Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.

Emotion recognition has increased the potential of affective computing by getting an instant feedback from users and thereby, have a better understanding of their behavior. Physiological sensors have been used to recognize human emotions in response to audio and video content that engages single (auditory) and multiple (two: auditory and vision) human senses, respectively. In this study, human emotions were recognized using physiological signals observed in response to tactile enhanced multimedia content that engages three (tactile, vision, and auditory) human senses. The aim was to give users an enhanced real-world sensation while engaging with multimedia content. To this end, four videos were selected and synchronized with an electric fan and a heater, based on timestamps within the scenes, to generate tactile enhanced content with cold and hot air effect respectively. Physiological signals, i.e., electroencephalography (EEG), photoplethysmography (PPG), and galvanic skin response (GSR) were recorded using commercially available sensors, while experiencing these tactile enhanced videos. The precision of the acquired physiological signals (including EEG, PPG, and GSR) is enhanced using pre-processing with a Savitzky-Golay smoothing filter. Frequency domain features (rational asymmetry, differential asymmetry, and correlation) from EEG, time domain features (variance, entropy, kurtosis, and skewness) from GSR, heart rate and heart rate variability from PPG data are extracted. The K nearest neighbor classifier is applied to the extracted features to classify four (happy, relaxed, angry, and sad) emotions. Our experimental results show that among individual modalities, PPG-based features gives the highest accuracy of 78.57 % as compared to EEG- and GSR-based features. The fusion of EEG, GSR, and PPG features further improved the classification accuracy to 79.76 % (for four emotions) when interacting with tactile enhanced multimedia.

Arrhythmia Classification of ECG Signals Using Hybrid Features.

Automatic detection and classification of life-threatening arrhythmia plays an important part in dealing with various cardiac conditions. In this paper, a novel method for classification of various types of arrhythmia using morphological and dynamic features is presented. Discrete wavelet transform (DWT) is applied on each heart beat to obtain the morphological features. It provides better time and frequency resolution of the electrocardiogram (ECG) signal, which helps in decoding important information of a quasiperiodic ECG using variable window sizes. RR interval information is used as a dynamic feature. The nonlinear dynamics of RR interval are captured using Teager energy operator, which improves the arrhythmia classification. Moreover, to remove redundancy, DWT subbands are subjected to dimensionality reduction using independent component analysis, and a total of twelve coefficients are selected as morphological features. These hybrid features are combined and fed to a neural network to classify arrhythmia. The proposed algorithm has been tested over MIT-BIH arrhythmia database using 13724 beats and MIT-BIH supraventricular arrhythmia database using 22151 beats. The proposed methodology resulted in an improved average accuracy of 99.75% and 99.84% for class- and subject-oriented scheme, respectively, using three-fold cross validation.

Sleep Posture Classification using a Convolutional Neural Network.

Sleep is a process of rest and renewal that is vital for humans. However, there are several sleep disorders such as rapid eye movement (REM) sleep behaviour disorder (RBD), sleep apnea, and restless leg syndrome (RLS) that can have an impact on a significant portion of the population. These disorders are known to be associated with particular behaviours such as specific body positions and movements. Clinical diagnosis requires patients to undergo polysomnography (PSG) in a sleep unit as a gold standard assessment. This involves attaching multiple electrodes to the head and body. In this experiment, we seek to develop non-contact approach to measure sleep disorders related to body postures and movement. An Infrared (IR) camera is used to monitor body position unaided by other sensors. Twelve participants were asked to adopt and then move through a set of 12 pre-defined sleep positions. We then adopted convolutional neural networks (CNNs) for automatic feature generation from IR data for classifying different sleep postures. The results show that the proposed method has an accuracy of between 0.76 & 0.91 across the participants and 12 sleepposes with, and without a blanket cover, respectively. The results suggest that this approach is a promising method to detect common sleep postures and potentially characterise sleep disorder behaviours.

Medical Image Analysis using Convolutional Neural Networks: A Review.

The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.

Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset.

A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.

The battle against respiration-induced organ motion in external beam radiotherapy.

The latter 2 decades of the last century have witnessed significant improvements in external beam radiotherapy (EBRT), moved primarily by the advances in imaging modalities and computer-based treatment planning. These advancements lead to introducing the addition of a fourth-dimension, time, to the three-dimensional geometry in EBRT. The new era in EBRT presents challenges and opportunities to compensate for the effect of respiratory-induced target motion and improve treatment output. A number of these methods have been investigated, some of them already clinically approved and some still under development. Thus, there has been an increasing amount of literature in the area of respiratory motion compensation in EBRT. One criticism in most of the literature is that, it is either unorganized, or provides limited information. A few literature reviews provide a comprehensive overview regarding this fast growing area of study. The literature review here will provide an up to date summary of these publications.