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

An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning

Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, leading to gradual loss of neurons and subsequent decline in cognitive, and behavioral activities. The two frequent diseases affecting the world's significant population falling in the above category are Alzheimer's disease (AD) and Parkinson's disease (PD). These disorders substantially impact the quality of life and burden healthcare systems and society. The demographic characteristics, and machine learning approaches have now been employed to diagnose these illnesses; however, they possess accuracy limitations. Therefore, the authors have developed ranking-based ensemble approach based on the weighted strategy of deep learning classifiers. The whole modeling procedure of the proposed approach incorporates three phases. In phase I, preprocessing techniques are applied to clean the noise in datasets to make it standardized according to deep learning models as it significantly impacts their performance. In phase II, five deep learning models are selected for classification and calculation of prediction results. In phase III, a ranking-based ensemble approach is proposed to ensemble the results of the five models after calculating the ranks and weights of them. In addition, the Magnetic Resonance Imaging (MRI) datasets named Alzheimer's Disease Neuroimaging Initiative (ADNI) for AD classification and Parkinson's Progressive Marker Initiative (PPMI) for PD classification are selected to validate the proposed approach. Furthermore, the proposed method achieved the classification accuracy on AD- Cognitive Normals (CN) at 97.89%, AD- Mild Cognitive Impairment (MCI) at 99.33% and CN-MCI at 99.44% and on PD-CN at 99.22%, PD- Scans Without Evidence of Dopaminergic Effect (SWEDD) at 97.56% and CN-SWEDD at 98.22% respectively. Also, the multi-class classification shows the promising accuracy of 97.18% for AD and 97.85% for PD for the proposed framework. The findings of the study show that the proposed deep learning-based ensemble technique is competitive for AD and PD prediction in both multiclass and binary class classification. Furthermore, the proposed approach enhances generalization performance in diagnosing neurodegenerative diseases and performs better than existing approaches.

Quantifying instability in neurological disorders EEG based on phase space DTM function

Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.

Data leakage in deep learning studies of translational EEG

A growing number of studies apply deep neural networks (DNNs) to recordings of human electroencephalography (EEG) to identify a range of disorders. In many studies, EEG recordings are split into segments, and each segment is randomly assigned to the training or test set. As a consequence, data from individual subjects appears in both the training and the test set. Could high test-set accuracy reflect data leakage from subject-specific patterns in the data, rather than patterns that identify a disease? We address this question by testing the performance of DNN classifiers using segment-based holdout (in which segments from one subject can appear in both the training and test set), and comparing this to their performance using subject-based holdout (where all segments from one subject appear exclusively in either the training set or the test set). In two datasets (one classifying Alzheimer's disease, and the other classifying epileptic seizures), we find that performance on previously-unseen subjects is strongly overestimated when models are trained using segment-based holdout. Finally, we survey the literature and find that the majority of translational DNN-EEG studies use segment-based holdout. Most published DNN-EEG studies may dramatically overestimate their classification performance on new subjects.

An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

Parkinson's disease diagnosis using deep learning: A bibliometric analysis and literature review

Parkinson's Disease (PD) is a progressive neurodegenerative illness triggered by decreased dopamine secretion. Deep Learning (DL) has gained substantial attention in PD diagnosis research, with an increase in the number of published papers in this discipline. PD detection using DL has presented more promising outcomes as compared with common machine learning approaches. This article aims to conduct a bibliometric analysis and a literature review focusing on the prominent developments taking place in this area. To achieve the target of the study, we retrieved and analyzed the available research papers in the Scopus database. Following that, we conducted a bibliometric analysis to inspect the structure of keywords, authors, and countries in the surveyed studies by providing visual representations of the bibliometric data using VOSviewer software. The study also provides an in-depth review of the literature focusing on different indicators of PD, deployed approaches, and performance metrics. The outcomes indicate the firm development of PD diagnosis using DL approaches over time and a large diversity of studies worldwide. Additionally, the literature review presented a research gap in DL approaches related to incremental learning, particularly in relation to big data analysis.

A review of emergent intelligent systems for the detection of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automatic feature extraction from EEG signals remains a challenging task. The classification of neuropsychiatric disorders demands the extraction of neuro-markers for use in automated EEG classification. Numerous advanced deep learning algorithms can be used for this purpose. In this article, we present a comprehensive review of the main factors and parameters that affect the performance of deep neural networks in classifying different neuropsychiatric disorders using EEG signals. We also analyze the EEG features used for improving classification performance. Our analysis includes 82 scientific journal papers that applied deep neural networks for subject-wise classification based on EEG signals. We extracted information on the EEG dataset and types of disorders, deep neural network structures, performance, and hyperparameters. The results show that most studies have focused on clinical classification, achieving an average accuracy of 91.83 ± 7.34, with convolutional neural networks (CNNs) being the most frequently used network architecture and resting-state EEG signals being the most commonly used data type. Additionally, the review reveals that depression (N = 18), Alzheimer's (N = 11), and schizophrenia (N = 11) were studied more frequently than other types of neuropsychiatric disorders. Our review provides insight into the performance of deep neural networks in EEG classification and highlights the importance of EEG feature extraction in improving classification accuracy. By identifying the main factors and parameters that affect deep neural network performance in EEG classification, our review can guide future research in this area. We hope that our findings will encourage further exploration of deep learning methods for EEG classification and contribute to the development of more accurate and effective methods for diagnosing and monitoring neuropsychiatric disorders using EEG signals.

