Early Detection of Parkinson Disease Using Wavelet Transform Along with Fourier Transform

Rizvi, Syed Qasim Afser; Wang, Guojun; Xing, Xiaofei

doi:10.1007/978-981-15-1301-5_26

Cited by 5 publications

(5 citation statements)

References 18 publications

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“…It will also reduce the workload of clinical staff and enable patients to have an easier diagnosis process. There were many studies in the literature for PD diagnosis [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]26]. Yücelbaş et al created a data set by conducting the subjects to diagnose PD.…”

Section: Discussionmentioning

confidence: 99%

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A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

Al-Otaibi

Ayouni

Khan

et al. 2022

BioMed Research International

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It makes no difference whether a person is male or female when it comes to neurodegenerative disorders; both sexes are equally susceptible to their devastating effects. Sometimes, it is unclear why a person in their life got a condition that is well-known in the world, such as Parkinson’s disease. Other times, it is evident why the individual obtained the ailment (PD). In modern times, a variety of cutting-edge algorithms that are based on treatment protocols have been developed for the purpose of diagnosing Parkinson’s disease. The approach that is presented in this article is the most current one; it was created using deep learning, and it can predict how severely Parkinson’s disease would affect a patient. In order to diagnose this condition, it is necessary to conduct a comprehensive medical history, a history of any past treatments, physical exams, and certain blood tests and brain films. Because they are less time-consuming and costly, diagnoses are becoming an increasingly important part of medical practice. The diagnosis of Parkinson’s disease by the physician is supported by the findings of the present research, which analyzed the voices of 253 participants. Preprocessing is done in order to get the most accurate results possible from the data. In order to carry out the technique of balancing, a methodical sampling approach was used to choose the data that would afterwards be evaluated. Using a feature selection approach that was determined by the magnitude of the label’s influence, many data groups were created and organized. DT, SVM, and kNN are three methods that are used in classification algorithms and performance assessment criteria. The model was developed as a result of selecting the classification method and data group that had the greatest performance value. This decision led to the creation of the model. During the process of building the model, the SVM technique was used, and data comprising 45% of the original data set were utilized. The information was arranged in descending order of significance, beginning with the most pertinent. In addition to achieving exceptional outcomes in every other aspect of the project, the performance accuracy target was successfully met at 86 percent. As a consequence of this, it has been decided that the physician will be provided with medical decision support with the assistance of the data set obtained from the speech recordings of the individual who may have Parkinson’s disease and the model that has been developed. This has led to the conclusion that medical decision support will be offered to the physician.

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Section: Discussionmentioning

confidence: 99%

“…Yücelbaş et al created a data set by conducting the subjects to diagnose PD. The generated data set was grouped according to the age factor and analyzed using the Dual Density 1-D Wavelet Transform method [16][17][18]. However, it has been studied on a small data set with few features.…”

Section: Discussionmentioning

confidence: 99%

A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

Al-Otaibi

Ayouni

Khan

et al. 2022

BioMed Research International

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“…Several approaches have emerged for the analysis of EEG time series to study neurological diseases. These approaches include the Fourier Transform (FT), Wavelet Transforms (WT) 24,25 , Brain Area Connectivity Matrices 21 , and nonlinear methods such as the Lyapunov Exponent, Correlation Dimension, Phase Space Reconstruction, as well as Lempel-Ziv Entropies and Complexities [26][27][28][29][30] . These approaches effectively capture stationary features of the time series, providing valuable insights into the dynamics and progression of the system.…”

Section: Introductionmentioning

confidence: 99%

Exploring quantile graphs: a novel approach for classifying Parkinson's disease-related events during the TUG test

