Muhammad Zulfiqar Rafi (1), Muis Muhtadi (2), Nofri Ramadan (3), Rinakit Estu Waluyo (4)
Background: Infant epilepsy is a serious neurological condition that is difficult to recognize visually because seizure manifestations often resemble normal infant reflexes. Specific background: Advances in computer vision and deep learning have enabled image-based seizure detection as a non-invasive approach for early identification in infants. Knowledge gap: Comparative evaluations of different YOLO model generations under identical experimental settings for infant seizure detection remain limited. Aims: This study evaluates and compares the performance of YOLOv8n, YOLOv10n, and YOLOv11n in detecting seizure activity from infant image data. Results: Using a dataset of 645 images across seizure and neutral classes, YOLOv11n achieved the highest precision, recall, and mean average precision at stricter localization thresholds, while YOLOv10n demonstrated the fastest inference time. Novelty: This study provides a systematic cross-generation evaluation of YOLO nano variants for infant seizure detection using uniform training parameters. Implications: The findings support informed selection of YOLO architectures for non-invasive, image-based early seizure detection systems in clinical and real-time monitoring contexts.
• YOLOv11n shows the most stable detection performance for infant seizure images• YOLOv10n achieves the fastest inference time under identical training settings• Cross-version evaluation reveals clear trade-offs between detection stability and processing speed
YOLO, Epilepsy, Computer Vision, Object Detection, Infant Seizure Detection
S. M. Zuberi et al., “ILAE classification and definition of epilepsy syndromes with onset in neonates and infants: Position statement by the ILAE Task Force on Nosology and Definitions,” Epilepsia, vol. 63, pp. 1349–1397, 2022, doi: 10.1111/epi.17239.
GBD Epilepsy Collaborators, “Global, regional, and national burden of epilepsy, 1990–2021: A systematic analysis for the Global Burden of Disease Study 2021,” The Lancet Public Health, vol. 10, no. 3, pp. e203–e227, Mar. 2025, doi: 10.1016/S2468-2667(24)00302-5.
Badan Kebijakan Pembangunan Kesehatan, “Survey Kesehatan Indonesia,” Kementerian Kesehatan Republik Indonesia, 2023. [Online]. Available: https://www.badankebijakan.kemkes.go.id/hasil-ski-2023/.
F. Yenila, S. Wahyuni, E. Rianti, H. Marfalino, and D. Gusmita, “Sistem Pakar Deteksi Hemangioma pada Batita menggunakan Metode Hybrid,” Jurnal Informasi dan Teknologi, vol. 4, no. 4, pp. 265–270, Dec. 2022, doi: 10.37034/jidt.v4i4.250.
R. Ansyah et al., “Aplikasi Deteksi Penyakit Tuberculosis (TB) Pada Balita Menggunakan Metode Pengolahan Citra Matlab,” JEKIN – Jurnal Teknik Informatika, vol. 1, no. 3, 2021, doi: 10.58794/jekin.v1i3.354.
U. R. Acharya, A. Shoeibi, M. Khodatars, N. Ghassemi, M. Jafari, P. Moridian, R. Alizadehsani, M. Panahiazar, F. Khozeimeh, A. Zare, H. Hosseini-Nejad, A. Khosravi, A. F. Atiya, D. Aminshahidi, S. Hussain, M. Rouhani, and S. Nahavandi, “Epileptic seizures detection using deep learning techniques: A review,” International Journal of Environmental Research and Public Health, vol. 18, no. 11, Art. no. 5780, 2021, doi: 10.3390/ijerph18115780.
R. M. Pressler et al., “The ILAE classification of seizures and the epilepsies: Modification for seizures in the neonate. Position paper by the ILAE Task Force on Neonatal Seizures,” Epilepsia, vol. 62, no. 3, pp. 615–628, Mar. 2021, doi: 10.1111/epi.16815.
R. Varghese and S. M., “YOLOv8: A novel object detection algorithm with enhanced performance and robustness,” in Proc. 2024 Int. Conf. on Advances in Data Engineering and Intelligent Computing Systems (ADICS), 2024, pp. 1–6, doi: 10.1109/ADICS57850.2024.10533619.
N. Mehla, Ishita, R. Talukdar, and D. K. Sharma, “Object detection in autonomous maritime vehicles: Comparison between YOLO V8 and EfficientDet,” in Data Science and Network Engineering, Lecture Notes in Networks and Systems, vol. 726, S. Namasudra, M. C. Trivedi, R. G. Crespo, and P. Lorenz, Eds. Singapore: Springer Nature Singapore, 2023, pp. 125–141, doi: 10.1007/978-981-99-6755-1_10.
A. Ratu, L. Kusneti, T. Filikano, and A. G., “Implementasi dan Evaluasi Sistem Pencarian Informasi Ulasan Restoran India Menggunakan Algoritma VSM,” Journal of Informatics and Business, vol. 2, no. 4, pp. 529–538, Jan.–Mar. 2025. [Online]. Available: https://jurnal.ittc.web.id/index.php/jibs/article/view/2128/1914.
A. Salam, R. Y. Bakti, Lukman, and F. I. Rachman, “Penerapan Algoritma Mobilenet Single Shot Detector Untuk Deteksi Api dan Asap Berpotensi Kebakaran Pada Citra Hutan,” Arus Jurnal Sains dan Teknologi, vol. 2, no. 2, pp. 502–510, Oct. 2024, doi: 10.57250/ajst.v2i2.663.
N. J. Hayati, D. Singasatia, and M. R. Muttaqin, “Object tracking menggunakan algoritma You Only Look Once (YOLO)v8 untuk menghitung kendaraan,” Komputa: Jurnal Ilmiah Komputer dan Informatika, vol. 12, no. 2, pp. 91–99, Nov. 2023, doi: 10.34010/komputa.v12i2.10654.
H. B. Amor, S. Bouallegue, A. Bohli, and R. Bouallègue, “Development of a new dynamic approach for facial recognition and emotion detection,” in Proc. 2023 Int. Conf. on Software, Telecommunications and Computer Networks (SoftCOM), 2023, pp. 1–6, doi: 10.23919/SoftCOM58365.2023.10271655.
R. Khanam and M. Hussain, “YOLOv11: An overview of the key architectural enhancements,” arXiv, Art. no. 2410.17725, 2024. [Online]. Available: http://arxiv.org/abs/2410.17725.
E. Z. Kuang, K. R. Bhandari, and J. Gao, “Optimizing waste management with advanced object detection for garbage classification,” arXiv, 2024. [Online]. Available: https://arxiv.org/abs/2410.09975
.
B. D. Prasetya, E. P. Amalia, P. D. Juliana, and R. K. Niswatin, “Implementasi object detection berbasis web untuk klasifikasi jenis sampah menggunakan YOLO,” Seminar Nasional Teknologi & Sains, vol. 4, no. 1, pp. 60–65, Jan. 2025, doi: 10.29407/m7yxsp77.
N. Crasto, “Class imbalance in object detection: An experimental diagnosis and study of mitigation strategies,” arXiv, 2024. [Online]. Available: https://arxiv.org/abs/2403.07113.