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| Analysis of Smartphone Recordings in Time, Frequency, and Cepstral Domains to Classify Parkinson’s Disease |
| Ilias Tougui, Abdelilah Jilbab, Jamal El Mhamdi |
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Healthc Inform Res. 2020;26(4):274-283. Published online October 31, 2020 DOI: https://doi.org/10.4258/hir.2020.26.4.274 |
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Analysis of Smartphone Recordings in Time, Frequency, and Cepstral Domains to Classify Parkinson’s Disease Healthcare Informatics Research. 2020;26(4):274-283  Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people International Journal of Speech Technology. 2016;19(3):449-456 Speech analysis for the detection of Parkinson’s disease by combined use of empirical mode decomposition, Mel frequency cepstral coefficients, and the K-nearest neighbor classifier ITM Web of Conferences. 2022;43:01019 Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease TecnoLógicas. 2020;23(47):93-108 Multiclass classification of Parkinson’s disease using cepstral analysis International Journal of Speech Technology. 2017;21(1):39-49 Smartphone monitoring for Parkinson’s disease Nature Medicine. 2021; Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings . 2020; Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings . 2020; Automatic Parkinson’s disease detection based on the combination of long-term acoustic features and Mel frequency cepstral coefficients (MFCC) Biomedical Signal Processing and Control. 2022;78:104013 Using Fuzzy-Rough Subset Evaluation for Feature Selection and Naive Bayes to Classify the Parkinson’s Disease . 2021; |
