57,715 research outputs found

    Relationships between cognitive status, speech impairment and communicative participation in Parkinson’s disease

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    Aim: To assess the relationships between cognitive status, speech impairment and communicative participation in Parkinson’s disease. Introduction: Speech and communication difficulties, as well as cognitive impairment, are prevalent in Parkinson’s. The contributions of cognitive impairment and acoustic speech characteristics remain equivocal. Relationships between Impairment and Participation levels of the International Classification of Functioning, Disability and Health (ICF) have not been thoroughly investigated. Methods: 45 people with Parkinson’s and 29 familiar controls performed read, mood and conversational speech tasks as part of a multimethod investigation. Data analysis formed three main parts. Depression, cognition and communication were assessed using questionnaires. Phonetic analysis was used to produce an acoustic characterisation of speech. Listener assessment was used to assess conveyance of emotion and intelligibility. Qualitative Content Analysis was used to provide a participant’s insight into speech and communicative difficulties associated with Parkinson’s disease. Results: Cognitive status was significantly associated with certain read speech acoustic characteristics, emotional conveyance and communicative participation. No association was found with intelligibility or conversational speech acoustic characteristics. The only acoustic speech characteristics that predicted intelligibility were intensity and pause in the read speech condition. The contribution of intelligibility to communicative participation was modest. People with Parkinson’s disease reported a range of psychosocial, cognitive and physical factors affecting their speech and communication. Conclusions: I provide evidence for a role for cognitive status in emotional conveyance and communicative participation, but not necessarily general speech production, in Parkinson’s disease. I demonstrate that there may not be a strong relationship between ICF Impairment level speech measures and functional measures of communication. I also highlight the distinction between measures of communication at the ICF Activity and Participation levels. This study demonstrates that reduced participation in everyday communication in Parkinson’s disease appears to result from a complex interplay of physical, cognitive and psychosocial factors. Further research is required to apply these findings to contribute to future advances in speech and language therapy for Parkinson’s disease

    Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

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    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

    On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

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    Objective: While 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. Method: EEG 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. Results: Behaviorally, 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. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients

    Family Interaction Styles of Children with Depressive Disorders, Schizophrenia-Spectrum Disorders, and Normal Controls

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    Family interaction processes during a problem-solving task were examined in children with depressive disorders, children with schizophrenia-spectrum disorders, and a normal control group of community children screened for the absence of psychiatric disorder. Major findings were: a) children with depressive disorders were more likely than children with schizophrenia-spectrum disorders and children with no psychiatric disorder to direct guilt-inducing comments toward their parents; and b) parents of children with schizophrenia-spectrum disorders were more likely to direct harsh critical comments toward the child than were parents of depressed children or parents of normal controls. In addition, children\u27s and mothers\u27 use of benign criticism was linked, while children\u27s harsh criticism was associated with intrusion from the father, and children\u27s self-denigrating comments were related to specific paternal criticism. Implications of these results for understanding transactional processes associated with childhood-onset depressive and schizophrenia-spectrum disorders are discussed

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

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    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

    Emotions in context: examining pervasive affective sensing systems, applications, and analyses

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    Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

    Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

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    Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders
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