Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact

Autor: Franco Giannini, Antonio Pisani, Mariachiara Ricci, Giovanni Saggio, Giulia Di Lazzaro, Nicola Biagio Mercuri
Rok vydání: 2020
Předmět:
Adult
Male
medicine.medical_specialty
Parkinson's disease
Movement
0206 medical engineering
Wearable computer
Health Informatics
02 engineering and technology
Newly diagnosed
Settore ING-INF/01 - Elettronica
Wearable Electronic Devices
03 medical and health sciences
wearable technology
0302 clinical medicine
Physical medicine and rehabilitation
Atmospheric measurements
Health Information Management
Accelerometry
Humans
Medicine
Electrical and Electronic Engineering
signal processing
Wearable technology
Aged
Monitoring
Physiologic
Aged
80 and over
business.industry
motion analysis
Healthy subjects
Novelty
Parkinson Disease
Signal Processing
Computer-Assisted
Motor impairment
Equipment Design
Middle Aged
IMU
machine learning
medicine.disease
020601 biomedical engineering
Computer Science Applications
Female
business
030217 neurology & neurosurgery
Zdroj: IEEE Journal of Biomedical and Health Informatics. 24:120-130
ISSN: 2168-2208
2168-2194
DOI: 10.1109/jbhi.2019.2903627
Popis: Objective: The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo , patients. Methods: A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects. Results: Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions. Conclusion: Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients. Significance: This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.
Databáze: OpenAIRE
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