GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Loh, Hui Wen; Ooi, Chui Ping; Palmer, Elizabeth E.; Barua, Prabal Datta; Doğan, Şengül; Tuncer, Türker; Bayğın, Mehmet; Acharya, U. Rajendra

doi:10.3390/electronics10141740

Cited by 67 publications

(35 citation statements)

References 60 publications

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“…The first convolutional layer contain numerous filters which extract features from the input image to generate multiple feature maps. The subsequent pooling and convolutional layers reduce the dimension of the feature maps and further enhance the features, thereby reducing the complexity of the feature map and the likelihood of overfitting [25]. This could be considered as analogous to the human visual system, where the visual cortex attempts to break down images into simpler representations for the brain to perceive the image with ease [24].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…After the final pooling layer, the feature maps are converted into single-list vectors at the flatten layer (Figure 2). The neurons in the neural networks, also known as the fully connected layers, will then learn to recognize the features from the single-list vectors and perform image classifications [25]. Hence, CNN models are known for their exemplary image recognition ability, which many studies have successfully demonstrated the success of CNN in medical imaging, including the recognition of breast tumors, and eye diseases using mammogram and color fundus images, respectively [26].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…In this review, we have found 6 studies that had proposed deep learning models to recognize EEG characteristics for automated detection of PD. Nearly half of these studies proposed the use of the CNN model [25,47,48], and the remaining three studies had proposed the application of an RNN [49], DNN [50], and a hybrid deep learning model that combines CNN and RNN algorithms [51] (Figure 5). The bestperforming model was developed by Khare et al [47], who has also proposed a CNN model with smoothed pseudo-Wigner Ville distribution (SPWVD) features from EEG signals as an input, and further obtained an accuracy near 100% (Figure 7, Appendix A Table A1).…”

Section: Brain Analysismentioning

confidence: 99%

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Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Section: Brain Analysismentioning

confidence: 99%

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Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Loh

Hong²,

Ooi

et al. 2021

Sensors

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“…[51,64,66,70] using ANN with values over 97% for sensitivity and precision, refs. [38,39,49] considering CNN with values over 99% for accuracy, precision and sensitivity ( [38,39] were based on the same study), refs. [29,30] using CNN + RNN with values over 93% in accuracy, sensitivity, and precision, refs.…”

Section: Evaluation Of the Models Usedmentioning

confidence: 99%

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

2022

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Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.

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“…Therefore, there is a growing need for brain-computer interface (BCI) systems for healthy elderly persons going through nonpathological physical and cognitive declines [30,52]. BCI systems connect the brain to a computer, allowing the user to enhance their life [23,51]. As machine learning and intelligent robotic technology advance, the range of BCI applications is growing.…”

Section: Introductionmentioning

confidence: 99%

Explainable Convolutional Neural Network to Investigate the Age-Related Changes in Multi-Order Functional Connectivity

Dong¹,

Jin²,

Bak³

et al. 2021

Preprint

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Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

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GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

Cited by 67 publications

References 60 publications

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

Explainable Convolutional Neural Network to Investigate the Age-Related Changes in Multi-Order Functional Connectivity

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