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Discrimination of Parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal
Abstract
A new technique for discrimination of Parkinson tremor from essential tremor is presented in this paper. This technique is based on Statistical Signal Characterization (SSC) of the spectrum of the accelerometer signal. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the classification factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. Three of twelve newly derived SSC parameters show good discrimination results. Specific results of those three parameters on training data and test data are shown in detail. A linear combination of the effects of those parameters on the discrimination results is also included. A total discrimination accuracy of 90% is obtained.
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Differentiation between PD and ET is more difficult at early stages, when specific treatment would be particularly important.
“Particularly, the differentiation between the two diseases is more difficult at early stages, when however, a specific treatment would be particularly important [3].”
From Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer (2017) · cites this paper as [3]
Although there is overlapping in the range of frequencies that PD and ET tremors exhibit, power spectrum analysis of accelerometer signals has proven to discriminate effectively between PD and ET.
“although there is overlapping in the range of frequencies that PD and ET tremors exhibit, power spectrum analysis of accelerometer signals has proven to discriminate effectively between PD and ET [3,11–14].”
From Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer (2017) · cites this paper as [3]
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Barrantes, S., Sánchez Egea, A. J., González Rojas, H. A., Martí, M. J., Compta, Y., Valldeoriola, F., Simo Mezquita, E., Tolosa, E., & Valls-Solè, J. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183843 · Cited by 106
Combined accelerometer and EMG analysis to differentiate essential tremor from Parkinson's disease
Ghassemi, N. H., Marxreiter, F., Pasluosta, C. F., Kugler, P., Schlachetzki, J., Schramm, A., Eskofier, B. M., & Klucken, J. (2016) · 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7590791 · Cited by 39
Molparia, B., Schrader, B. N., Cohen, E., Wagner, J. L., Gupta, S. R., Gould, S., Hwynn, N., Spencer, E. G., & Torkamani, A. (2018) · PeerJ · doi:10.7717/peerj.5308 · Cited by 15
Metadata
- Authors
- Hossen, A., Muthuraman, M., Al-Hakim, Z., Raethjen, J., Deuschl, G., & Heute, U.
- Affiliations
- Sultan Qaboos University (OM)
- Christian-Albrechts-Universität zu Kiel (DE)
- Venue
- Bio-Medical Materials and Engineering
- Publisher
- SAGE Publications
- Published
- 2013
- Volume
- 23
- Issue
- 6
- Pages
- 513-531
- ISSN
- 0959-2989 (ISSN-L)
- OpenAlex
- W344410928
- Cited by
- 40
- References
- 0
- Citations by year
- 2024: 6 · 2023: 2 · 2022: 5 · 2021: 7 · 2020: 2 · 2019: 1 · 2018: 6 · 2017: 4 · 2016: 1 · 2015: 4 · 2014: 2
- FWCI
- 1.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bio-Medical Materials and Engineering (publishedVersion)
- PubMed (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Clinical Neurology › Deep Brain Stimulation and Movement Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- parkinsonian tremor, essential tremor, accelerometer signals, spectral analysis, Parkinson's disease, tremor classification
- MeSH terms
- Adult, Aged, Aged, 80 and over, Female, Humans, Male, Middle Aged, Parkinson Disease, ROC Curve, Signal Processing, Computer-Assisted, Essential Tremor
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