A study of auxiliary screening for Alzheimer's disease based on handwriting characteristics.
Background and objectives: Alzheimer's disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and norm...
| Publicado en: | Frontiers in Aging Neuroscience Vol. 15; pp. 1 - 13 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
3/15/2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162782579&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162782579 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 3/15/2023 vid: 15 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 162782579 162782579 162782579 10.3389/fnagi.2023.1117250 162782579 ppf: 1 ppct: 12 formats: tig: atl: A study of auxiliary screening for Alzheimer's disease based on handwriting characteristics. aug: au: Hengnian Qi Ruoyu Zhang Zhuqin Wei Chu Zhang Lina Wang Qing Lang Kai Zhang Xuesong Tian affil: Information Engineering Department, Huzhou University, Huzhou, China sug: subj: Alzheimer's Disease Diagnosis Handwriting Evaluation Kinematics Human Funding Source Male Female Aged T-Tests Mann-Whitney U Test Logistic Regression ROC Curve Descriptive Statistics Exploratory Research China Aged: 65+ years Male Female ab: Background and objectives: Alzheimer's disease (AD) has an insidious onset, the early stages are easily overlooked, and there are no reliable, rapid, and inexpensive ancillary detection methods. This study analyzes the differences in handwriting kinematic characteristics between AD patients and normal elderly people to model handwriting characteristics. The aim is to investigate whether handwriting analysis has a promising future in AD auxiliary screening or even auxiliary diagnosis and to provide a basis for developing a handwriting-based diagnostic tool. Materials and methods: Thirty-four AD patients (15 males, 77.15 ± 1.796 years) and 45 healthy controls (20 males, 74.78 ± 2.193 years) were recruited. Participants performed four writing tasks with digital dot-matrix pens which simultaneously captured their handwriting as they wrote. The writing tasks consisted of two graphics tasks and two textual tasks. The two graphics tasks are connecting fixed dots (task 1) and copying intersecting pentagons (task 2), and the two textual tasks are dictating three words (task 3) and copying a sentence (task 4). The data were analyzed by using Student's t-test and Mann-Whitney U test to obtain statistically significant handwriting characteristics. Moreover, seven classification algorithms, such as eXtreme Gradient Boosting (XGB) and Logistic Regression (LR) were used to build classification models. Finally, the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Area Under Curve (AUC) were used to assess whether writing scores and kinematics parameters are diagnostic. Results: Kinematic analysis showed statistically significant differences between the AD and controlled groups for most parameters (p < 0.05, p < 0.01). The results found that patients with AD showed slower writing speed, tremendous writing pressure, and poorer writing stability. We built statistically significant features into a classification model, among which the model built by XGB was the most effective with a maximum accuracy of 96.55%. The handwriting characteristics also achieved good diagnostic value in the ROC analysis. Task 2 had a better classification effect than task 1. ROC curve analysis showed that the best threshold value was 0.084, accuracy = 96.30%, sensitivity = 100%, specificity = 93.41%, PPV = 92.21%, NPV = 100%, and AUC = 0.991. Task 4 had a better classification effect than task 3. ROC curve analysis showed that the best threshold value was 0.597, accuracy = 96.55%, sensitivity = 94.20%, specificity = 98.37%, PPV = 97.81%, NPV = 95.63%, and AUC = 0.994. Conclusion: This study's results prove that handwriting characteristic analysis is promising in auxiliary AD screening or AD diagnosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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