TY - GEN
T1 - Ictal Periods Detection in Photoplethysmographic and Electrodermal Signals
AU - Ramirez-Peralta, Maria Fernanda
AU - Romo-Fuentes, Maria Fernanda
AU - Tovar-Corona, Blanca
AU - Silva-Ramirez, Martin Arturo
AU - Garay-Jimenez, Laura Ivoone
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The occurrence of an epileptic crisis can generate changes in the autonomous nervous system, given the relation between the zones in which an epileptic crisis generates and propagates, and the zones of the brain that control the involuntary responses of the body. Thus, an investigation aiming at identifying ictal periods in autonomous nervous system-controlled signals that can be, also, continuously recorded through a wearable device is proposed. Two signals are considered, electrodermal activity, obtained from the measurement of the galvanic skin response, and heart rate variability, derived from the analysis of the inter beat interval computed from the photoplethysmographic signal. A database of 11 subjects, composed by these two signals recorded simultaneously with electroencephalography is employed. Time and frequency-domain features were extracted from the electrodermal and heart rate variability signals by taking 4-minute segments previous to the beginning of a seizure as interictal, and 4-minute segments after as ictal, for training, and through a 4-minute windows with a 30-second slide segmentation for testing, while the electroencephalographic signal was taken as reference to obtain the tags for the ictal and interictal periods. The features were classified using a perceptron multilayer neural network trained with scaled conjugate gradient backpropagation algorithm, obtaining the following results, accuracy: 25.15%, recall: 97.49%, specificity: 0.44% precision: 24.86%, and F2 score: 60.12%.
AB - The occurrence of an epileptic crisis can generate changes in the autonomous nervous system, given the relation between the zones in which an epileptic crisis generates and propagates, and the zones of the brain that control the involuntary responses of the body. Thus, an investigation aiming at identifying ictal periods in autonomous nervous system-controlled signals that can be, also, continuously recorded through a wearable device is proposed. Two signals are considered, electrodermal activity, obtained from the measurement of the galvanic skin response, and heart rate variability, derived from the analysis of the inter beat interval computed from the photoplethysmographic signal. A database of 11 subjects, composed by these two signals recorded simultaneously with electroencephalography is employed. Time and frequency-domain features were extracted from the electrodermal and heart rate variability signals by taking 4-minute segments previous to the beginning of a seizure as interictal, and 4-minute segments after as ictal, for training, and through a 4-minute windows with a 30-second slide segmentation for testing, while the electroencephalographic signal was taken as reference to obtain the tags for the ictal and interictal periods. The features were classified using a perceptron multilayer neural network trained with scaled conjugate gradient backpropagation algorithm, obtaining the following results, accuracy: 25.15%, recall: 97.49%, specificity: 0.44% precision: 24.86%, and F2 score: 60.12%.
KW - electrodermal activity
KW - epilepsy
KW - heart rate variability
KW - ictal period detection
KW - wearables
UR - http://www.scopus.com/inward/record.url?scp=85123826539&partnerID=8YFLogxK
U2 - 10.1109/CCE53527.2021.9633091
DO - 10.1109/CCE53527.2021.9633091
M3 - Contribución a la conferencia
AN - SCOPUS:85123826539
T3 - CCE 2021 - 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control
BT - CCE 2021 - 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2021
Y2 - 10 November 2021 through 12 November 2021
ER -