Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity

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

doi:10.1016/j.compeleceng.2022.107946

Cited by 12 publications

(5 citation statements)

References 15 publications

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“…The implementation of the proposed PD classification is performed using the PYTHON programming language. Besides, the comparison with the conventional PD classification methods likes 2D-CNN [22], CSP+KNN [19], DWT+SVM [18] and Channelwise CNN [20] for depicting the superiority of the proposed model.…”

Section: Resultsmentioning

confidence: 99%

“…Complexity may be introduced by utilizing various machine learning algorithms and feature extraction metrics, which can make the models difficult to comprehend and use in clinical contexts. To accurately classify PSD and healthy control (HC) participants, a convolutional neural network (CNN)-based classification model with seven hidden layers and various filter sizes was suggested [20]. Three-dimensional data was transformed into a one-dimensional tensor flow using a flattening layer.…”

Section: Related Workmentioning

confidence: 99%

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CCZO Residual GhostNet

Victor Paul M,

Shankar

2024

Int. j. electr. comput. eng. syst. (Online)

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Parkinson's disease (PD) classification plays a crucial role in medical diagnosis and patient management. Identifying Parkinson's disease at an early stage can lead to more effective treatment and improved patient outcomes. However, existing methods for Parkinson's disease classification face several limitations. The foremost limitation is the need for accurate and reliable diagnostic tools, as misdiagnosis can lead to inappropriate treatments and unnecessary stress for patients. Thus, a hybrid deep learning model is introduced in this research. The proposed model involves the utilization of EEG signals obtained from a publicly available dataset. Key features are extracted from the EEG signals using a bandpass filter, and every feature is associated with specific brainwave frequencies and cognitive states. The feature mapping and classification are executed through the Chaotic Chebyshev Zebra optimization- based Residual GhostNet (CCZO_Residual_GhostNet). This hybrid classifier, Residual GhostNet, combines ResNet-152 with GhostNet, enhancing classification precision. Furthermore, the CCZO algorithm optimizes the loss function, introducing elements of chaos and Chebyshev mapping to improve classification accuracy. The assessment based on accuracy, sensitivity, specificity, and F-score acquired 98.76%, 98.59%, 98.95%, and 99%, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

CCZO Residual GhostNet

Victor Paul M,

Shankar

2024

Int. j. electr. comput. eng. syst. (Online)

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“…NDDs gradually kill the patient's motor neurons, thus affecting essential muscle activities such as talking, walking, breathing, and swallowing [17]. They cause gradual muscle weakness, uncontrollable tics, muscle or limb rigidity, and spasms on the knee and ankles, finally causing the patient to lose their ability to voluntarily control their movements [18].…”

Section: Common Motor Impairments Caused By Nddsmentioning

confidence: 99%

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

et al. 2022

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Neurodegenerative diseases (NDDs) are among the 10 causes of death worldwide. The effects of NDDs, including irreversible motor impairments, have an impact not only on patients themselves but also on their families and social environments. One strategy to mitigate the pain of NDDs is to early identify and remotely monitor related motor impairments using wearable devices. Technological progress has contributed to reducing the hardware complexity of mobile devices while simultaneously improving their efficiency in terms of data collection and processing and energy consumption. However, perhaps the greatest challenges of current mobile devices are to successfully manage the security and privacy of patient medical data and maintain reasonable costs with respect to the traditional patient consultation scheme. In this work, we conclude: (1) Falls are most monitored for Parkinson’s disease, while tremors predominate in epilepsy and Alzheimer’s disease. These findings will provide guidance for wearable device manufacturers to strengthen areas of opportunity that need to be addressed, and (2) Of the total universe of commercial wearables devices that are available on the market, only a few have FDA approval, which means that there is a large number of devices that do not safeguard the integrity of the users who use them.

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“…Thus, PSD patients suffer from abnormal activity patterns resulting in movement-based severity and a high risk of traumatic events. [2].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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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Parkinson and essential tremor classification to identify the patient’s risk based on tremor severity

Cited by 12 publications

References 15 publications

CCZO Residual GhostNet

CCZO Residual GhostNet

A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases

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

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