Among movement disorders, essential tremor is by far the most common, as much as eight times more prevalent than Parkinson’s disease. Although these two conditions differ in their presentation and course, clinicians do not always recognize them, leading to common misdiagnoses. Proper and early diagnosis is important for receiving the right treatment and support. In this paper, the development of a portable and reliable tremor classification system based on a wearable device, enabling clinicians to differentiate between essential tremor and Parkinson’s disease-associated one, is reported. Inertial data were collected from subjects with a well-established diagnosis of tremor, and analyzed to extract different sets of relevant spectral features. Supervised learning methods were then applied to build several classification models, among which the best ones achieved an average accuracy above 90%. Results encourage the use of wearable technology as effective and affordable tools to support clinicians.

Locatelli, P., Alimonti, D., Traversi, G., Re, V. (2020). Classification of essential tremor and parkinson’s tremor based on a low-power wearable device. ELECTRONICS, 9(10), 1-18 [10.3390/electronics9101695].

Classification of essential tremor and parkinson’s tremor based on a low-power wearable device

Patrick Locatelli;Dario Alimonti;
2020

Abstract

Among movement disorders, essential tremor is by far the most common, as much as eight times more prevalent than Parkinson’s disease. Although these two conditions differ in their presentation and course, clinicians do not always recognize them, leading to common misdiagnoses. Proper and early diagnosis is important for receiving the right treatment and support. In this paper, the development of a portable and reliable tremor classification system based on a wearable device, enabling clinicians to differentiate between essential tremor and Parkinson’s disease-associated one, is reported. Inertial data were collected from subjects with a well-established diagnosis of tremor, and analyzed to extract different sets of relevant spectral features. Supervised learning methods were then applied to build several classification models, among which the best ones achieved an average accuracy above 90%. Results encourage the use of wearable technology as effective and affordable tools to support clinicians.
Articolo in rivista - Articolo scientifico
Essential tremor; Movement disorders; Parkinsonian tremor; Parkinson’s disease; Wearable device;
English
2020
9
10
1
18
1695
none
Locatelli, P., Alimonti, D., Traversi, G., Re, V. (2020). Classification of essential tremor and parkinson’s tremor based on a low-power wearable device. ELECTRONICS, 9(10), 1-18 [10.3390/electronics9101695].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/394593
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