Analysis of physiological signals for recognition of boredom,pain, and surprise emotions

Background

The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals.

Methods

Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions.

Results

The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7 % is obtained by using DFA.

Conclusions

This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.     

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.     

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.     

An integrative and functional framework for the study of animal emotion and mood

A better understanding of animal emotion is an important goal in disciplines ranging from neuroscience to animal welfare science. The conscious experience of emotion cannot be assessed directly, but neural, behavioural and physiological indicators of emotion can be measured. Researchers have used these measures to characterize how animals respond to situations assumed to induce discrete emotional states (e.g. fear). While advancing our understanding of specific emotions, this discrete emotion approach lacks an overarching framework that can incorporate and integrate the wide range of possible emotional states. Dimensional approaches that conceptualize emotions in terms of universal core affective characteristics (e.g. valence (positivity versus negativity) and arousal) can provide such a framework. Here, we bring together discrete and dimensional approaches to: (i) offer a structure for integrating different discrete emotions that provides a functional perspective on the adaptive value of emotional states, (ii) suggest how long-term mood states arise from short-term discrete emotions, how they also influence these discrete emotions through a bi-directional relationship and how they may function to guide decision-making, and (iii) generate novel hypothesis-driven measures of animal emotion and mood.     

Voice Pathologies Classification and Detection Using EMD-DWT Analysis Based on Higher Order Statistic Features

BackgroundThe voice is a prominent tool allowing people to communicate and to change information in their daily activities. However, any slight alteration in the voice production system may affect the voice quality. Over the last years, researchers in biomedical engineering field worked to develop a robust automatic system that may help clinicians to perform a preventive diagnosis in order to detect the voice pathologies in an early stage.MethodIn this context, pathological voice detection and classification method based on EMD-DWT analysis and Higher Order Statistics (HOS) features, is proposed. Also DWT coefficients features are extracted and tested. To carry out our experiments a wide subset of voice signal from normal subjects and subjects which suffer from the five most frequent pathologies in the Saarbrücken Voice Database (SVD), is selected. In The first step, we applied the Empirical Mode Decomposition (EMD) to the voice signal. Afterwards, among the obtained candidates of Intrinsic Mode Functions (IMFs), we choose the robust one based on temporal energy criterion. In the second step, the selected IMF was decomposed via the Discrete Wavelet Transform (DWT). As a result, two features vector includes six HOSs parameters, and a features vector includes six DWT features were formed from both approximation and detail coefficients. In order to classify the obtained data a support vector machine (SVM) is employed. After having trained the proposed system using the SVD database, the system was evaluated using voice signals of volunteer's subjects from the Neurological department of RABTA Hospital of Tunis.ResultsThe proposed method gives promising results in pathological voices detection. The accuracies reached 99.26% using HOS features and 93.1% using DWT features for SVD database. In the classification, an accuracy of 100% was reached for “Funktionelle Dysphonia vs. Rekrrensparese” based on HOS features. Nevertheless, using DWT features the accuracy achieved was 90.32% for “Hyperfunktionelle Dysphonia vs. Rekurrensparse”. Furthermore, in the validation the accuracies reached were 94.82%, 91.37% for HOS and DWT features, respectively. In the classification the highest accuracies reached were for classifying “Parkinson versus Paralysis” 94.44% and 88.87% based on HOS and DWT features, respectively.ConclusionHOS features show promising results in the automatic voice pathology detection and classification compared to DWT features. Thus, it can reliably be used as noninvasive tool to assist clinical evaluation for pathological voices identification.     

