TY - GEN
T1 - Automated Classification of EEG patterns from BrainEEG Signals for Predicting Sleep Deficiency
AU - Sharma, Parth
AU - Mohapatra, Rajesh Kumar
AU - Patel, Dev
AU - Vyas, Ritesh
AU - Satapathy, Santosh
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026/1/1
Y1 - 2026/1/1
N2 - This research conducts a comprehensive analysis of machine learning techniques used for automatic sleep stage identification. By leveraging advanced algorithms such as XGBoost, SMOTEBoost, RUSBoost, and Bagging Classifier, the study aims to optimize classification accuracy. Experimental results show that XGBoost achieved the highest classification accuracy of 83.86%, followed closely by the Bagging Classifier at 83.78%. Meanwhile, RUSBoost and SMOTEBoost recorded lower accuracies of 74.10% and 72.53%, respectively. Through extensive testing and comparative evaluation, the study highlights the effectiveness, robustness, and sensitivity of these algorithms in accurately identifying sleep stages from biomedical data. The findings offer valuable insights into selecting suitable machine-learning techniques to improve sleep monitoring, diagnosis, and healthcare. Additionally, the study underscores the importance of algorithm selection, feature engineering, and model evaluation in achieving accurate and reliable sleep stage classification.
AB - This research conducts a comprehensive analysis of machine learning techniques used for automatic sleep stage identification. By leveraging advanced algorithms such as XGBoost, SMOTEBoost, RUSBoost, and Bagging Classifier, the study aims to optimize classification accuracy. Experimental results show that XGBoost achieved the highest classification accuracy of 83.86%, followed closely by the Bagging Classifier at 83.78%. Meanwhile, RUSBoost and SMOTEBoost recorded lower accuracies of 74.10% and 72.53%, respectively. Through extensive testing and comparative evaluation, the study highlights the effectiveness, robustness, and sensitivity of these algorithms in accurately identifying sleep stages from biomedical data. The findings offer valuable insights into selecting suitable machine-learning techniques to improve sleep monitoring, diagnosis, and healthcare. Additionally, the study underscores the importance of algorithm selection, feature engineering, and model evaluation in achieving accurate and reliable sleep stage classification.
KW - Electroencephalogram
KW - Feature Extraction
KW - Machine Learning
KW - Sleep Staging
UR - https://www.scopus.com/pages/publications/105022204373
U2 - 10.1007/978-3-032-05117-2_15
DO - 10.1007/978-3-032-05117-2_15
M3 - Conference contribution
AN - SCOPUS:105022204373
SN - 9783032051165
T3 - Lecture Notes in Networks and Systems
SP - 158
EP - 169
BT - Intelligent Systems - Proceedings of 5th International Conference on Machine Learning, IoT and Big Data, ICMIB 2025
A2 - Udgata, Siba K.
A2 - Mohapatra, Debasis
A2 - Sethi, Srinivas
A2 - Rana, Muhammad Ehsan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th International Conference on Machine Learning, IoT and Big Data, ICMIB 2025
Y2 - 4 April 2025 through 6 April 2025
ER -