| Autor: |
Rigas G; Department of Material Sciences and Engineering, University of Ioannina, Ioannina, Greece. rigas@cs.uoi.gr, Tzallas AT, Tsipouras MG, Bougia P, Tripoliti EE, Baga D, Fotiadis DI, Tsouli SG, Konitsiotis S |
| Jazyk: |
angličtina |
| Zdroj: |
IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society [IEEE Trans Inf Technol Biomed] 2012 May; Vol. 16 (3), pp. 478-87. Date of Electronic Publication: 2012 Jan 02. |
| DOI: |
10.1109/TITB.2011.2182616 |
| Abstrakt: |
Tremor is the most common motor disorder of Parkinson's disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patient's body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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