TY - GEN
T1 - Extremely Random Forest based Automatic Tonic-Clonic Seizure Detection using Spectral Analysis on Electroencephalography Data
AU - Stewart, Craig
AU - Fung, Wai Keung
AU - Fough, Nazila
AU - Prabhu, Radhakrishna
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/8/7
Y1 - 2023/8/7
N2 - Machine learning proliferates society and has begun changing medicine. This report covers an investigation into how Extremely Random Forests combined with Fast Fourier Transform feature extraction performed on two-dimensional time-series Epileptic Seizure data from the Bonn/UCI dataset. It found that robust classification can take place with lower channel counts, achieving 99.81% recall, 98.8% precision and 99.35% accuracy, outperforming previous works carried into this scenario.
AB - Machine learning proliferates society and has begun changing medicine. This report covers an investigation into how Extremely Random Forests combined with Fast Fourier Transform feature extraction performed on two-dimensional time-series Epileptic Seizure data from the Bonn/UCI dataset. It found that robust classification can take place with lower channel counts, achieving 99.81% recall, 98.8% precision and 99.35% accuracy, outperforming previous works carried into this scenario.
KW - Fourier Transform
KW - electroencephalography
KW - epilepsy
KW - extremely random forest
UR - http://www.scopus.com/inward/record.url?scp=85168561419&partnerID=8YFLogxK
U2 - 10.1109/NEWCAS57931.2023.10198101
DO - 10.1109/NEWCAS57931.2023.10198101
M3 - Conference contribution
AN - SCOPUS:85168561419
T3 - 21st IEEE Interregional NEWCAS Conference, NEWCAS 2023 - Proceedings
BT - 21st IEEE Interregional NEWCAS Conference, NEWCAS 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE Interregional NEWCAS Conference, NEWCAS 2023
Y2 - 26 June 2023 through 28 June 2023
ER -