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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Technologia: Jurnal Ilmiah
Maria Angelina Cahyani Candrakasih
Universitas Amikom Purwokerto
Computer Science & IT

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Hybrid CNN for Sleep Stage Classification Based on EEG Maria Angelina Cahyani Candrakasih; Bagus Adhi Kusuma; Pungkas Subarkah
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12999

Abstract

Sleep stage classification is essential for diagnosing sleep disorders such as insomnia and sleep apnea. However, manual scoring of polysomnography (PSG) is time consuming and subjective. Automatic systems based on single channel EEG are promising for home based monitoring, but they face challenges due to class imbalance and inter subject variability. This study proposes a hybrid model that combines 15 handcrafted features (statistical and spectral) with 128 dimensional features extracted by a one dimensional Convolutional Neural Network (1D CNN), followed by a stacking ensemble (Random Forest and Support Vector Machine as base learners, Logistic Regression as meta learner). Using 40 subjects from the Sleep EDF Expanded dataset, a strict subject independent split (80% train / 20% test) was applied to avoid data leakage. The dataset contained 107,258 epochs with extreme imbalance (Wake 67.6%, N1 2.95%). After SMOTE oversampling on the training set, the model achieved an accuracy of 67.5%, macro F1 score of 31.4%, and Cohen’s Kappa of 0.34. An ablation study showed that CNN features alone (72.2% accuracy) outperformed handcrafted features (70.4%) and hybrid features (67.5%). The confusion matrix revealed that minority stages (especially N1, N3, REM) were poorly recognized. These results highlight that cross subject generalization remains a major challenge in EEG based sleep staging, and proper subject independent validation is critical to avoid overoptimistic claims.
PERBANDINGAN RANDOM FOREST DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI SLEEP APNEA Maria Angelina Cahyani Candrakasih; Dwi Krisbiantoro; Retno Waluyo
Technologia : Jurnal Ilmiah Vol 16 No 2 (2025): Technologia (April)
Publisher : Fakultas Teknologi Informasi, Universitas Islam Kalimantan Muhammad Arsyad Al Banjari, di bawah koordinasi UPT Publikasi dan Pengelolaan Jurnal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/tji.v16i2.18555

Abstract

Penelitian ini bertujuan untuk menilai performa algoritma Random Forest (RF) dan Support Vector Machine (SVM) dalam mendeteksi dan mengklasifikasikan sleep apnea dengan menggunakan dataset yang mencakup berbagai parameter fisiologis dan gaya hidup. Metode yang diterapkan mencakup pengumpulan data, analisis eksploratori, dan pra-pemrosesan data, termasuk penanganan ketidakseimbangan melalui teknik SMOTE. Dataset dibagi menjadi data pelatihan dan data uji untuk melatih model, yang kemudian dievaluasi dengan berbagai metrik seperti akurasi, precision, recall, F1-score, confusion matrix, ROC curve, AUC, dan validasi silang. Hasil penelitian menunjukkan bahwa algoritma Random Forest memberikan akurasi yang lebih tinggi, yaitu 0.927, dibandingkan dengan Support Vector Machine yang mencapai akurasi 0.915. Oleh karena itu, Random Forest dapat dianggap sebagai algoritma yang lebih unggul dalam klasifikasi sleep apnea berdasarkan dataset yang digunakan. Penelitian ini memberikan kontribusi pada pengembangan sistem deteksi sleep apnea berbasis machine learning yang dapat meningkatkan akurasi diagnosis penyakit tersebut. Rencana penelitian selanjutnya akan difokuskan pada pengembangan model yang lebih efisien dan penerapan teknik lain untuk mengatasi masalah dataset yang tidak seimbang.
Co-Authors Bagus Adhi Kusuma Dwi Krisbiantoro Pungkas Subarkah Retno Waluyo