Wearable Platform for Automatic Recognition of Parkinson Disease by Muscular Implication Monitoring

The need for diagnostic tools for the characterization of progressive movement disorders - as the Parkinson Disease (PD) - aiming to early detect and monitor the pathology is getting more and more impelling. The parallel request of wearable and wireless solutions, for the real-time monitoring in a non-controlled environment, has led to the implementation of a Quantitative Gait Analysis platform for the extraction of muscular implications features in ordinary motor action, such as gait. The here proposed platform is used for the quantification of PD symptoms. Addressing the wearable trend, the proposed architecture is able to define the real-time modulation of the muscular indexes by using 8 EMG wireless nodes positioned on lower limbs. The implemented system “translates” the acquisition in a 1-bit signal, exploiting a dynamic thresholding algorithm. The resulting 1-bit signals are used both to define muscular indexes both to drastically reduce the amount of data to be analyzed, preserving at the same time the muscular information. The overall architecture has been fully implemented on Altera Cyclone V FPGA. The system has been tested on 4 subjects: 2 affected by PD and 2 healthy subjects (control group). The experimental results highlight the validity of the proposed solution in Disease recognition and the outcomes match the clinical literature results

Quantifying cognitive function in concussed athletes before and after acute exercise using a choice reaction time task

Following a concussion, cognitive deficits have been shown to last longer than symptom resolution. Currently clinicians rely heavily on symptom emergence following the fundamental exercises of the return to play (RTP) protocol, which may leave athletes at risk of returning to play too early if cognitive deficits have not been detected. The purpose of this study was to assess the effects of exercise on choice reaction time (CRT) both at rest and following an acute exercise in 3 populations: non-concussed (NC), recently concussed (RC), and post-concussion syndrome (PCS) individuals. A CRT task in the form of an iPad application measured each individual’s decision-making capabilities at four blocks: (1) 10 minutes prior to exercise, (2) Immediately prior to exercise, (3) immediately post exercise, and (4) 5 minutes post exercise. Participants were also fitted with an eye-tracking system during CRT task performance at rest in order to assess higher levels of cognitive processing. Results demonstrated a facilitative effect of learning and exercise arousal on CRT task performance in both NC and PCS but not in RC. Average RT in RC was not significantly different from NC while average RT in PCS was found to be significantly higher than NC. Gaze behaviour was significantly worse in PCS compared to NC while RC and NC were not significantly different. The absence of symptoms does not inherently mean that cognitive performance under acute physical stress has completely recovered in recently concussed individuals. On the other hand, PCS individuals continue to experience concussion-related symptoms, but appear to display partially recovered cognitive performance. Findings from the current study encourage the use of cognitive assessments following acute exercise during the RTP protocol in order to detect possibly lingering cognitive deficits

Acute effects of radiofrequency electromagnetic field emitted by mobile phone on brain function

Due to its attributes, characteristics and technological resources, mobile phone (MP) has become one of the most commonly used communication devices. Historically, ample evidence has ruled out the substantial short-term impact of radiofrequency electromagnetic field (RF-EMF) emitted by MP on human cognitive performance. However, more recent evidence suggests the potential harmful effects associated with MP EMF exposure. The aim of this review is to readdress the question of whether the effect of MP EMF exposure on brain function should be reopened. We strengthen our argument focusing on recent neuroimaging and electroencephalography studies, in order to present a more specific analysis of effects of MP EMF exposure on neurocognitive function. Several studies indicate an increase in cortical excitability and/or efficiency with EMF exposure, which appears to be more prominent in fronto- temporal regions and has been associated with faster reaction time. Cortical excitability might also underpin disruption to sleep. Notably however, several inconsistent findings exist, and conclusions regarding adverse effects of EMF exposure are currently limited. It also should be noted that the crucial scientific question of the effect of longer-term MP EMF exposure on brain function remains unanswered and essentially unaddressed

Neurocognitive impairment following central nervous system infections in Kenyan children as detected by event related potentials

As mortality in childhood decreases due to advances in modern medicine, presence of better nutrition and fresh water supply, the impact of disability has become increasingly important especially in resource poor countries. Children living in sub- Saharan Africa are also exposed to a number of potentially debilitating infections which have been shown to have long-term cognitive effects even in absence of clinical neurological sequelae. The objective of the study is to demonstrate that event related potentials (ERPs) can be used to detect neurocognitive impairment following the most common central nervous (CNS) system infections affecting children in sub-Saharan Africa, namely falciparum malaria, acute bacterial meningitis (ABM) and human immune-deficiency virus (HIV). Four groups of children were recruited: children previously admitted with severe falciparum malaria (n= 50), or acute bacterial meningitis (n = 65), or mY-infected (n= 39) or were unexposed to any of these conditions (n= 177). Passive auditory and visual oddball ERP protocols were used. The results of the group of 50 children aged 6-7 years old with a history of severe falciparum malaria (cerebral malaria, CM= 27, malaria plus seizures, M/S= 14 and prostrated malaria, PM= 9) show that children exposed to CM, MIS and PM had significantly longer auditory N200 and P3a latencies and smaller N200 amplitudes than study controls. The results of 65 children aged between 4-15 years old with a history of pneumococcal meningitis shmved that children with a history of bacterial meningitis had significantly smaller auditory P100 amplitudes, longer N200 latencies and longer visual P200 latencies than community controls. Finally, the results of 40 children aged between 18-40 months infected with IllV showed that they had longer auditory P100 latencies, larger auditory P200 amplitudes and smaller Negative component, Nc, amplitudes than community controls. It is concluded that the CNS infections may result in neuro-developmental delays in childhood. Further, CNS infections may interfere with normal education outcomes by precipitating attention deficit amongst children post infection