Melatonin: A hormone that modulates pain

AimsMelatonin is a hormone synthesized principally in the pineal gland that has been classically associated with endocrine actions. However, several lines of evidence suggest that melatonin plays a role in pain modulation. This paper reviews the available evidence on melatonin's analgesic effects in animals and human beings.Main methodsA medline search was performed using the terms “melatonin”, “inflammatory pain”, “neuropathic pain”, “functional pain”, “rats”, “mice”, “human”, “receptors”, “opioid” and “free radicals” in combinations.Key findingsThe antinociceptive effect of melatonin has been evaluated in diverse pain models, and several findings show that melatonin receptors modulate pain mechanisms as activation induces an antinociceptive effect at spinal and supraspinal levels under conditions of acute and inflammatory pain. More recently, melatonin induced-antinociception has been extended to neuropathic pain states. This effect agrees with the localization of melatonin receptors in thalamus, hypothalamus, dorsal horn of the spinal cord, spinal trigeminal tract, and trigeminal nucleus. The effects of melatonin result from activation of MT₁ and MT₂ melatonin receptors, which leads to reduced cyclic AMP formation and reduced nociception. In addition, melatonin is able to activate opioid receptors indirectly, to open several K⁺ channels and to inhibit expression of 5-lipoxygenase and cyclooxygenase 2. This hormone also inhibits the production of pro-inflammatory cytokines, modulates GABA_A receptor function and acts as a free radical scavenger.SignificanceMelatonin receptors constitute attractive targets for developing analgesic drugs, and their activation may prove to be a useful strategy to generate analgesics with a novel mechanism of action.     

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown”. This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.     

A Comparative Approach for MI-Based EEG Signals Classification Using Energy,Power and Entropy

ObjectiveThe initial principal task of a Brain-Computer Interfacing (BCI) research is to extract the best feature set from a raw EEG (Electroencephalogram) signal so that it can be used for the classification of two or multiple different events. The main goal of the paper is to develop a comparative analysis among different feature extraction techniques and classification algorithms.Materials and methodsIn this present investigation, four different methodologies have been adopted to classify the recorded MI (motor imagery) EEG signal, and their comparative study has been reported. Haar Wavelet Energy (HWE), Band Power, Cross-correlation, and Spectral Entropy (SE) based Cross-correlation feature extraction techniques have been considered to obtain the necessary features set from the raw EEG signals. Four different machine learning algorithms, viz. LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), Naïve Bayes, and Decision Tree, have been used to classify the features.ResultsThe best average classification accuracies are 92.50%, 93.12%, 72.26%, and 98.71% using the four methods. Further, these results have been compared with some recent existing methods.ConclusionThe comparative results indicate a significant accuracy level performance improvement of the proposed methods with respect to the existing one. Hence, this presented work can guide to select the best feature extraction method and the classifier algorithm for MI-based EEG signals.     

Affect induction through musical sounds: an ethological perspective

How does music induce or evoke feeling states in listeners? A number of mechanisms have been proposed for how sounds induce emotions, including innate auditory responses, learned associations and mirror neuron processes. Inspired by ethology, it is suggested that the ethological concepts of signals, cues and indices offer additional analytic tools for better understanding induced affect. It is proposed that ethological concepts help explain why music is able to induce only certain emotions, why some induced emotions are similar to the displayed emotion (whereas other induced emotions differ considerably from the displayed emotion), why listeners often report feeling mixed emotions and why only some musical expressions evoke similar responses across cultures.     

Upper extremity biomechanics in computer tasks differ by gender

ObjectivesThis laboratory study examined gender differences in upper extremity postures, applied forces, and muscle activity when a computer workstation was adjusted to individual anthropometry according to current guidelines.MethodsFifteen men and 15 women completed five standardized computer tasks: touch-typing, completing a form, editing text, sorting and resizing graphical objects and navigating intranet pages. Subjects worked at a height-adjustable workstation with the keyboard on top of the work surface and the mouse to the right. Subjects repeated the text editing task with the mouse in two other locations: a “high” mouse position, which simulated using a keyboard drawer with the mouse on the primary work surface, and “center” mouse position with the mouse between the keyboard and the body, centered with the body’s center line. Surface electromyography measured muscle activity; electrogoniometric and magnetic motion analysis system measured wrist, forearm and upper arm postures; load-cells measured typing forces; and a force-sensing mouse measured applied forces.ResultsRelative forces applied to the keyboard, normalized muscle activity of two forearm muscles, range of motion for the wrist and shoulder joints and external rotation of the shoulder were higher for women (p < 0.05). When subjects were dichotomized instead by anthropometry (either large/small shoulder width or arm length), the differences in forces, muscle activity of the shoulder and wrist posture and shoulder posture became more pronounced with smaller subjects having higher values. Postural differences between the genders increased in the high mouse position and decreased in the center mouse location.ConclusionsWhen a workstation is adjusted per current guidelines differences in upper extremity force, muscle activity and postural factors still exist between genders. However, these were often stronger when subjects were grouped by anthropometry suggesting that perhaps the computer input devices themselves should be scaled to be more in proportion with the anthropometry and strength of the user.     

