A thermal video database for studying deception in real life.

In this paper, we have developed a database based on almost real-life theft incidents with due diligence using isolated subjects over a period of time at a government hospital under the plea of free health checkup. The experiment has been conducted at Midnapore Medical College and Hospital, West Bengal, India, with proper ethical committee approval. The participants are selected at the behest of the police department with habitual crime records. Most of them have been repeatedly charged with petty crimes of pick-pocketing and stealing. They are invited individually at different instances of time under the plea of medical checkup where they have been enticed to steal cash. It is followed by a two-stage process, a friendly interaction followed by a slightly tougher interrogation. Facial thermal imaging could be more effective as it is noninvasive and could be a stealth method of tracking the facial blood flow and temperature patterns.

Contactless Monitoring Of Human Vitals: A Study With Simultaneous Measurements Using FMCW Radar And Thermal Camera.

This paper proposes contactless monitoring of Heart Rate (HR) and Breath Rate (BR) with simultaneous measurements using Frequency Modulated Continuous Wave (FMCW) radar and thermal camera. The radar collects the body movement signals which include Random Body Movements (RBMs). Non-negative Matrix Factorization (NMF) and Wavelet analysis were used on this signal to get the accurate values of HR and BR. Similarly, with thermal imaging, nostril and forehead regions are tracked to estimate the values of BR as well as HR. We conducted an experiment with 50 subjects to find similarities in the performance of radar and thermal camera while measuring HR and BR. Simultaneously, these two methods have been validated with pulse oximeter and visual camera. From the visual camera, we can get the abdominal movements on which the BR can be ascertained whereas pulse oximeter gives us the HR. Radar signals are degraded because of large RBMs whereas thermal signals get distorted because of sudden temperature changes in the surroundings, sweating, and occlusion. We used a Signal Quality Metric (SQM) to ascertain the measurement quality of the vital signs. This SQM-based approach can further be used for sensor fusion to build a robust contactless system to monitor vital signs.Clinical relevance- Contactless and accurate measurement of HR and BR is very essential for continuous and comfortable monitoring of vitals. In this paper, we combine both FMCW radar and thermal camera so that one can complement the other in adverse scenarios on the basis of signal quality.

A Novel Technique for Detecting Depressive Disorder: A Speech Database-Based Approach.

Depression is the second most diagnosed disease in the world and is predicted to be the highest by the year 2030. Depressive disorder impacts both on mentally and physically, thus diagnosing this disorder in early stage is essential. Automatic Depression Detection (ADD) system via speech can greatly facilitate early-stage depression diagnosis. Development of such systems demands a standard balanced database. In this work, we present a novel labeled audio distress interview database. To our knowledge, this is the first depression database in Bengali language that contains audio responses from depressed and non-depressed subjects. Alongside this, we present a set of hand-crafted acoustic features that effectively detect depression mood using speech signals. Finally, we justify the quality of our developed database and the efficacy of the feature set in predicting depression using a baseline machine learning (ML) model. We believe that the annotated database will be a valuable resource for use by treating clinicians.Clinical Relevance-This research reports a new speech database in Bengali language for depression detection. This database can be used in healthcare by developing an automatic prediction model for depression detection.

Effective fall detection and post-fall breath rate tracking using a low-cost CW Doppler radar sensor.

Existing low-cost Doppler radar-based fall detection systems encounter challenges due to false alarms and the absence of post-fall health tracking, significantly impacting their accuracy and overall compatibility for fall detection. This paper presents a cost-effective, robust solution for a fall detection system with the post-fall health tracking facility using a 3.18 GHz continuous-wave Doppler radar sensor. The experimental data acquisition is conducted in-house under the guidance of a healthcare expert, involving various activities such as standing, sitting, sleeping, running, walking, falling, sit-to-stand, and stand-to-sit transitions. We propose an algorithm comprising four hierarchical stages, each with specific objectives. Considering the complexity, the model is trained differently for each stage to optimize the classification accuracy. The system architecture is designed to minimize computational costs and power consumption through modular implementation in stages, utilizing low-power equipment and incorporating traditional machine-learning algorithms. Experimental results demonstrate a fall detection accuracy of 93.24% and breath rate measurement error of 2.26%, which is competitive with recent state-of-the-art approaches. Obtained results highlight the effectiveness of the proposed system in addressing the challenges of false alarms and post-fall health tracking while maintaining cost-efficiency and accuracy in fall detection.

Sparse algorithms for EEG source localization.

Source localization using EEG is important in diagnosing various physiological and psychiatric diseases related to the brain. The high temporal resolution of EEG helps medical professionals assess the internal physiology of the brain in a more informative way. The internal sources are obtained from EEG by an inversion process. The number of sources in the brain outnumbers the number of measurements. In this article, a comprehensive review of the state-of-the-art sparse source localization methods in this field is presented. A recently developed method, certainty-based-reduced-sparse-solution (CARSS), is implemented and is examined. A vast comparative study is performed using a sixty-four-channel setup involving two source spaces. The first source space has 5004 sources and the other has 2004 sources. Four test cases with one, three, five, and seven simulated active sources are considered. Two noise levels are also being added to the noiseless data. The CARSS is also evaluated. The results are examined. A real EEG study is also attempted. Graphical Abstract.

A New Multi-resolution Analysis Method for Electrooculography Signals.

Electrooculography (EOG) signals indicate the degree and direction of eye movements. