufik Muharram1 ,
Ayres Pradiptyas1 ,
Rinaldito Ahmad Ryanari1 ,
Raditya Arya Prasetyo1 ,
Rizky Adi1 ,
Mohammad Reza Faisal2 ,
Rizqi Fitri Naryanto3 ,
Haralampos Hatzikirou4
- 1Computer and Informatics Engineering Department, Politeknik Negeri Jakarta, Depok, Indonesia
- 2Computer Science Department, Lambung Msangkurat University, Banjarbaru, Indonesia
- 3Mechanical Engineering Department, Engineering Faculty, Universitas Negeri Semarang, Semarang, Indonesia
- 4Mathematics Department, Khalifa University, Abu Dhabi, United Arab Emirates
- Manuscript receivedFebruary 1, 2025
- revisedMarch 26, 2025
- acceptedMay 8, 2025
- publishedSeptember 5, 2025
Abstract
Sleep apnea, a serious sleep disorder characterized by interrupted breathing during sleep, poses significant health risks such as cardiovascular disease and diabetes. Traditional diagnostic methods, such as polysomnography, are cumbersome and expensive, creating a demand for automated solutions. Previous sleep apnea detection research relied on Multi-Layer Perceptrons (MLPs), which, while functional, can be limited in capturing the complex temporal dependencies within Electroencephalogram (EEG) signals. Our study introduces a novel approach by combining Convolutional Neural Network (CNN) with Fast Fourier Transform (FFT) to detect sleep apnea. The FFT was implemented as feature extraction to capture frequency-domain characteristics of the EEG signals. Two versions of the model were developed: one trained on raw EEG data and the other on FFT-processed data. The Physionet Sleep-EDF Database served as the source for EEG recordings, labeled as “normal” or indicative of sleep-disordered breathing. Performance metrics, including accuracy, precision, recall, and F1-Score, were used to evaluate the models. The CNN trained on raw EEG data achieved superior results, with 92% accuracy, a precision of 0.86, recall of 1.00, and an F1-Score of 0.92, outperforming previous studies utilizing Multi-Layer Perceptrons (MLP). However, the result shows that the approach using FFT produces worse results. This suggests that, in the context of sleep apnea detection using our specific dataset, the most discriminative features may not reside solely in the frequency domain as extracted by FFT. The results demonstrate the potential of CNNs in developing low-cost, accessible diagnostic tools. Future efforts should address dataset limitations and explore alternative feature extraction methods to improve generalizability.
Keywords
- Convolutional Neural Network (CNN)
- deep learning
- Electroencephalogram (EEG)
- sleep disorder
- sleep apnea
How to Cite
Mera Kartika Delimayanti, Asep Taufik Muharram, Ayres Pradiptyas, Rinaldito Ahmad Ryanari, Raditya Arya Prasetyo, Rizky Adi, Mohammad Reza Faisal, Rizqi Fitri Naryanto, and Haralampos Hatzikirou, "Automated Sleep Apnea Detection Using CNNs: Insights into the Impact of FFT Feature Extraction on EEG Signals," Journal of Advances in Information Technology, vol. 16, no. 9, pp. 1217-1225, 2025. https://doi.org/10.12720/jait.16.9.1217-1225
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
