Towards Feasible Deep Learning Approach Using EEG for Neurodegenerative Diseases
In this study, we propose the phase space reconstruction (PSR)-based deep learning framework for enhanced detection of frontotemporal dementia (FTD) utilizing electroen-cephalography (EEG). Our experimental results demonstrate significant improvements over conventional screening methods, particularl...
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| Published in: | The ... International Winter Conference on Brain-Computer Interface pp. 1 - 5 |
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| Format: | Conference Proceeding |
| Language: | English |
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24.02.2025
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| ISSN: | 2572-7672 |
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| Abstract | In this study, we propose the phase space reconstruction (PSR)-based deep learning framework for enhanced detection of frontotemporal dementia (FTD) utilizing electroen-cephalography (EEG). Our experimental results demonstrate significant improvements over conventional screening methods, particularly the mini-mental state examination (MMSE), which exhibits a considerable false-negative rate of 39.1 % in FTD screening. The proposed framework achieved a remarkable 88.89 % accuracy in identifying previously misclassified FTD cases. The evaluation was conducted using a public dataset comprising 23 FTD patients and 29 healthy controls, wherein our framework consistently outperformed both traditional MMSE and the EEGNet-based approach. Through the integration of PSR techniques with advanced deep learning architectures, our findings suggest that EEG-based computational approaches could serve as robust complementary diagnostic tools in clinical FTD assessment protocols. These results underscore the potential of machine learning applications in enhancing the accuracy and reliability of neurological disorder diagnosis. |
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| AbstractList | In this study, we propose the phase space reconstruction (PSR)-based deep learning framework for enhanced detection of frontotemporal dementia (FTD) utilizing electroen-cephalography (EEG). Our experimental results demonstrate significant improvements over conventional screening methods, particularly the mini-mental state examination (MMSE), which exhibits a considerable false-negative rate of 39.1 % in FTD screening. The proposed framework achieved a remarkable 88.89 % accuracy in identifying previously misclassified FTD cases. The evaluation was conducted using a public dataset comprising 23 FTD patients and 29 healthy controls, wherein our framework consistently outperformed both traditional MMSE and the EEGNet-based approach. Through the integration of PSR techniques with advanced deep learning architectures, our findings suggest that EEG-based computational approaches could serve as robust complementary diagnostic tools in clinical FTD assessment protocols. These results underscore the potential of machine learning applications in enhancing the accuracy and reliability of neurological disorder diagnosis. |
| Author | Kim, Ho-Jung Wont, Dong-Ok Park, Dogeun Ju, Young-Gi Kim, Keun-Tae |
| Author_xml | – sequence: 1 givenname: Dogeun surname: Park fullname: Park, Dogeun email: dogeun.park@hallym.ac.kr organization: Hallym University,Dept. Artificial Intelligence Convergence,Chuncheon,Republic of Korea – sequence: 2 givenname: Ho-Jung surname: Kim fullname: Kim, Ho-Jung email: hojungkim@hallym.ac.kr organization: Hallym University,Dept. Artificial Intelligence Convergence,Chuncheon,Republic of Korea – sequence: 3 givenname: Young-Gi surname: Ju fullname: Ju, Young-Gi email: younggi.ju@hallym.ac.kr organization: Hallym University,Dept. Artificial Intelligence Convergence,Chuncheon,Republic of Korea – sequence: 4 givenname: Keun-Tae surname: Kim fullname: Kim, Keun-Tae email: ktkim@hallym.ac.kr organization: Hallym University,Dept. Artificial Intelligence Convergence,Chuncheon,Republic of Korea – sequence: 5 givenname: Dong-Ok surname: Wont fullname: Wont, Dong-Ok email: dongok.won@hallym.ac.kr organization: Convergence and College of Medicine Hallym University,Dept. Artificial Intelligence,Chuncheon,Republic of Korea |
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| Snippet | In this study, we propose the phase space reconstruction (PSR)-based deep learning framework for enhanced detection of frontotemporal dementia (FTD) utilizing... |
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| SubjectTerms | Accuracy Brain-computer interfaces Deep learning Dementia EEG Electroencephalography false- negative frontotemporal dementia MMSE Neurological diseases phase-space reconstruction Protocols Reliability |
| Title | Towards Feasible Deep Learning Approach Using EEG for Neurodegenerative Diseases |
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