CrossRef Text and Data Mining
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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

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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   Crossref logo
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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   Crossref logo
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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   Crossref logo
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Cepstral Analysis and Hilbert-Huang Transform for Automatic Detection of Parkinson’s Disease
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Multiclass classification of Parkinson’s disease using cepstral analysis
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Smartphone monitoring for Parkinson’s disease
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Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings
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Gradient Boosting for Parkinson’s Disease Diagnosis from Voice Recordings
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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   Crossref logo
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Using Fuzzy-Rough Subset Evaluation for Feature Selection and Naive Bayes to Classify the Parkinson’s Disease
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