Deep-learning detection of mild cognitive impairment from sleep electroencephalography for patients with Parkinson's disease

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease.

Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease

BACKGROUND AND OBJECTIVES

Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points.

METHODS

In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features.

RESULTS

We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable.

CONCLUSIONS

The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.

Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling a person's movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient's nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person's voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson's cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.

Paroxysmal Slow-Wave Events Are Uncommon in Parkinson's Disease

Background: Parkinson’s disease (PD) is currently considered to be a multisystem neurodegenerative disease that involves cognitive alterations. EEG slowing has been associated with cognitive decline in various neurological diseases, such as PD, Alzheimer’s disease (AD), and epilepsy, indicating cortical involvement. A novel method revealed that this EEG slowing is composed of paroxysmal slow-wave events (PSWE) in AD and epilepsy, but in PD it has not been tested yet. Therefore, this study aimed to examine the presence of PSWE in PD as a biomarker for cortical involvement. Methods: 31 PD patients, 28 healthy controls, and 18 juvenile myoclonic epilepsy (JME) patients (served as positive control), underwent four minutes of resting-state EEG. Spectral analyses were performed to identify PSWEs in nine brain regions. Mixed-model analysis was used to compare between groups and brain regions. The correlation between PSWEs and PD duration was examined using Spearman’s test. Results: No significant differences in the number of PSWEs were observed between PD patients and controls (p > 0.478) in all brain regions. In contrast, JME patients showed a higher number of PSWEs than healthy controls in specific brain regions (p < 0.023). Specifically in the PD group, we found that a higher number of PSWEs correlated with longer disease duration. Conclusions: This study is the first to examine the temporal characteristics of EEG slowing in PD by measuring the occurrence of PSWEs. Our findings indicate that PD patients who are cognitively intact do not have electrographic manifestations of cortical involvement. However, the correlation between PSWEs and disease duration may support future studies of repeated EEG recordings along the disease course to detect early signs of cortical involvement in PD.

Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques

Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for preventing disease development. In this study, we present efficient discrete wavelet transform (DWT)-based methods for detecting PD from health control (HC) in two cases, namely, off-and on-medication. First, the EEG signals are preprocessed to remove major artifacts before being decomposed into several EEG sub-bands (approximate and details) using DWT. The features are then extracted from the wavelet packet-derived reconstructed signals using different entropy measures, namely, log energy entropy, Shannon entropy, threshold entropy, sure entropy, and norm entropy. Several machine learning techniques are investigated to classify the resulting PD/HC features. The effects of DWT coefficients and brain regions on classification accuracy are being investigated as well. Two public datasets are used to verify the proposed methods: the SanDiego dataset (31 subjects, 93 min) and the UNM dataset (54 subjects, 54 min). The results are promising and show that four entropy measures: log energy entropy, threshold entropy, sure entropy, and modified-Shannon entropy (TShEn) lead to high classification accuracy, indicating they are good biomarkers for PD detection. With the SanDiego dataset, the classification results of off-medication PD versus HC are 99.89, 99.87, and 99.91 for accuracy, sensitivity, and specificity, respectively, using the combination of DWT + TShEn and KNN classifier. Using the same combination, the results of on-medication PD versus HC are 94.21, 93.33, and 95%. With the UNM dataset, the obtained classification accuracy is around 99.5% in both cases of off-and on-medication PD using DWT + TShEn + SVM and DWT + ThEn + KNN, respectively. The results also demonstrate the importance of all DWT coefficients and that selecting a suitable small number of EEG channels from several brain regions could improve the classification accuracy.

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application's performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model's performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.

