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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 |
Healthc Inform Res. 2020;26(4):274-283. Published online October 31, 2020 DOI: https://doi.org/10.4258/hir.2020.26.4.274 |
Analysis of Smartphone Recordings in Time, Frequency, and Cepstral Domains to Classify Parkinson’s Disease Using Human Factor Cepstral Coefficient on Multiple Types of Voice Recordings for Detecting Patients with Parkinson's Disease Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease Multiclass classification of Parkinson’s disease using cepstral analysis Low Frequency of Leisure-Time Activities Correlates with Cognitive Decline and Apathy in Patients with Parkinson’s Disease Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings Influence of Hypertension on Neurocognitive Domains in Nondemented Parkinson’s Disease Patients Can a Smartphone Diagnose Parkinson Disease? A Deep Neural Network Method and Telediagnosis System Implementation |