Objective: Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity. Approach: Emotional EEG data were obtained from 20 PD patients and 20 healthy age-, gender-and education level-matched controls by inducing the six basic emotions of happiness, sadness, fear, anger, surprise and disgust using multimodal (audio and visual) stimuli. In addition, participants were asked to report their subjective affect. Because of the nonlinear and dynamic nature of EEG signals, we utilized higher order spectral features (specifically, bispectrum) for analysis. Two different classifiers namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used to investigate the performance of the HOS based features to classify each of the six emotional states of PD patients compared to HC. Ten-fold cross-validation method was used for testing the reliability of the classifier results.Main Results: From the experimental results with our EEG data set, we found that (a) classification performance of bispectrum features across ALL frequency bands is better than individual frequency bands in both the groups using SVM classifier; (b) higher frequency band plays a more important role in emotion activities than lower frequency band; and (c) PD patients showed emotional impairments compared to HC, as demonstrated by a lower classification performance, particularly for negative emotions (sadness, fear, anger and disgust). Significance:These results demonstrate the effectiveness of applying EEG features with machine learning techniques to classify the each emotional state difference of PD patients compared to HC, and BackgroundSocial communication and the ability to respond to emotional signals are essential for meaningful interpersonal interactions. While Parkinson's disease (PD) has traditionally been defined as a motor system disorder (in the form of tremors, rigidity, and bradykinesia) [1], there is growing evidence of cognitive and social deficits for people associated with this disease [2,3].Non-motor symptoms, including disruptions in processing of emotional information [4,5] have been found in over 50% of newly diagnosed PD patients [6] and can appear in any stage of disease progression [7]. Interestingly, social cognitive dysfunction has been found before the appearance of motor disturbances in PD [8].Individuals with PD show impairments in the ability to recognize emotions from facial expressions [9,10], emotional prosody [11,12] and show reduced startle reactivity to highly arousing unpleasant pictures [13,14]. There is sparse event related potential (ERP) evidence that early processing of emotional prosody (mismatch nega...

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 as 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.Multi-modal 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 the 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.

ObjectiveWhile Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.MethodEEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli.ResultsBehaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli.ConclusionThese distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

BackgroundEffective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.ResultsThis study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy.ConclusionsThe results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.

BackgroundVolatile organic compounds (VOCs) emitted from exhaled breath from human bodies have been proven to be a useful source of information for early lung cancer diagnosis. To date, there are still arguable information on the production and origin of significant VOCs of cancer cells. Thus, this study aims to conduct in-vitro experiments involving related cell lines to verify the capability of VOCs in providing information of the cells.MethodThe performances of e-nose technology with different statistical methods to determine the best classifier were conducted and discussed. The gas sensor study has been complemented using solid phase micro-extraction-gas chromatography mass spectrometry. For this purpose, the lung cancer cells (A549 and Calu-3) and control cell lines, breast cancer cell (MCF7) and non-cancerous lung cell (WI38VA13) were cultured in growth medium.ResultsThis study successfully provided a list of possible volatile organic compounds that can be specific biomarkers for lung cancer, even at the 24th hour of cell growth. Also, the Linear Discriminant Analysis-based One versus All-Support Vector Machine classifier, is able to produce high performance in distinguishing lung cancer from breast cancer cells and normal lung cells.ConclusionThe findings in this work conclude that the specific VOC released from the cancer cells can act as the odour signature and potentially to be used as non-invasive screening of lung cancer using gas array sensor devices.