Optimization assisted framework for thyroid detection and classification: A new ensemble technique

Ultrasound (US) is an inexpensive and non-invasive technique for capturing the image of the thyroid gland and nearby tissue. The classification and detection of thyroid disorders is still in its infant stage. This study aims to present a new thyroid diagnosis approach, which consists of three phases like “(i) feature extraction, (ii) feature dimensionality reduction, and (iii) classification”. Initially, the thyroid images as well as its related data are given as input. From the input image, the features such as“ Grey Level Co-occurrence Matrix(GLCM), Grey level Run Length Matrix(GLRM), proposed Local Binary Pattern(LBP), and Local Tetra Patterns (LTrP)” are extracted. Meanwhile, from the input data, the higher-order statistical features like skewness, kurtosis, entropy, as well as moment get retrieved. Consequently, the Linear Discriminant Analysis (LDA) based dimensionality reduction is processed to resolve the problem of “curse of dimensionality”. Finally, the classification is carried out via two phases: Image features are classified using an ensemble classifier that includes Support Vector Machine (SVM)& Neural Network(NN) models. The data features are subjected to Recurrent Neural Network(RNN) based classification, which is optimized by an Adaptive Elephant Herding Algorithm (AEHO) via tuning the optimal weight. At last, the performance of the adopted scheme is compared to the extant models in terms of various measures. Especially, the mean value of the suggested RNN + AEHO model is 4.35%, 3.54%, 6.07%, 3.8%, 1.69%, 2.85%, 2.07%, 2.54%, 0.13%, 0.035%, and 8.53% better than the existing CNN, NB, RF, KNN, Levenberg, RNN + EHO, RNN + FF, RNN + WOA, WF-CS, FU-SLnO and HFBO methods respectively.     

Ultrasonography in the diagnosis of asymptomatic hyperuricemia and gout

ABSTRACT

Sonography has detected urate deposits in 34%–42% of the patients with asymptomatic hyperuricemia. This may prompt reclassification of asymptomatic hyperuricemia into “asymptomatic gout” and consideration of urate lowering therapy (ULT) to resolve urate deposits. In patients with gout and no visible tophi, sonography has detected urate deposits in half of the patients. This may allow diagnosing “tophaceous gout” and influencing the serum urate target level, prophylaxis to avoid acute gout flares during ULT, and clinical follow-up. Current accessibility to sonography may better classify patients with hyperuricemia and gout and contribute to delineate therapeutic objectives and clinical guidance.     

Evolutionary explanations of emotions

Emotions can be explained as specialized states, shaped by natural selection, that increase fitness in specific situations. The physiological, psychological, and behavioral characteristics of a specific emotion can be analyzed as possible design features that increase the ability to cope with the threats and opportunities present in the corresponding situation. This approach to understanding the evolutionary functions of emotions is illustrated by the correspondence between (a) the subtypes of fear and the different kinds of threat; (b) the attributes of happiness and sadness and the changes that would be advantageous in propitious and unpropitious situations; and (c) the social emotions and the adaptive challenges of reciprocity relationships. In addition to addressing a core theoretical problem shared by evolutionary and cognitive psychology, explicit formulations of the evolutionary functions of specific emotions are of practical importance for understanding and treating emotional disorders.     

