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

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

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

A survey of searching and information extraction on a classical text using ontology-based semantics modeling: a case of Quran

Quran is the religious text of Islam. Followers of Islam believe that it is the verbatim word of Allah (God). In the last few years, the Quran has become a target of interest for researchers in the field of computer science, for exploring the divine knowledge encapsulated in it. Since the last few years ontologies have gained significant importance in computer science research because of its machine understandable and semantic nature. Ontologies play an important role in supporting the notion of the semantic web. Some work has been done on the Quran exploiting the platform of ontologies. This paper presents a survey based on recent works which uses ontologies as a means of representing and encapsulating the knowledge of the Quran. In order to compare the reviewed literature, an authentic framework is used which is applicable to any ontology application. Furthermore, the paper includes a comprehensive comparison table based on the framework which allows the readers to understand the details of all works in a glance. At the end of the paper, the conclusion and future work section highlights the shortcomings of the existing works and give a sense of direction to aspiring researchers in order to contribute to the domain of the Quran

Multi-sources data fusion framework for remote triage prioritization in telehealth

The healthcare industry is streamlining processes to offer more timely and effective services to all patients. Computerized software algorithm and smart devices can streamline the relation between users and doctors by providing more services inside the healthcare telemonitoring systems. This paper proposes a multi-sources framework to support advanced healthcare applications. The proposed framework named Multi Sources Healthcare Architecture (MSHA) considers multi-sources: sensors (ECG, SpO2 and Blood Pressure) and text-based inputs from wireless and pervasive devices of Wireless Body Area Network. The proposed framework is used to improve the healthcare scalability efficiency by enhancing the remote triaging and remote prioritization processes for the patients. The proposed framework is also used to provide intelligent services over telemonitoring healthcare services systems by using data fusion method and prioritization technique. As telemonitoring system consists of three tiers (Sensors/ sources, Base station and Server), the simulation of the MSHA algorithm in the base station is demonstrated in this paper. The achievement of a high level of accuracy in the prioritization and triaging patients remotely, is set to be our main goal. Meanwhile, the role of multi sources data fusion in the telemonitoring healthcare services systems has been demonstrated. In addition to that, we discuss how the proposed framework can be applied in a healthcare telemonitoring scenario. Simulation results, for different symptoms relate to different emergency levels of heart chronic diseases, demonstrate the superiority of our algorithm compared with conventional algorithms in terms of classify and prioritize the patients remotely

Kajian penghasilan polipina: the conversion of pineapple leaves to white fabric / Wan Yunus Wan Ahmad … [et al.]

The Malaysian pineapple leaf fibre (PALF) from Yankee type was converted to fabric through fibre scrapping, hand spinning and hand loom weaving. It was scoured and bleached to remove impurities and turn to natural white colour. The project was to reduce pineapple leaves waste in plantation but a lot of works needs to be carried out to speed up the process of conversion to fibre, yarns and fabric. Process of conversion can be much faster and in larger volume by using suitable machineries and trained workers

Fundamental of Entrepreneurship ENT300: Smart Laundry Enterprise / Angela Julika Jilan... [et al.]

A laundry service is a company which does laundry for its clients. There are a number of different styles of laundry service, with varying rates. Laundry services are especially popular with people who are very busy, and with people and organizations which have high volumes of laundry, along with people who simply dislike doing laundry. Many communities have laundry services, which may be listed in the phone book or available through laundromats. In a classic laundry service, laundry is picked up from residences and businesses on a regular schedule. People usually pay by weight for their laundry, with the service washing, drying, and folding the laundry. Additional services like stain treatment. dry cleaning, and ironing may also be offered at some laundry services. It is also possible to drop laundry off with a laundry service. Many laundromats allow customers to drop laundry off for cleaning, also charging by weight. Customers may be able to pick from an a la carte menu which includes folding, ironing, and mending services. These services often offer quick turnaround for customers in a hurry. Some laundry services cater to specific types of customers. Diaper services, for example, just handle cloth diapers for their customers, dropping off a load of freshly laundered diapers every time they pick up a dirty load. College laundry services handle dormitory laundry for college students, which may be limited to sheets and towels owned by the service, or extended to all student laundry. Other laundry services prefer to work with industrial customers like restaurants, inns, hospitals, and animal shelters. Some are specifically certified to handle biohazardous laundry, often charging an extra fee for hazardous substances

Design, build and test IIUM remote controlled glider

The main objective of this subject is to design and build a sailplane that meets certain performance requirement. Simple mission profile was used which is include warm up, taxi, takeoff, climb, cruise and landing. The cruising altitude, range and sailplanes requirements and specification has been decided base on certain criteria. The take off weight of the sailplane has been successfully estimated as 1.225 kg. NACA 4412 airfoil was used for the wing sectiion. The fuselage diensions were estimated. The length of the fuselage from nose to tail is 0.8 meter, and the fuselage cross section will be rectangular in shape. The power available was estimated as 0.2 hp. The static stability analysis shows good results since all three components which are wing fuselage and tail gives desirable stability results. After 13 weeks of hard work, the team had managed to complete the fabrication of sailplane and also had managed to have it flown by a hired RC pilot. The aircraft flew without any difficulty and the objectives was reached, which is to perform an unpowered gliding

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

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

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

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

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities