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Automated Detection of PD Resting Tremor Using PSD with Recurrent Neural Network Classifier
Abstract
Diagnosis of Parkinson’s disease (PD) is a challenging problem for medical community. Typically characterized by tremor, PD occurs due to the loss of dopamine in the brain’s thalamic region that results in involuntary or oscillatory movement in the body. The early stage of the PD is referred as resting tremors, which appears when the muscles are relaxed. It is well known that surface EMG recording provides clinical information on the neuro-physiological characteristics of the tremors. This paper discusses the detection of resting tremors by extracting power spectral density (PSD) features from EMGs. Two methods namely, PSD by Welch and Burgs are applied by configuring the order of the predictors and are then classified using a recurrent neural network model, Elman Neural Network (REN). Experiments are performed using EMG patterns and statistical measures such as mean and maximum of PSD are used to classify the normal and abnormal PD subjects. It is found from the experimental results that the mean value of power spectral density by Burg with recurrent neural network classifier yields a classification accuracy of 95.6%. The proposed work need to be validated with larger datasets for real -time clinical application.
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Arvind et al. used an RNN to detect PD in rest tremors with an accuracy of 95.6%.
“Arvind et al.5 have used a recurrent neural network (RNN) to detect PD in rest tremors with an accuracy of 95.6%.”
From Explainable artificial intelligence for searching frequency characteristics in Parkinson's disease tremor (2023) · cites this paper as Arvind et al.5
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References 18 · Cited by 11
Research works printed but not linked: 13
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Metadata
- Authors
- Arvind, R., Karthik, B., Sriraam, N., & Kannan, J. K.
- Affiliations
- Sri Sivasubramaniya Nadar College of Engineering (IN)
- ORCID
- Sriraam, N.
- Venue
- 2010 International Conference on Advances in Recent Technologies in Communication and Computing
- Publisher
- IEEE
- Published
- 2010-10
- Volume
- 7
- Pages
- 414-417
- OpenAlex
- W2006668534
- Cited by
- 11
- References
- 18
- Citations by year
- 2023: 2 · 2021: 1 · 2019: 1 · 2015: 3 · 2014: 3 · 2013: 1
- FWCI
- 0.74
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Keywords
- Parkinson's disease, resting tremor, surface electromyography, power spectral density, recurrent neural networks, Elman neural network, tremor detection, early diagnosis
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