Deep learning and Blockchain-based Essential and Parkinson Tremor Classification Scheme

Hathaliya, Jigna J.; Modi, Hetav; Gupta, Rajesh; Tanwar, Sudeep

doi:10.1109/infocomwkshps54753.2022.9798053

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…The respective studies are presented in Table V. Researchers in [82], integrated Gated Recurrent Unit (GRU) and LSTM algorithms. Initially, accelerometer sensors were utilized to capture hand tremors in each subject's three axial dimensions.…”

Section: A Advanced DL Methods For Pd Diagnosis and Severity Assessme...mentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Parkinson's Disease (PD) is among the most frequent neurological disorders. Approaches that employ artificial intelligence and notably deep learning, have been extensively embraced with promising outcomes. This study dispenses an exhaustive review between 2016 and January 2023 on deep learning techniques used in the prognosis and evolution of symptoms and characteristics of the disease based on gait, upper limb movement, speech and facial expression-related information as well as the fusion of more than one of the aforementioned modalities. The search resulted in the selection of 87 original research publications, of which we have summarized the relevant information regarding the utilized learning and development process, demographic information, primary outcomes, and sensory equipment related information. Various deep learning algorithms and frameworks have attained state-of-theart performance in many PD-related tasks by outperforming conventional machine learning approaches, according to the research reviewed. In the meanwhile, we identify significant drawbacks in the existing research, including a lack of data availability and interpretability of models. The fast advancements in deep learning and the rise in accessible data provide the opportunity to address these difficulties in the near future and for the broad application of this technology in clinical settings.

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Section: A Advanced DL Methods For Pd Diagnosis and Severity Assessme...mentioning

confidence: 99%

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Skaramagkas

Pentari

Kefalopoulou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Through our literature search, we identified two studies that used sophisticated DL to distinguish between ET and Parkinsonian tremor. Initially, Hathaliya et al [ 48 ] integrated gated recurrent unit (GRU) and LSTM algorithms. Specifically, accelerometer data were passed through the GRU model, and served as input into the LSTM model to improve its performance.…”

Section: Literature Review: Deep Learning For Parkinson’s Disease Ide...mentioning

confidence: 99%

Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism

Skaramagkas,

Boura,

Spanaki

et al. 2023

Sensors

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Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients’ quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

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“…In addition, medical imaging is used to observe the visual presentation of radiomic features, which helps establish the disease progression early. Presently, various imaging techniques are used to detect disease progression, where SPECT and positron emission tomography (PET) detect the deficiency of metabolism, DNs, and neurochemical changes inside the human brain [7].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data

et al. 2022

Self Cite

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In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize the deficiency of dopamine-generated patterns inside the brain. These patterns are used to establish a patient’s disease progression, which helps distinguish the patients into different categories. Furthermore, we used a convolutional neural network (CNN) model to classify the patients based on the dopamine level inside the brain. The dataset used throughout this paper is the Parkinson’s progressive markers initiative (PPMI) dataset. The collected dataset was pre-processed and data amplification was performed to balance the imbalanced dataset. A CNN-based neural network was defined to classify input SPECT images into four categories. The motivation behind the proposed model is to reduce the number of resources consumed while maintaining the performance of the classification model. This will help the healthcare ecosystem run the classification model on mobile devices. The proposed model contains 14 layers with input layers, convolutional layers, max-pool layers, flatten layers, and dense layers with different dimensions. The dense layer classifies the patients into four different categories, including PSD, healthy control, scans without evidence of dopaminergic deficit (SWEDD), and GenReg PSD from the entire SPECT imaging dataset, which is used to establish the disease progression of different patients using SPECT images. The proposed model is trained with a large dataset with 58,692 images for training and 11,738 images for validation, and 7826 for testing. The proposed model outperforms the classification models from the surveyed papers. The proposed model’s accuracy is 0.889, recall is 0.9012, the precision is 0.9104, and the F1-score is 0.9057.

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Deep learning and Blockchain-based Essential and Parkinson Tremor Classification Scheme

Cited by 6 publications

References 14 publications

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Multi-Modal Deep Learning Diagnosis of Parkinson’s Disease—A Systematic Review

Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism

Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data

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