Makale detayı · 2013
Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings
Dergi
IEEE Transactions on Journal of Biomedical and Health InformaticsISSN 2168-2194
ISSN kaydı başka bir dergiye işaret ediyor; ad YÖKSİS kaydından.
- Yıl
- 2013
- Tür
- article
Veri kaynağı ayrımı
- YÖKSİS YÖKSİS makale kaydı
- YÖKSİS dergi adı IEEE Transactions on Journal of Biomedical and Health Informatics
- Katalog eşleşmesi (ISSN) IEEE Journal of Biomedical and Health Informatics
- OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)
Özet
OpenAlex · İngilizce
There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.
Konular
Atıflar
OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.
710 atıf
OpenAlex cited_by_count (önbellek / veritabanı)
Yerel katalogda bu makaleye atıf yapan 55 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).
- A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform 2019
- The detection of Parkinson disease using the genetic algorithm and SVM classifier 2021
- Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson’xxs disease 2017
- Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson s disease 2017
- GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson Disease Detection Using EEG Signals 2021
- Computer‐Aided Diagnosis of Parkinson’s Disease Using Complex‐Valued Neural Networks and mRMR Feature Selection Algorithm 2015
- Parkinson’s detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decomposition 2021
- A Deep Learning-CNN Based System for Medical Diagnosis: An Application on Parkinson’s Disease Handwriting Drawings 2018
- A Machine Learning System for the Diagnosis of Parkinson's Disease from Speech Signals and Its Application to Multiple Speech Signal Types 2016
- Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements 2020