TY - GEN
T1 - Development of 1D-CNN Methods for Classifying Pediatric Epilepsy Through EEG Signals
AU - Flores-Palermo, Oscar
AU - Espiritu-Cueva, Christian
AU - Ugarte, Willy
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Our work focuses on the development of a method that allows high-precision classification of ictal and preictal seizure activity in pediatric patients by analyzing EEG signals. In this study, different methods are analyzed using the CHB-MIT dataset, applying various preprocessing techniques and 1D-CNN model architectures. This paper compares two data acquisition methods identified during the experimentation process, namely training a 1D-CNN + LSTM model with single channel data (one channel per second) and multi-channel data (23 channels per second). The results showed that the multi-channel methodology outperformed its counterpart, achieving sensitivity, specificity, precision, accuracy and F1 score of 94.05%, 85.90%, 87.73%, 90.12% and 90.79%, respectively.
AB - Our work focuses on the development of a method that allows high-precision classification of ictal and preictal seizure activity in pediatric patients by analyzing EEG signals. In this study, different methods are analyzed using the CHB-MIT dataset, applying various preprocessing techniques and 1D-CNN model architectures. This paper compares two data acquisition methods identified during the experimentation process, namely training a 1D-CNN + LSTM model with single channel data (one channel per second) and multi-channel data (23 channels per second). The results showed that the multi-channel methodology outperformed its counterpart, achieving sensitivity, specificity, precision, accuracy and F1 score of 94.05%, 85.90%, 87.73%, 90.12% and 90.79%, respectively.
KW - 1D-CNN
KW - CHB-MIT
KW - Classification
KW - DWT
KW - EEG
KW - Ictal
KW - Preictal
UR - https://www.scopus.com/pages/publications/105006848828
U2 - 10.1007/978-3-031-91428-7_28
DO - 10.1007/978-3-031-91428-7_28
M3 - Contribución a la conferencia
AN - SCOPUS:105006848828
SN - 9783031914270
T3 - Communications in Computer and Information Science
SP - 401
EP - 415
BT - Information Management and Big Data - 11th Annual International Conference, SIMBig 2024, Proceedings
A2 - Lossio-Ventura, Juan Antonio
A2 - Ceh-Varela, Eduardo
A2 - Díaz, Eduardo
A2 - Paz Espinoza, Freddy
A2 - Tadonki, Claude
A2 - Alatrista-Salas, Hugo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th Annual International Conference on Information Management and Big Data, SIMBig 2024
Y2 - 20 November 2024 through 22 November 2024
ER -