Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study
We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on si...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/21/10/956 |
| _version_ | 1811301619893010432 |
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| author | Zelong Wang Majd Abazid Nesma Houmani Sonia Garcia-Salicetti Anne-Sophie Rigaud |
| author_facet | Zelong Wang Majd Abazid Nesma Houmani Sonia Garcia-Salicetti Anne-Sophie Rigaud |
| author_sort | Zelong Wang |
| collection | DOAJ |
| description | We aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection. |
| first_indexed | 2024-04-13T07:12:04Z |
| format | Article |
| id | doaj.art-28265f0e738c430aa1e891377aa3e370 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-13T07:12:04Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-28265f0e738c430aa1e891377aa3e3702022-12-22T02:56:50ZengMDPI AGEntropy1099-43002019-09-01211095610.3390/e21100956e21100956Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility StudyZelong Wang0Majd Abazid1Nesma Houmani2Sonia Garcia-Salicetti3Anne-Sophie Rigaud4Electronics and Physics Department, Telecom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 Evry, FranceElectronics and Physics Department, Telecom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 Evry, FranceElectronics and Physics Department, Telecom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 Evry, FranceElectronics and Physics Department, Telecom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011 Evry, FranceAP-HP, Groupe Hospitalier Cochin Paris Centre, Hôpital Broca, Pôle Gérontologie, 75013 Paris, FranceWe aimed to explore the online signature modality for characterizing early-stage Alzheimer’s disease (AD). A few studies have explored this modality, whereas many on online handwriting have been published. We focused on the analysis of raw temporal functions acquired by the digitizer on signatures produced during a simulated check-filling task. Sample entropy was exploited to measure the information content in raw time sequences. We show that signatures of early-stage AD patients have lower information content than those of healthy persons, especially in the time sequences of pen pressure and pen altitude angle with respect to the tablet. The combination of entropy values on two signatures for each person was classified with two linear classifiers often used in the literature: support vector machine and linear discriminant analysis. The improvements in sensitivity and specificity were significant with respect to the a priori group probabilities in our population of AD patients and healthy subjects. We show that altitude angle, when combined with pen pressure, conveys crucial information on the wrist-hand-finger system during signature production for pathology detection.https://www.mdpi.com/1099-4300/21/10/956online signature analysissample entropyalzheimer’s diseaseclassification |
| spellingShingle | Zelong Wang Majd Abazid Nesma Houmani Sonia Garcia-Salicetti Anne-Sophie Rigaud Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study Entropy online signature analysis sample entropy alzheimer’s disease classification |
| title | Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study |
| title_full | Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study |
| title_fullStr | Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study |
| title_full_unstemmed | Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study |
| title_short | Online Signature Analysis for Characterizing Early Stage Alzheimer’s Disease: A Feasibility Study |
| title_sort | online signature analysis for characterizing early stage alzheimer s disease a feasibility study |
| topic | online signature analysis sample entropy alzheimer’s disease classification |
| url | https://www.mdpi.com/1099-4300/21/10/956 |
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