Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network
Building upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9999448/ |
| _version_ | 1797956394059563008 |
|---|---|
| author | Quang Dao Mounim A. El-Yacoubi Anne-Sophie Rigaud |
| author_facet | Quang Dao Mounim A. El-Yacoubi Anne-Sophie Rigaud |
| author_sort | Quang Dao |
| collection | DOAJ |
| description | Building upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the tasks of classification of neuro-degenerative diseases from behavioral data, we investigate several data augmentation techniques. In this respect, compared to the traditional data augmentation techniques proposed for HW-based Parkinson detection, we investigate a variant of Generative Adversarial Networks (GANs), DoppelGANger, especially tailored for times series and hence suitable for synthesizing realistic online handwriting sequences. Based on a 1D-Convolutional Neural Network (1D-CNN) to perform Alzheimer classification, we show, on a real dataset related to HW and Alzheimer, that our DoppelGANger-based augmentation model allow the CNN to significantly outperform both the current state of the art and the other data augmentation techniques. |
| first_indexed | 2024-04-10T23:48:19Z |
| format | Article |
| id | doaj.art-a4994a2a48fa4902ad2902d7b8819ea3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T23:48:19Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a4994a2a48fa4902ad2902d7b8819ea32023-01-11T00:00:29ZengIEEEIEEE Access2169-35362023-01-01112148215510.1109/ACCESS.2022.32323969999448Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural NetworkQuang Dao0https://orcid.org/0000-0002-5684-9658Mounim A. El-Yacoubi1https://orcid.org/0000-0002-7383-0588Anne-Sophie Rigaud2Samovar/Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, FranceSamovar/Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, FranceAP-HP, Groupe Hospitalier Cochin Paris Centre, Hôpital Broca, Pôle Gérontologie, Paris, FranceBuilding upon the recent advances and successes in the application of deep learning to the medical field, we propose in this work a new approach to detect and classify early-stage Alzheimer patients using online handwriting (HW) loop patterns. To cope with the lack of training data prevalent in the tasks of classification of neuro-degenerative diseases from behavioral data, we investigate several data augmentation techniques. In this respect, compared to the traditional data augmentation techniques proposed for HW-based Parkinson detection, we investigate a variant of Generative Adversarial Networks (GANs), DoppelGANger, especially tailored for times series and hence suitable for synthesizing realistic online handwriting sequences. Based on a 1D-Convolutional Neural Network (1D-CNN) to perform Alzheimer classification, we show, on a real dataset related to HW and Alzheimer, that our DoppelGANger-based augmentation model allow the CNN to significantly outperform both the current state of the art and the other data augmentation techniques.https://ieeexplore.ieee.org/document/9999448/Alzheimer diseaseDoppelGANgeronline handwriting1D-convolution neural networks |
| spellingShingle | Quang Dao Mounim A. El-Yacoubi Anne-Sophie Rigaud Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network IEEE Access Alzheimer disease DoppelGANger online handwriting 1D-convolution neural networks |
| title | Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network |
| title_full | Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network |
| title_fullStr | Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network |
| title_full_unstemmed | Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network |
| title_short | Detection of Alzheimer Disease on Online Handwriting Using 1D Convolutional Neural Network |
| title_sort | detection of alzheimer disease on online handwriting using 1d convolutional neural network |
| topic | Alzheimer disease DoppelGANger online handwriting 1D-convolution neural networks |
| url | https://ieeexplore.ieee.org/document/9999448/ |
| work_keys_str_mv | AT quangdao detectionofalzheimerdiseaseononlinehandwritingusing1dconvolutionalneuralnetwork AT mounimaelyacoubi detectionofalzheimerdiseaseononlinehandwritingusing1dconvolutionalneuralnetwork AT annesophierigaud detectionofalzheimerdiseaseononlinehandwritingusing1dconvolutionalneuralnetwork |