Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device
Abstract Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but traditional systems are limited by size, cost...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-12526-1 |
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| author | Masahiro Hata Takufumi Yanagisawa Yuki Miyazaki Hisaki Omori Atsuya Hirashima Yuta Nakagawa Mitsuhiro Eto Kenji Yoshiyama Hideki Kanemoto Byambadorj Nyamradnaa Shusuke Yoshimoto Kotaro Ezure Shun Takahashi Manabu Ikeda |
| author_facet | Masahiro Hata Takufumi Yanagisawa Yuki Miyazaki Hisaki Omori Atsuya Hirashima Yuta Nakagawa Mitsuhiro Eto Kenji Yoshiyama Hideki Kanemoto Byambadorj Nyamradnaa Shusuke Yoshimoto Kotaro Ezure Shun Takahashi Manabu Ikeda |
| author_sort | Masahiro Hata |
| collection | DOAJ |
| description | Abstract Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but traditional systems are limited by size, cost, and the need for skilled technicians. This study proposes a deep-learning-based approach using data from a portable EEG device to distinguish healthy volunteers (HVs) from patients with dementia-related conditions. We analyzed EEG data from 233 participants, including 119 HVs and 114 patients, and transformed the signals into frequency-domain features using a short-time Fourier transform. A customized transformer-based model was trained and evaluated using 10-fold cross-validation and a holdout dataset. In the cross-validation, the model achieved an area under the curve (AUC) of 0.872 and a balanced accuracy (bACC) of 80.8% in distinguishing HVs from patients. Subgroup analyses were conducted for HVs versus patients stratified by dementia severity and by clinical diagnosis, yielding AUCs ranging from 0.812 to 0.898 and bACCs from 74.9 to 86.4%. Comparable results were obtained in the holdout dataset. These findings suggest that portable EEG data combined with deep learning may serve as a practical tool for the early detection and classification of dementia-related conditions. |
| format | Article |
| id | doaj-art-9cf6d4d97a264ea69194db42f5dec4bb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9cf6d4d97a264ea69194db42f5dec4bb2025-08-20T03:45:59ZengNature PortfolioScientific Reports2045-23222025-07-0115111110.1038/s41598-025-12526-1Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography deviceMasahiro Hata0Takufumi Yanagisawa1Yuki Miyazaki2Hisaki Omori3Atsuya Hirashima4Yuta Nakagawa5Mitsuhiro Eto6Kenji Yoshiyama7Hideki Kanemoto8Byambadorj Nyamradnaa9Shusuke Yoshimoto10Kotaro Ezure11Shun Takahashi12Manabu Ikeda13Department of Psychiatry, Osaka University Graduate School of MedicineInstitute for Advanced Co-creation Studies, Osaka UniversityDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicinePGV IncPGV IncPGV IncDepartment of Psychiatry, Osaka University Graduate School of MedicineDepartment of Psychiatry, Osaka University Graduate School of MedicineAbstract Mild cognitive impairment (MCI) and dementia pose significant health challenges in aging societies, emphasizing the need for accessible, cost-effective, and noninvasive diagnostic tools. Electroencephalography (EEG) is a promising biomarker, but traditional systems are limited by size, cost, and the need for skilled technicians. This study proposes a deep-learning-based approach using data from a portable EEG device to distinguish healthy volunteers (HVs) from patients with dementia-related conditions. We analyzed EEG data from 233 participants, including 119 HVs and 114 patients, and transformed the signals into frequency-domain features using a short-time Fourier transform. A customized transformer-based model was trained and evaluated using 10-fold cross-validation and a holdout dataset. In the cross-validation, the model achieved an area under the curve (AUC) of 0.872 and a balanced accuracy (bACC) of 80.8% in distinguishing HVs from patients. Subgroup analyses were conducted for HVs versus patients stratified by dementia severity and by clinical diagnosis, yielding AUCs ranging from 0.812 to 0.898 and bACCs from 74.9 to 86.4%. Comparable results were obtained in the holdout dataset. These findings suggest that portable EEG data combined with deep learning may serve as a practical tool for the early detection and classification of dementia-related conditions.https://doi.org/10.1038/s41598-025-12526-1DementiaMild cognitive impairmentAlzheimer’s diseaseDLBEEGDeep learning |
| spellingShingle | Masahiro Hata Takufumi Yanagisawa Yuki Miyazaki Hisaki Omori Atsuya Hirashima Yuta Nakagawa Mitsuhiro Eto Kenji Yoshiyama Hideki Kanemoto Byambadorj Nyamradnaa Shusuke Yoshimoto Kotaro Ezure Shun Takahashi Manabu Ikeda Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device Scientific Reports Dementia Mild cognitive impairment Alzheimer’s disease DLB EEG Deep learning |
| title | Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device |
| title_full | Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device |
| title_fullStr | Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device |
| title_full_unstemmed | Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device |
| title_short | Accurate deep-learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device |
| title_sort | accurate deep learning model to differentiate dementia severity and diagnosis using a portable electroencephalography device |
| topic | Dementia Mild cognitive impairment Alzheimer’s disease DLB EEG Deep learning |
| url | https://doi.org/10.1038/s41598-025-12526-1 |
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