Deep neural network to differentiate brain activity between patients with euthymic bipolar disorders and healthy controls during verbal fluency performance: A multichannel near-infrared spectroscopy study

In this study, we aimed to differentiate between euthymic bipolar disorder (BD) patients and healthy controls (HC) based on frontal activity measured by fNIRS that were converted to spectrograms with Convolutional Neural Networks (CNN). And also, we investigated brain regions that cause this distinction. In total, 29 BD patients and 28 HCs were recruited. Their brain cortical activities were measured using fNIRS while performing letter versions of VFT. Each one of the 24 fNIRS channels was converted to a 2D spectrogram on which a CNN architecture was designed and utilized for classification. We found that our CNN algorithm using fNIRS activity during a VFT is able to differentiate subjects with BD from healthy controls with 90% accuracy, 80% sensitivity, and 100% specificity. Moreover, validation performance reached an AUC of 94%. From our individual channel analyses, we observed channels corresponding to the left inferior frontal gyrus (left-IFC), medial frontal cortex (MFC), right dorsolateral prefrontal cortex (DLPFC), Broca area, and right premotor have considerable activity variation to distinguish patients from HC. fNIRS activity during VFT can be used as a potential marker to classify euthymic BD patients from HCs. Activity particularly in the MFC, left-IFC, Broca's area, and DLPFC have a considerable variation to distinguish patients from healthy controls.

Parkinson’s disease detection based on multi-pattern analysis and multi-scale convolutional neural networks

Parkinson’s disease (PD) is a complex neurodegenerative disease. At present, the early diagnosis of PD is still extremely challenging, and there is still a lack of consensus on the brain characterization of PD, and a more efficient and robust PD detection method is urgently needed. In order to further explore the features of PD based on brain activity and achieve effective detection of PD patients (including OFF and ON medications), in this study, a multi-pattern analysis based on brain activation and brain functional connectivity was performed on the brain functional activity of PD patients, and a novel PD detection model based on multi-scale convolutional neural network (MCNN) was proposed. Based on the analysis of power spectral density (PSD) and phase-locked value (PLV) features of multiple frequency bands of two independent resting-state electroencephalography (EEG) datasets, we found that there were significant differences in PSD and PLV between HCs and PD patients (including OFF and ON medications), especially in the β and γ bands, which were very effective for PD detection. Moreover, the combined use of brain activation represented by PSD and functional connectivity patterns represented by PLV can effectively improve the performance of PD detection. Furthermore, our proposed MCNN model shows great potential for automatic PD detection, with cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve all above 99%. Our study may help to further understand the characteristics of PD and provide new ideas for future PD diagnosis based on spontaneous EEG activity.

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

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.

BDD-Net: An End-to-End Multiscale Residual CNN for Earthquake-Induced Building Damage Detection

Building damage maps can be generated from either optical or Light Detection and Ranging (Lidar) datasets. In the wake of a disaster such as an earthquake, a timely and detailed map is a critical reference for disaster teams in order to plan and perform rescue and evacuation missions. Recent studies have shown that, instead of being used individually, optical and Lidar data can potentially be fused to obtain greater detail. In this study, we explore this fusion potential, which incorporates deep learning. The overall framework involves a novel End-to-End convolutional neural network (CNN) that performs building damage detection. Specifically, our building damage detection network (BDD-Net) utilizes three deep feature streams (through a multi-scale residual depth-wise convolution block) that are fused at different levels of the network. This is unlike other fusion networks that only perform fusion at the first and the last levels. The performance of BDD-Net is evaluated under three different phases, using optical and Lidar datasets for the 2010 Haiti Earthquake. The three main phases are: (1) data preprocessing and building footprint extraction based on building vector maps, (2) sample data preparation and data augmentation, and (3) model optimization and building damage map generation. The results of building damage detection in two scenarios show that fusing the optical and Lidar datasets significantly improves building damage map generation, with an overall accuracy (OA) greater than 88%.

Resting-state electroencephalography based deep-learning for the detection of Parkinson's disease

Parkinson's disease (PD) is one of the most serious and challenging neurodegenerative disorders to diagnose. Clinical diagnosis on observing motor symptoms is the gold standard, yet by this point nerve cells are degenerated resulting in a lower efficacy of therapeutic treatments. In this study, we introduce a deep-learning approach based on a recently-proposed 20-Layer Convolutional Neural Network (CNN) applied on the visual realization of the Wavelet domain of a resting-state EEG. The proposed approach was able to efficiently and accurately detect PD as well as distinguish subjects with PD on medications from subjects who are off medication. The gradient-weighted class activation mapping (Grad-CAM) was used to visualize the features based on which the approach provided the predictions. A significantly high accuracy, sensitivity, specificity, AUC, and Weighted Kappa Score up to 99.9% were achieved and the visualization of the regions in the Wavelet images that contributed to the deep-learning approach decisions was provided. The proposed framework can then serve as an effective computer-aided diagnostic tool that will support physicians and scientists in further understanding the nature of PD and providing an objective and confident opinion regarding the clinical diagnosis of the disease.

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

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.

Novel automated PD detection system using aspirin pattern with EEG signals

BACKGROUND AND OBJECTIVE

Parkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals.

METHOD

In this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting.

RESULTS

A public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively.

CONCLUSION

Our developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.