Pineda,

Vicchietti,

Carra

et al. 2024

Preprint

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Parkinson’s disease (PD) is a neurological disorder that affects the central nervous system, resulting in the progressive impairment of movements, such as tremors, slow movements, muscle stiffness, imbalance, speech and writing disturbances, and deterioration of gait. Understanding the challenges associated with PD, including episodes of freezing, where individuals suddenly stop and struggle to resume rhythmic movements, has been a subject of study in the literature. The reliable detection of freezing before it occurs still lacks an effective mechanism, emphasizing the importance of understanding the dynamic changes in the brain related to both frozen moments and healthy functioning. Recent studies have focused on analyzing PD through techniques such as electroencephalography (EEG), which records the brain’s electrical activity and generates temporal series. However, this approach faces challenges, prompting the exploration of alternative techniques and interdisciplinary approaches to gather more comprehensive information about these signals. A promising approach is the use of Quantile Graphs (QG), which have shown potential in distinguishing patients with various medical conditions but have not yet been tested in PD patients. The QG method involves mapping temporal series into a network based on transition probabilities. In this context, this article aims to investigate the application of QG to quantify differences in brains affected by PD and pave the way for the application of this method to the disease. To achieve this initial goal, EEG data from 18 channels were collected from four PD patients, classifying three events of the patient during the Timed Up and Go (TUG) test (normal walking, Freezing of Gait, and voluntary stop). The findings confirmed the utility of QG in analyzing complex and nonlinear signals, such as those generated by EEG recordings in individuals with PD. Using QG, researchers can obtain valuable information about the intricate dynamics of brain activity related to PD. This approach holds promise for improving the understanding of PD and potentially aiding in the early detection and treatment of freezing episodes, ultimately enhancing the quality of life for individuals living with this challenging condition.

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“…P Arkinson's Disease (PD) is a well-known neurodegenerative disorder influencing a substantial population [1]. It progresses in a manner impacting the motor actions of the patient intensely by inducing the loss of dopaminergic neurons in the substantia nigra pars compacta [1], [2]. It deprives the patient of cardinal motor activities, including cognitive and speech control too [3].…”

Section: Introductionmentioning

confidence: 99%

Classifying Parkinson’s Disease Using Resting State Electroencephalogram Signals and U^EN-PDNet

Rizvi,

Wang,

Khan

et al. 2023

IEEE Access

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Parkinson's Disease (PD) in the set of neuro-degenerative disorders stimulates due to the loss of dopaminergic neurons from the substantia nigra. Electroencephalogram (EEG) signals are being extensively utilized for diagnosing PD. The existing approaches extract the features using various frequency transformations that lose valuable signal information. An optimized Deep Convolutional Neural Network (CNN) inspired by the encoder part of U-Net architecture is proposed for classifying PD incorporating the resting electroencephalogram (EEG) signal dataset. The proposed model follows the U-Net architecture for extracting the features from the signals. The EEG recordings are taken from two datasets: the University of Mexico (UNM) EEGs and the University of California San Diego (UCSD) resting state dataset.The EEGs are pre-processed with a basic pre-processing pipeline, which is then separated into single channels, plotting each channel as a simple graph. These graphs are then fed to the proposed 23-layered convolutional neural network (CNN) for classifying PD from the normal control. Consequently, the model achieved maximum values of 93.10%, 93.18%, 93.09%, and 0.9313 of accuracy, precision, recall, and F1-score respectively for the UNM dataset, whereas, 97.90%, 98%, 97.87% and 0.9794 of accuracy, precision, recall, and F1-score respectively for UCSD dataset. The results show improved scores compared to the Machine Learning and CNN models applied on the same datasets individually.

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Early Detection of Parkinson Disease Using Wavelet Transform Along with Fourier Transform

Cited by 5 publications

References 18 publications

A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

Exploring quantile graphs: a novel approach for classifying Parkinson's disease-related events during the TUG test

Classifying Parkinson’s Disease Using Resting State Electroencephalogram Signals and U^EN-PDNet

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Early Detection of Parkinson Disease Using Wavelet Transform Along with Fourier Transform

Cited by 5 publications

References 18 publications

A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

A Novel Method for Parkinson’s Disease Diagnosis Utilizing Treatment Protocols

Exploring quantile graphs: a novel approach for classifying Parkinson's disease-related events during the TUG test

Classifying Parkinson’s Disease Using Resting State Electroencephalogram Signals and U EN -PDNet

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Classifying Parkinson’s Disease Using Resting State Electroencephalogram Signals and U^EN-PDNet