Quantum machine learning for detection of sleep deprivation from EEG signals
- 1Maulana Abul Kalam Azad University of Technology
Abstract
Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adversely impacts cognitive performance, neural functioning, and overall health. Electroencephalography (EEG) offers an objective means of capturing neural alterations associated with sleep loss, making it well-suited for automated detection frameworks. In this study, we explore the application of a Quantum Support Vector Machine and Hybrid Quantum Neural Networks to classify sleep-deprived and well-rested states using resting-state EEG signals. A comprehensive feature extraction pipeline is employed, incorporating spectral band power, band ratios, Hjorth parameters, and functional connectivity measures. These features are subsequently encoded into quantum states to construct a quantum kernel, which is then utilized for classification. Model performance is evaluated under both epoch-level and subject-level data partitioning schemes. The Hybrid Quantum Neural Network (HQNN) achieves the highest performance across both evaluation settings, attaining an accuracy of 96.88% at the epoch level and 81.25% at the subject level. The Quantum Support Vector Machine (QSVM) model achieves accuracies of 93.75% and 75.00% for epoch-level and subject-level evaluations, respectively. At subject-level and epoch-level evaluation, HQNN outperforms previously reported results (95.72% and 68.23%). Overall, these findings highlight the potential of quantum machine learning as a competitive approach for EEG-based sleep deprivation detection, with promising implications for real-world biomedical applications.
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BibTeX
@misc{sarmasarkar2026quantum,
title = {{Quantum machine learning for detection of sleep deprivation from EEG signals}},
author = {P. Sarma-Sarkar and R. Saini and P. P. Roy},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.14.732153},
url = {https://www.biorxiv.org/content/10.64898/2026.06.14.732153v2},
note = {CC BY},
}
RIS
TY - UNPB
TI - Quantum machine learning for detection of sleep deprivation from EEG signals
AU - P. Sarma-Sarkar
AU - R. Saini
AU - P. P. Roy
PY - 2026
DA - 2026/06/14
PB - bioRxiv
DO - 10.64898/2026.06.14.732153
UR - https://www.biorxiv.org/content/10.64898/2026.06.14.732153v2
AB - Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adversely impacts cognitive performance, neural functioning, and overall health. Electroencephalography (EEG) offers an objective means of capturing neural alterations associated with sleep loss, making it well-suited for automated detection frameworks. In this study, we explore the application of a Quantum Support Vector Machine and Hybrid Quantum Neural Networks to classify sleep-deprived and well-rested states using resting-state EEG signals. A comprehensive feature extraction pipeline is employed, incorporating spectral band power, band ratios, Hjorth parameters, and functional connectivity measures. These features are subsequently encoded into quantum states to construct a quantum kernel, which is then utilized for classification. Model performance is evaluated under both epoch-level and subject-level data partitioning schemes. The Hybrid Quantum Neural Network (HQNN) achieves the highest performance across both evaluation settings, attaining an accuracy of 96.88% at the epoch level and 81.25% at the subject level. The Quantum Support Vector Machine (QSVM) model achieves accuracies of 93.75% and 75.00% for epoch-level and subject-level evaluations, respectively. At subject-level and epoch-level evaluation, HQNN outperforms previously reported results (95.72% and 68.23%). Overall, these findings highlight the potential of quantum machine learning as a competitive approach for EEG-based sleep deprivation detection, with promising implications for real-world biomedical applications.
ER -