Optimizing Parkinson's Disease Classification and Severity Assessment Using Dense Multiscale Sample Entropy and Hybrid Feature Selection-Reference-Cited by-同舟云学术

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Optimizing Parkinson's Disease Classification and Severity Assessment Using Dense Multiscale Sample Entropy and Hybrid Feature Selection

Published:2023-12-14 Issue: Volume: Page:64-69
ISSN:
Container-title:Proceedings of the 2023 11th International Conference on Information Technology: IoT and Smart City
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Author:

Nguyen Quoc Duy Nam¹^ORCID,Tran Thi Hong¹^ORCID,Nakano Tadashi¹^ORCID

Affiliation:

1. Osaka Metropolitan University, Japan

Funder

Japan Science and Technology Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638985.3638996

Reference22 articles.

1. Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating

2. A deep learning approach for parkinson’s disease severity assessment

3. Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network

4. Lauren Burrell, Otis Smart, George Georgoulas, Eric Marsh, and George Vachtsevanos. 2007. Evaluation of Feature Selection Techniques for Analysis of Functional MRI and EEG.256–262.

5. Leon Chaitow and Judith DeLany. 2011. Chapter 3 - Gait analysis. Churchill Livingstone 61–84. https://doi.org/10.1016/B978-0-443-06815-7.00003-6

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