Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi

Analysis of gamma-band activity from human EEG using empirical mode decomposition

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand.Universidad de AlcaláInstituto de Salud Carlos II

EMG-based eye gestures recognition for hands free interfacing

This study investigates the utilization of an Electromyography (EMG) based device to recognize five eye gestures and classify them to have a hands free interaction with different applications. The proposed eye gestures in this work includes Long Blinks, Rapid Blinks, Wink Right, Wink Left and finally Squints or frowns. The MUSE headband, which is originally a Brain Computer Interface (BCI) that measures the Electroencephalography (EEG) signals, is the device used in our study to record the EMG signals from behind the earlobes via two Smart rubber sensors and at the forehead via two other electrodes. The signals are considered as EMG once they involve the physical muscular stimulations, which are considered as artifacts for the EEG Brain signals for other studies. The experiment is conducted on 15 participants (12 Males and 3 Females) randomly as no specific groups were targeted and the session was video taped for reevaluation. The experiment starts with the calibration phase to record each gesture three times per participant through a developed Voice narration program to unify the test conditions and time intervals among all subjects. In this study, a dynamic sliding window with segmented packets is designed to faster process the data and analyze it, as well as to provide more flexibility to classify the gestures regardless their duration from one user to another. Additionally, we are using the thresholding algorithm to extract the features from all the gestures. The Rapid Blinks and the Squints were having high F1 Scores of 80.77% and 85.71% for the Trained Thresholds, as well as 87.18% and 82.12% for the Default or manually adjusted thresholds. The accuracies of the Long Blinks, Rapid Blinks and Wink Left were relatively higher with the manually adjusted thresholds, while the Squints and the Wink Right were better with the trained thresholds. However, more improvements were proposed and some were tested especially after monitoring the participants actions from the video recordings to enhance the classifier. Most of the common irregularities met are discussed within this study so as to pave the road for further similar studies to tackle them before conducting the experiments. Several applications need minimal physical or hands interactions and this study was originally a part of the project at HCI Lab, University of Stuttgart to make a hands-free switching between RGB, thermal and depth cameras integrated on or embedded in an Augmented Reality device designed for the firefighters to increase their visual capabilities in the field

Applications of the electric potential sensor for healthcare and assistive technologies

The work discussed in this thesis explores the possibility of employing the Electric Potential Sensor for use in healthcare and assistive technology applications with the same and in some cases better degrees of accuracy than those of conventional technologies. The Electric Potential Sensor is a generic and versatile sensing technology capable of working in both contact and non-contact (remote) modes. New versions of the active sensor were developed for specific surface electrophysiological signal measurements. The requirements in terms of frequency range, electrode size and gain varied with the type of signal measured for each application. Real-time applications based on electrooculography, electroretinography and electromyography are discussed, as well as an application based on human movement. A three sensor electrooculography eye tracking system was developed which is of interest to eye controlled assistive technologies. The system described achieved an accuracy at least as good as conventional wet gel electrodes for both horizontal and vertical eye movements. Surface recording of the electroretinogram, used to monitor eye health and diagnose degenerative diseases of the retina, was achieved and correlated with both corneal fibre and wet gel surface electrodes. The main signal components of electromyography lie in a higher bandwidth and surface signals of the deltoid muscle were recorded over the course of rehabilitation of a subject with an injured arm. Surface electromyography signals of the bicep were also recorded and correlated with the joint dynamics of the elbow. A related non-contact application of interest to assistive technologies was also developed. Hand movement within a defined area was mapped and used to control a mouse cursor and a predictive text interface

Prediction of Digital Eye Strain Due to Online Learning Based on the Number of Blinks