A review of dose calculation approaches with cone beam CT in photon and proton therapy

Background and purposeThe use of cone beam computed tomography (CBCT) for performing dose calculations in radiation therapy has been widely investigated as it could provide a quantitative analysis of the dosimetric impact of changes in patients during the treatment. The aim of this review was to classify different techniques adopted to perform CBCT dose calculation and to report their dosimetric accuracy with respect to the metrics used.Methods and materialsA literature search was carried out in PubMed and ScienceDirect databases, based upon the following keywords: “cone beam computed tomography”, “CBCT”, “cone beam CT”, “dose calculation”, “accuracy”. Sixty-nine peer-reviewed relevant articles were included in this review: thirty-one patient studies, fifteen phantom studies and twenty-three patient & phantom studies. Most studies were found to have focused on head and neck, lung and prostate cancers.ResultsThe techniques adopted to perform CBCT dose calculation have been grouped in six categories labelled as (1) pCT calibration, (2) CBCT calibration, (3) HU override, (4) Deformable image registration, (5) Dose deformation, and (6) Combined techniques. Differences between CBCT dose and reference dose were reported both for target volumes and OARs.ConclusionsA comparison among the available techniques for CBCT dose calculations is challenging as many variables are involved. Therefore, a set of reporting standards is recommended to enable meaningful comparisons among different studies. The accuracy of the results was strongly dependent on the image quality, regardless of the methods used, highlighting the need for dose validation and quality assurance standards.     

Association of ABO and Rh blood groups with breast cancer

ObjectivesThe aim of this study was to determine the association of “ABO” and “Rhesus” blood groups with incidence of breast cancer.MethodsIn this study, we identified 70 research documents from data based search engines including “PubMed”, “ISI-Web of Knowledge”, “Embase” and “Google Scholar”. The research papers were selected by using the primary key-terms including “ABO blood type”, “Rhesus” blood type and “breast cancer”. The research documents in which “ABO” and “Rhesus” blood types and breast cancer was debated were included. After screening, we reviewed 32 papers and finally we selected 25 research papers which met the inclusion criteria and remaining documents were excluded.ResultsBlood group “A” has high incidence of breast cancer (45.88%), blood group “O” has (31.69%); “B” (16.16%) and blood group “AB” has (6.27%) incidence of breast cancer. Blood group “A” has highest and blood group “AB” has least association with breast cancer. Furthermore, “Rhesus +ve” blood group has high incidence of breast cancer (88.31%) and “Rhesus –ve” blood group has least association with breast cancer (11.68%).ConclusionBlood group “A” and “Rhesus +ve” have high risk of breast cancer, while blood type “AB” and “Rhesus –ve” are at low peril of breast cancer. Physicians should carefully monitor the females with blood group “A” and “Rh +ve” as these females are more prone to develop breast cancer. To reduce breast cancer incidence and its burden, preventive and screening programs for breast cancer especially in young women are highly recommended.     

Actividad de la micafungina contra las biopelículas de Candida

BackgroundMost recalcitrant infections are associated to colonization and microbial biofilm development. These biofilms are difficult to eliminate by the immune response mechanisms and the current antimicrobial therapy.AimTo describe the antifungal of micafungin against fungal biofilms based in the scientific and medical literature of recent years.MethodsWe have done a bibliographic retrieval using the scientific terms “micafungin”, “activity”, “biofilm”, “Candida”, “Aspergillus”, “fungi”, “mycos”*, susceptibility, in PubMed/Medline from the National Library of Medicine from 2006 to 2009.ResultsMost current antifungal agents (amphotericin B and fluconazole) and the new azole antifungals have no activity against fungal biofilms. However, micafungin and the rest of echinocandins are very active against Candida albicans, Candida dubliniensis, Candida glabrata, and Candida krusei biofilms but their activities are variable and less strong against Candida tropicalis and Candida parapsilosis biofilms. Moreover, they have not activities against the biofilms of Cryptococcus y Trichosporon.ConclusionsThe activity of micafungin against Candida biofilms gives more strength to its therapeutic indication for candidaemia and invasive candidiasis associated to catheter, prosthesis and other biomedical devices.     

Variability in the mating calls of the Lusitanian toadfish Halobatrachus didactylus: cues for potential individual recognition

The mating sounds (boatwhistles) of nesting batrachoidid Halobatrachus didactylus males were recorded in the Tagus Estuary from piers. Thirteen males with 16 boatwhistles per fish were analysed for 20 acoustic features. All variables showed larger between‐male than within‐male variation and differed significantly among individuals. Discriminant function analyses (DFA) considering seven of these variables assigned 90–100% of boatwhistles to the correct individual, depending on the number of males and number of sounds per male included in the model. The acoustic features that consistently best discriminated individuals were the dominant frequency of the middle tonal segment of the boatwhistle (P₂) and dominant frequency modulation, followed by P₂ pulse period, amplitude modulation and sound duration. These results suggest the possibility of individual recognition based on acoustic cues.     

