Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram
Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integra...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Neuroscience Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772528625000123 |
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| author | Janjhyam Venkata Naga Ramesh Aadam Quraishi Yassine Aoudni Mustafa Mudhafar Divya Nimma Monika Bansal |
| author_facet | Janjhyam Venkata Naga Ramesh Aadam Quraishi Yassine Aoudni Mustafa Mudhafar Divya Nimma Monika Bansal |
| author_sort | Janjhyam Venkata Naga Ramesh |
| collection | DOAJ |
| description | Non-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation. |
| format | Article |
| id | doaj-art-3b21cb095fbc46d78afc49ea471777dc |
| institution | OA Journals |
| issn | 2772-5286 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Neuroscience Informatics |
| spelling | doaj-art-3b21cb095fbc46d78afc49ea471777dc2025-08-20T02:26:20ZengElsevierNeuroscience Informatics2772-52862025-06-015210019710.1016/j.neuri.2025.100197Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogramJanjhyam Venkata Naga Ramesh0Aadam Quraishi1Yassine Aoudni2Mustafa Mudhafar3Divya Nimma4Monika Bansal5Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India; Corresponding author at: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, 522302, India.M.D. Research, Intervention Treatment Institute, Houston, Texas, USAComputer & Embedded Systems Laboratory National School of Engineers of Sfax (ENIS), University of Sfax, BP 1173, 3038, Sfax, TunisiaDepartment of Medical Physics, Faculty of Medical Applied Sciences, University of Kerbala, 56001, Karbala, Iraq; Department of Anesthesia Techniques and Intensive Care, Al-Taff university college, 56001, Kerbala, IraqData Analyst in UMMC, USADepartment of Computer Science, SSD Women's Institute of Technology, Bathinda, IndiaNon-invasive brain stimulation (NIBS) techniques, such as transcranial infrared (tNIR) stimulation, offer promising advancements in sleep monitoring and regulation. To enhance sleep stage classification without relying on traditional polysomnography (PSG) systems, we propose a novel approach integrating single-channel electrocardiogram (ECG) signals, heart rate variability (HRV) features, and tNIR stimulation. The maximal overlap discrete wavelet transform (MODWT) is applied for multi-resolution analysis of ECG signals, followed by peak information extraction. Based on the first-order deviation of peak positions, multi-dimensional HRV features are extracted. To identify HRV features strongly associated with different sleep stages, we introduce a feature selection method combining the ReliefF algorithm and Gini index. The selected features are then processed using the INFO-ABC Logit Boost method to establish correlations between HRV dynamics and sleep stages. Experimental results on publicly available datasets demonstrate that the proposed model achieves an overall accuracy of 83.67%, a precision of 82.59%, a Kappa coefficient of 77.94%, and an F1-score of 82.97%. Compared with conventional sleep staging methods, our approach enhances sleep quality assessment and facilitates real-time, non-invasive monitoring in home and mobile healthcare settings, leveraging the potential of tNIR-based NIBS for sleep modulation.http://www.sciencedirect.com/science/article/pii/S2772528625000123Non-invasive brain stimulationHealthcaretNIRHRVECG |
| spellingShingle | Janjhyam Venkata Naga Ramesh Aadam Quraishi Yassine Aoudni Mustafa Mudhafar Divya Nimma Monika Bansal Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram Neuroscience Informatics Non-invasive brain stimulation Healthcare tNIR HRV ECG |
| title | Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram |
| title_full | Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram |
| title_fullStr | Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram |
| title_full_unstemmed | Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram |
| title_short | Non-invasive brain stimulation-based sleep stage classification using transcranial infrared based electrocardiogram |
| title_sort | non invasive brain stimulation based sleep stage classification using transcranial infrared based electrocardiogram |
| topic | Non-invasive brain stimulation Healthcare tNIR HRV ECG |
| url | http://www.sciencedirect.com/science/article/pii/S2772528625000123 |
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