Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms
Open accessAbstract
Due to overlapping tremor features, the medical diagnosis of Parkinson’s disease (PD) and essential tremor (ET) mainly relies on the clinical experience of doctors, which often leads to misdiagnosis. Seven predictive models using machine learning algorithms including random forest (RF), eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), ridge classification (Ridge), backpropagation neural network (BP), and convolutional neural network (CNN) were evaluated and compared aiming to better differentiate between PD and ET by using accessible demographics and tremor information of the upper limbs. The tremor information including tremor acceleration and surface electromyogram (sEMG) signals were collected from 398 patients (PD = 257, ET = 141) and then were used to train the established models to separate PD and ET. The performance of the models was evaluated by indices of accuracy and area under the curve (AUC), which indicated the ensemble learning models including RF and XGBoost showed the best overall predictive ability with accuracy above 0.84 and AUC above 0.90. Furthermore, the relative importance of sex, age, four postures, and five tremor features was analyzed and ranked showing that the dominant frequency of sEMG of flexors, the average amplitude of sEMG of flexors, resting posture, and winging posture had a greater impact on the diagnosis of PD, whereas sex and age were less important. These results provide a reference for the intelligent diagnosis of PD and show promise for use in wearable tremor suppression devices.
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Xing et al. reached 78% accuracy differentiating PD and ET using a CNN.
“Xing et al.4 did minimal data preprocessing to create inputs to train the convolutional neural network (CNN) for differentiating between PD and ET and has reached 78% accuracy.”
From Explainable artificial intelligence for searching frequency characteristics in Parkinson's disease tremor (2023) · cites this paper as Xing et al.4
The Results (Validation of the Training Set) report that the CNN reached 0.78 accuracy when distinguishing PD from ET. The Methods (Establishment of Models) describe selecting, down-sampling, transforming, filtering, stacking, and normalizing signals for CNN input, so the characterization of its preprocessing as minimal is not supported. (LLM, conf=0.85)
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- Authors
- Xing, X., Luo, N., Li, S., Zhou, L., Song, C., & Liu, J.
- Affiliations
- University of Shanghai for Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Song, Chengli
- Liu, Jun
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2022-03-21
- Volume
- 16
- Pages
- 701632-701632
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC8978337
- OpenAlex
- W4220740220
- Cited by
- 39
- References
- 23
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 9 · 2023: 10 · 2022: 2
- FWCI
- 6.00
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Natural Science Foundation of China (51901137, 51735003)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Keywords
- Parkinson's disease, essential tremor, tremor classification, differential diagnosis, surface electromyography, upper limb tremor, random forest, XGBoost, convolutional neural networks, feature importance
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