Individualization Through Standardization: Electronic Orders for Subcutaneous Insulin in the Hospital

ObjectiveThe objective was to design electronic order sets that would promote safe, effective, and individualized order entry for subcutaneous insulin in the hospital, based on a review of best practices.MethodsSaint Francis Hospital in Evanston, Illinois, a community teaching hospital, was selected as the pilot site for 6 hospitals in the Health Care System to introduce an electronic medical record. Articles dealing with man agement of hospital hyperglycemia, medical order entry systems, and patient safety were reviewed selectively.ResultsIn the published literature on institutional glycemic management programs and insulin order sets, features were identified that improve safety and effectiveness of subcutaneous insulin therapy. Subcutaneous electronic insulin order sets were created, designated in short: “patients eating”, “patients not eating”, and “patients receiving overnight enteral feedings.” Together with an option for free text entry, menus of administra tion instructions were designed within each order set that were applicable to specific insulin orders and expressed in standardized language, such as “hold if tube feeds stop” or “do not withhold.”ConclusionTwo design features are advocated for electronic order sets for subcutaneous insulin that will both standardize care and protect individualization. First, within the order sets, the glycemic management plan should be matched to the carbohydrate exposure of the patients, with juxtaposition of appropriate orders for both glucose monitoring and insulin. Second, in order to convey precautions of insulin use to pharmacy and nursing staff, the prescriber must be able to attach administration instructions to specific insulin orders. (Endocr Pract. 2012;18:976-987)     

A New Design of Mental State Classification for Subject Independent BCI Systems

BackgroundBrain Computer Interface (BCI) systems have been widely used to develop sustainable assistive technology for people suffering from neurological impairments. A major limitation of current BCI systems is that they are based on Subject-dependent (SD) concept. The SD based BCI system is time consuming and inconvenient for physical or mental disables people and also not suitable for limited computer resources. In order to overcome these problems, recently subject-independent (SI) based BCI concept has been introduced to identify mental states of motor disabled people but the expected outcome of the SI based BCI has not been achieved yet. Hence this paper intends to present an efficient scheme for SI based BCI system. The goal of this research is to develop a method for classifying mental states which can be used by any user. For attaining this target, this study employs a supervised spatial filtering method with four types of feature extraction methods including Katz Fractal Dimension, Sub band Energy, Log Variance and Root Mean Square (RMS) and finally the obtained features are used as input to Linear Discriminant Analysis (LDA) classification model for identifying mental states for SI BCI system.ResultsThe performance of the proposed design is evaluated in several ways such as considering different time window length; different frequency bands; different number of channels. The mean classification accuracy using Katz feature is 84.35% which is the maximum output compare to other features that outperforms the existing methods.ConclusionsOur proposed design will help to make a new technology for development of real-time SI based BCI systems that can be more supportive for the motor disabled patients.     

Electron cryo-microscopy for elucidating the dynamic nature of live-protein complexes

BackgroundRecent innovations in the field of electron cryo-microscopy (cryo-EM) have achieved structure determination of target macromolecules at “near-atomic resolution”. In addition, cryo-EM has the potential to deal with proteins in multiple conformations in close to physiological conditions.Scope of reviewThis is an overview of the latest technical and methodological developments in cryo-EM, especially key features for elucidating the dynamic nature of specimens.Major conclusionsIt is now possible to elucidate “near-atomic resolution” structures by selecting “well-behaved” particles in Single Particle Analysis (SPA) of cryo-EM images.General significanceCurrently, cryo-EM is utilized actively as an alternative to X-ray crystallography to obtain high-resolution structures of macromolecules, especially those which are hard to crystalize. One possible reason for difficulty in crystallization could be the dynamic nature of the specimen. New developments in the field of cryo-EM will make it possible to deal with specimen heterogeneity and reveal the dynamic nature of specimens.