التفاصيل البيبلوغرافية
| العنوان: |
A Novel Feature from Instrumented Utensils for Clinical Assessment of Friedreich Ataxia. |
| المؤلفون: |
Abeysekara LL, Kolambahewage C, Pathirana PN, Horne M, Szmulewicz DJ, Corben LA |
| المصدر: |
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2023 Jul; Vol. 2023, pp. 1-4. |
| نوع المنشور: |
Journal Article; Research Support, Non-U.S. Gov't |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: [IEEE] Country of Publication: United States NLM ID: 101763872 Publication Model: Print Cited Medium: Internet ISSN: 2694-0604 (Electronic) Linking ISSN: 23757477 NLM ISO Abbreviation: Annu Int Conf IEEE Eng Med Biol Soc Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: [Piscataway, NJ] : [IEEE], [2007]- |
| مواضيع طبية MeSH: |
Friedreich Ataxia*/diagnosis, Humans ; Cerebellum ; Movement ; Case-Control Studies |
| مستخلص: |
Friedreich Ataxia (FRDA) is an inherited disorder that affects the cerebellum and other regions of the human nervous system. It causes impaired movement that affects quality and reduces lifespan. Clinical assessment of movement is a key part of diagnosis and assessment of severity. Recent studies have examined instrumented measurement of movement to support clinical assessments. This paper presents a frequency domain approach based on Average Band Power (ABP) estimation for clinical assessment using Inertial Measurement Unit (IMU) signals. The IMUs were attached to a 3D printed spoon and a cup. Participants used them to mimic eating and drinking activities during data collection. For both activities, the ABP of frequency components from individuals with FRDA clustered in 0 to 0.2Hz band. This suggests that the ABP of this frequency is affected by FRDA irrespective of the device or activity. The ABP in this frequency band was used to distinguish between FRDA and non-ataxic participants using the Area Under the Receiver-Operating-Characteristic Curve (AUC) which produced peak values greater than 0.8. The machine learning models (logistic regression and neural networks) produced accuracy greater than 80% with these features common to both devices. |
| تواريخ الأحداث: |
Date Created: 20231212 Date Completed: 20231216 Latest Revision: 20231220 |
| رمز التحديث: |
20231221 |
| DOI: |
10.1109/EMBC40787.2023.10340519 |
| PMID: |
38083604 |
| قاعدة البيانات: |
MEDLINE |
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