Eye strain is a big concern, especially when it comes to continuous and prolonged online learning. If this is allowed to continue, it will result in Computer Vision Syndrome, also known as Digital Eye Strain (DES), which includes headaches, blurred vision, dry eyes, and even neck and shoulder pain. This condition can be observed either directly based on excessive eye blinking or indirectly based on observations of the electrical activity of eye movements or electrooculography (EOG). The observed blink signal from the EOG, as a representation of eye strain, is the focus of this study. Data acquisition was obtained using the EOG sensor and was carried out on the condition that the participants were conducting online learning activities. There are four different modes of observation taken in succession: when the eye is in a viewing state but without blinking, when the eye blinks intentionally, when the eye is closed, and finally when the eye sees naturally. Observation time is 10s, 20s and 30s, where each interval is performed three times for every mode. The obtained signal is processed by the proposed method. The resulting signal is then labeled as a Blinking signal. Determination of the number of blinks or CNT_PEAK is the result of training this signal by tuning its threshold and width. If the number of blinks is less than or more than 17 then the system will provide a prediction of eye status which is stated in two categories, the first is normal eye while the last is eye strain or fatigue

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

Graphene textiles towards soft wearable interfaces for electroocular remote control of objects

Study of eye movements (EMs) and measurement of the resulting biopotentials, referred to as electrooculography (EOG), may find increasing use in applications within the domain of activity recognition, context awareness, mobile human-computer interaction (HCI) applications, and personalized medicine provided that the limitations of conventional “wet” electrodes are addressed. To overcome the limitations of conventional electrodes, this work, reports for the first time the use and characterization of graphene-based electroconductive textile electrodes for EOG acquisition using a custom-designed embedded eye tracker. This self-contained wearable device consists of a headband with integrated textile electrodes and a small, pocket-worn, battery-powered hardware with real-time signal processing which can stream data to a remote device over Bluetooth. The feasibility of the developed gel-free, flexible, dry textile electrodes was experimentally authenticated through side-by-side comparison with pre-gelled, wet, silver/silver chloride (Ag/AgCl) electrodes, where the simultaneously and asynchronous recorded signals displayed correlation of up to ~87% and ~91% respectively over durations reaching hundred seconds and repeated on several participants. Additionally, an automatic EM detection algorithm is developed and the performance of the graphene-embedded “all-textile” EM sensor and its application as a control element toward HCI is experimentally demonstrated. The excellent success rate ranging from 85% up to 100% for eleven different EM patterns demonstrates the applicability of the proposed algorithm in wearable EOG-based sensing and HCI applications with graphene textiles. The system-level integration and the holistic design approach presented herein which starts from fundamental materials level up to the architecture and algorithm stage is highlighted and will be instrumental to advance the state-of-the-art in wearable electronic devices based on sensing and processing of electrooculograms

Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (

Driving Fatigue Recognition with Functional Connectivity Based on Phase Synchronization

Accumulating evidences showed that the optimal brain network topology was altered with the progression of fatigue during car driving. However, the extent of discriminative power of functional connectivity that contribute to the driving fatigue detection is still unclear. In this study, we extracted two types of features (network properties and critical connections) to explore their usefulness in driving fatigue detection. EEG data were recorded twice from twenty healthy subjects during a simulated driving experiment. Multi-band functional connectivity matrices were established using phase lag index, which serve as input for the following graph theoretical analysis and critical connections determination between the most vigilant and fatigued states. We found a reorganisation of brain network towards less efficient architecture in fatigue state across all frequency bands. Further interrogations showed that the discriminative connections were mainly connected to frontal areas, i.e., most of the increased connections are from frontal pole to parietal or occipital regions. Moreover, we achieved a satisfactory classification accuracy (96.76%) using the discriminative connection features in β band. Our study demonstrated that graph theoretical properties and critical connections are of discriminative power for manifesting fatigue alterations and the critical connection is an efficient feature for driving fatigue detection

Psychophysiology-based QoE assessment : a survey

We present a survey of psychophysiology-based assessment for quality of experience (QoE) in advanced multimedia technologies. We provide a classification of methods relevant to QoE and describe related psychological processes, experimental design considerations, and signal analysis techniques. We summarize multimodal techniques and discuss several important aspects of psychophysiology-based QoE assessment, including the synergies with psychophysical assessment and the need for standardized experimental design. This survey is not considered to be exhaustive but serves as a guideline for those interested to further explore this emerging field of research