Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Measurement and Evaluation of Finger Tapping Movements Using Log-linearized Gaussian Mixture Networks

This paper proposes a method to quantitatively measure and evaluate finger tapping movements for the assessment of motor function using log-linearized Gaussian mixture networks (LLGMNs). First, finger tapping movements are measured using magnetic sensors, and eleven indices are computed for evaluation. After standardizing these indices based on those of normal subjects, they are input to LLGMNs to assess motor function. Then, motor ability is probabilistically discriminated to determine whether it is normal or not using a classifier combined with the output of multiple LLGMNs based on bagging and entropy. This paper reports on evaluation and discrimination experiments performed on finger tapping movements in 33 Parkinson’s disease (PD) patients and 32 normal elderly subjects. The results showed that the patients could be classified correctly in terms of their impairment status with a high degree of accuracy (average rate: 93.1 ± 3.69%) using 12 LLGMNs, which was about 5% higher than the results obtained using a single LLGMN

Adaptation and contextualization of deep neural network models

The ability of Deep Neural Networks (DNNs) to provide very high accuracy in classification and recognition problems makes them the major tool for developments in such problems. It is, however, known that DNNs are currently used in a ‘black box’ manner, lacking transparency and interpretability of their decision-making process. Moreover, DNNs should use prior information on data classes, or object categories, so as to provide efficient classification of new data, or objects, without forgetting their previous knowledge. In this paper, we propose a novel class of systems that are able to adapt and contextualize the structure of trained DNNs, providing ways for handling the above-mentioned problems. A hierarchical and distributed system memory is generated and used for this purpose. The main memory is composed of the trained DNN architecture for classification/prediction, i.e., its structure and weights, as well as of an extracted - equivalent – Clustered Representation Set (CRS) generated by the DNN during training at its final - before the output – hidden layer. The latter includes centroids - ‘points of attraction’ - which link the extracted representation to a specific area in the existing system memory. Drift detection, occurring, for example, in personalized data analysis, can be accomplished by comparing the distances of new data from the centroids, taking into account the intra-cluster distances. Moreover, using the generated CRS, the system is able to contextualize its decision-making process, when new data become available. A new public medical database on Parkinson’s disease is used as testbed to illustrate the capabilities of the proposed architecture

Functional MRI characterization of animal models of parkinsonism

Parkinson's disease (PD) is the second most common neurological disorder. It is characterized by the progressive development of motor symptoms - bradykinesia, resting tremor, muscular rigidity and difficulty with postural control - which serve as criterias for its clinical diagnosis. However, there is a need for biomarkers to detect PD early before the appearance of the symptoms, but also to evaluate efficacy of treatments. Such biomarkers would also to evaluate the translational value of models of the disease. In recent years, magnetic resonance imaging (MRI) has been used by researchers to identify biomarkers of PD in the patients' brain. One MRI method that is gradually becoming more popular is resting-state functional MRI (rs-fMRI). It consists in tracking the activity of brain by acquiring the MRI signal of the brain over time for several minutes while the patient is at rest, i.e. when he/she tries not to think about anything in particular. Compared to task-based fMRI, it is advantageous for studying PD as patients have problems to perform tasks, both because of motor symptoms but also cognitive symptoms which are common in PD. In this thesis, after successfully demonstrating the translational value of rs-fMRI by comparing a set of functional networks in naive Sprague-Dawley and healthy human subjects (paper I), several rat models of parkinsonism were characterized. These models consisted in a well-established model, the unilateral 6-hydroxydopamine (6-OHDA) model (paper II), and two progressive models of parkinsonism, the alpha-synuclein adeno-associated virus overexpression model, a genetic model (paper III), and the β-sitosterol-β-D-glucoside model, a new toxin-based model (paper IV). By acquiring rs-fMRI datasets and analysing them using seed-based correlation analysis, functional connectivity maps were generated. We could reproducibly demonstrate that sensorimotor corticostriatal functional connectivity is increased in the 6-OHDA lesioned animals compared to their control counterparts, while in models with milder parkinsonian pathology, the sensorimotor corticostriatal functional connectivity is decreased. We therefore emit the hypothesis that there is a U-shaped function describing corticostriatal functional connectivity relative to the level of striatal dopaminergic innervation. We also observed in both models of mild parkinsonism a reinforcement of negative functional connectivity between the prefrontal cortex, in particular the orbital cortex, and the primary somatosensory cortex compared to their healthy counterparts. These results demonstrate that rs-fMRI is a valid method to observe alterations in the brain related to parkinsonism in animals and that both motor and non-motor areas of the brain are affected by the loss of dopaminergic neurons. Further investigations must be conducted to understand the mechanisms involved in these changes and evaluate their translational value

