The detection of Parkinson disease using the genetic algorithm and SVM classifier
The speech signal is like the black box of human beings where much information is hidden.
The treatment of this signal provides us with the speaker's identity. In a way, it is similar to an …
The treatment of this signal provides us with the speaker's identity. In a way, it is similar to an …
Artificial intelligence model for parkinson disease detection using machine learning algorithms
In order for Parkinson's disease (PD) treatment and examination to be logical, a key
requirement is that estimates of disease stage and severity are quantitative, reliable, and …
requirement is that estimates of disease stage and severity are quantitative, reliable, and …
Automatic classification of the obstruction site in obstructive sleep apnea based on snoring sounds
Y Liu, Y Feng, Y Li, W Xu, X Wang, D Han - American Journal of …, 2022 - Elsevier
Objectives Snoring is a common symptom of obstructive sleep apnea (OSA) which is
considered to be potential predictors of the obstruction site. Successful treatment of OSA …
considered to be potential predictors of the obstruction site. Successful treatment of OSA …
ECG signal diagnosis using Discrete Wavelet Transform and K-Nearest Neighbor classifier.
Y Toulni, T Belhoussine Drissi, B Nsiri - Proceedings of the 4th …, 2021 - dl.acm.org
Early detection of heart problems can save the lives of many people around the world; the
electrical activity of the heart represented by electrocardiograms (ECGs) gives us important …
electrical activity of the heart represented by electrocardiograms (ECGs) gives us important …
Machine learning Ensemble for the Parkinson's disease using protein sequences
The most challenging issue in diagnosing and treating neurological disorders is gene
identification that causes the disease. Classification of the genes that cause or initiate …
identification that causes the disease. Classification of the genes that cause or initiate …
[PDF][PDF] CNN and LSTM for the classification of parkinson's disease based on the GTCC and MFCC
Parkinson's disease is a recognizable clinical syndrome with a variety of causes and clinical
presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 …
presentations; it represents a rapidly growing neurodegenerative disorder. Since about 90 …
[PDF][PDF] Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease
N Benayad, Z Soumaya, BD Taoufiq… - International Journal of …, 2022 - academia.edu
Among the several ways followed for detecting Parkinson's disease, there is the one based
on the speech signal, which is a symptom of this disease. In this paper focusing on the …
on the speech signal, which is a symptom of this disease. In this paper focusing on the …
Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings
D Hemmerling, M Wodzinski… - 2023 45th Annual …, 2023 - ieeexplore.ieee.org
Parkinson's disease (PD) is the 2 nd most prevalent neurodegenerative disease in the
world. Thus, the early detection of PD has recently been the subject of several scientific and …
world. Thus, the early detection of PD has recently been the subject of several scientific and …
[PDF][PDF] ECG Signal Classification Using DWT, MFCC and SVM Classifier
Accepted: 10 February 2023 The diagnosis techniques of diseases which are based on
biomedical signals processing are constantly evolving, cardiovascular diseases are no …
biomedical signals processing are constantly evolving, cardiovascular diseases are no …
DWT-BSS: Blind Source Separation applied to EEG signals by extracting wavelet transform's approximation coefficients
The Electroencephalogram (EEG) signal is widely contaminated by a physiological artifact,
such as muscle activity, heart rhythm, and eye movement. The researcher has proposed a …
such as muscle activity, heart rhythm, and eye movement. The researcher has proposed a …