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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  Using Human Factor Cepstral Coefficient on Multiple Types of Voice Recordings for Detecting Patients with Parkinson's Disease IRBM. 2017;38(6):346-351 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 Multiclass classification of Parkinson’s disease using cepstral analysis International Journal of Speech Technology. 2017;21(1):39-49 Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease TecnoLógicas. 2020;23(47):93-108 Low Frequency of Leisure-Time Activities Correlates with Cognitive Decline and Apathy in Patients with Parkinson’s Disease Advances in Parkinson's Disease. 2014;03(03):15-21 Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings . 2020; Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings . 2020; Influence of Hypertension on Neurocognitive Domains in Nondemented Parkinson’s Disease Patients Parkinson's Disease. 2014;2014:1-10 Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation Parkinson's Disease. 2017;2017:1-11 |