Strategies that shape perception

In recent years there has been an increased focus on individual differences. Such differences have been observed in conditions where people display performance deficits, such as developmental prosopagnosia (McConachie, 1976), in conditions where subjects demonstrate enhanced skills, such as synesthesia (Terhune et al., 2013), as well as in neurotypical individuals, for instance, in the form of subtle individual differences in visual perception (Zelazny & Sørensen, 2020). Our interaction with the environment during brain maturation shapes how perceptual strategies are formed and prioritized. One of the principal tasks for the brain during this stage is to establish templates and context frames in long-term memory. These templates and context frames serve as the basis for various perceptual strategies used to interpret sensory information. Over time, these templates are updated in light of both sensory evidence and the perceptual strategies that have proven advantageous. Successful strategies thus have a greater likelihood of being used in the future, hence shaping our perceptual strategic preferences. In the well-known case of AB, who was afflicted with developmental prosopagnosia (McConachie, 1976), there is evidence to suggest that she prioritized peoples’ clothing as a strategy for recognition over the more common one of relying on facial features. Similarly, grapheme-color synesthesia may develop as a strategy for learning the alphabet. Here, a common strategy may be to associate the abstract letter shapes with previously established color categories in an attempt to aid letter recognition (Brogaard & Sørensen, in press). If this particular strategy is sufficiently prioritized, this may result in grapheme-color synesthesia (cf. Mannix & Sørensen, in press). Here, we argue that individual variability in visual perception reflects differences in perceptual strategies. An interesting consequence of this thesis is that perceptual experience is likely to vary considerably more across individuals than hitherto assumed

AUTOMATED ARTIFACT REMOVAL AND DETECTION OF MILD COGNITIVE IMPAIRMENT FROM SINGLE CHANNEL ELECTROENCEPHALOGRAPHY SIGNALS FOR REAL-TIME IMPLEMENTATIONS ON WEARABLES

Electroencephalogram (EEG) is a technique for recording asynchronous activation of neuronal firing inside the brain with non-invasive scalp electrodes. EEG signal is well studied to evaluate the cognitive state, detect brain diseases such as epilepsy, dementia, coma, autism spectral disorder (ASD), etc. In this dissertation, the EEG signal is studied for the early detection of the Mild Cognitive Impairment (MCI). MCI is the preliminary stage of Dementia that may ultimately lead to Alzheimers disease (AD) in the elderly people. Our goal is to develop a minimalistic MCI detection system that could be integrated to the wearable sensors. This contribution has three major aspects: 1) cleaning the EEG signal, 2) detecting MCI, and 3) predicting the severity of the MCI using the data obtained from a single-channel EEG electrode. Artifacts such as eye blink activities can corrupt the EEG signals. We investigate unsupervised and effective removal of ocular artifact (OA) from single-channel streaming raw EEG data. Wavelet transform (WT) decomposition technique was systematically evaluated for effectiveness of OA removal for a single-channel EEG system. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT), is studied with four WT basis functions: haar, coif3, sym3, and bior4.4. The performance of the artifact removal algorithm was evaluated by the correlation coefficients (CC), mutual information (MI), signal to artifact ratio (SAR), normalized mean square error (NMSE), and time-frequency analysis. It is demonstrated that WT can be an effective tool for unsupervised OA removal from single channel EEG data for real-time applications.For the MCI detection from the clean EEG data, we collected the scalp EEG data, while the subjects were stimulated with five auditory speech signals. We extracted 590 features from the Event-Related Potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with Radial Basis Kernel (RBF) (sigma = 10, cost = 102). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI. We also developed a single channel Electro-encephalography (EEG) based MCI severity monitoring algorithm by generating the Montreal Cognitive Assessment (MoCA) scores from the features extracted from EEG. We performed multi-trial and single-trail analysis for the algorithm development of the MCI severity monitoring. We studied Multivariate Regression (MR), Ensemble Regression (ER), Support Vector Regression (SVR), and Ridge Regression (RR) for multi-trial and deep neural regression for the single-trial analysis. In the case of multi-trial, the best result was obtained from the ER. In our single-trial analysis, we constructed the time-frequency image from each trial and feed it to the convolutional deep neural network (CNN). Performance of the regression models was evaluated by the RMSE and the residual analysis. We obtained the best accuracy with the deep neural regression method