Computational Language Assessment in patients with speech, language, and communication impairments

Speech, language, and communication symptoms enable the early detection, diagnosis, treatment planning, and monitoring of neurocognitive disease progression. Nevertheless, traditional manual neurologic assessment, the speech and language evaluation standard, is time-consuming and resource-intensive for clinicians. We argue that Computational Language Assessment (C.L.A.) is an improvement over conventional manual neurological assessment. Using machine learning, natural language processing, and signal processing, C.L.A. provides a neuro-cognitive evaluation of speech, language, and communication in elderly and high-risk individuals for dementia. ii. facilitates the diagnosis, prognosis, and therapy efficacy in at-risk and language-impaired populations; and iii. allows easier extensibility to assess patients from a wide range of languages. Also, C.L.A. employs Artificial Intelligence models to inform theory on the relationship between language symptoms and their neural bases. It significantly advances our ability to optimize the prevention and treatment of elderly individuals with communication disorders, allowing them to age gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite

Estimating tremor in Vocal Fold Biomechanics for Neurological Disease characterisation

Neurological Diseases (ND) are affecting larger segments of aging population every year. Treatment is dependent on expensive accurate and frequent monitoring. It is well known that ND leave correlates in speech and phonation. The present work shows a method to detect alterations in vocal fold tension during phonation. These may appear either as hypertension or as cyclical tremor. Estimations of tremor may be produced by auto-regressive modeling of the vocal fold tension series in sustained phonation. The correlates obtained are a set of cyclicality coefficients, the frequency and the root mean square amplitude of the tremor. Statistical distributions of these correlates obtained from a set of male and female subjects are presented. Results from five study cases of female voice are also given

Multimodal Classification of Parkinson's Disease in Home Environments with Resiliency to Missing Modalities

Parkinson’s disease (PD) is a chronic neurodegenerative condition that affects a patient’s everyday life. Authors have proposed that a machine learning and sensor-based approach that continuously monitors patients in naturalistic settings can provide constant evaluation of PD and objectively analyse its progression. In this paper, we make progress toward such PD evaluation by presenting a multimodal deep learning approach for discriminating between people with PD and without PD. Specifically, our proposed architecture, named MCPD-Net, uses two data modalities, acquired from vision and accelerometer sensors in a home environment to train variational autoencoder (VAE) models. These are modality-specific VAEs that predict effective representations of human movements to be fused and given to a classification module. During our end-to-end training, we minimise the difference between the latent spaces corresponding to the two data modalities. This makes our method capable of dealing with missing modalities during inference. We show that our proposed multimodal method outperforms unimodal and other multimodal approaches by an average increase in F1-score of 0.25 and 0.09, respectively, on a data set with real patients. We also show that our method still outperforms other approaches by an average increase in F1-score of 0.17 when a modality is missing during inference, demonstrating the benefit of training on multiple modalities

CLASSIFICATION OF PARKINSON'S DISEASE IN BRAIN MRI IMAGES USING DEEP RESIDUAL CONVOLUTIONAL NEURAL NETWORK

In our aging culture, neurodegenerative disorders like Parkinson's disease (PD) are among the most serious health issues. It is a neurological condition that has social and economic effects on individuals. It happens because the brain's dopamine-producing cells are unable to produce enough of the chemical to support the body's motor functions. The main symptoms of this illness are eyesight, excretion activity, speech, and mobility issues, followed by depression, anxiety, sleep issues, and panic attacks. The main aim of this research is to develop a workable clinical decision-making framework that aids the physician in diagnosing patients with PD influence. In this research, we proposed a technique to classify Parkinson’s disease by MRI brain images. Initially, normalize the input data using the min-max normalization method and then remove noise from input images using a median filter. Then utilizing the Binary Dragonfly Algorithm to select the features. Furthermore, to segment the diseased part from MRI brain images using the technique Dense-UNet. Then, classify the disease as if it’s Parkinson’s disease or health control using the Deep Residual Convolutional Neural Network (DRCNN) technique along with Enhanced Whale Optimization Algorithm (EWOA) to get better classification accuracy. Here, we use the public Parkinson’s Progression Marker Initiative (PPMI) dataset for Parkinson’s MRI images. The accuracy, sensitivity, specificity, and precision metrics will be utilized with manually gathered data to assess the efficacy of the proposed methodology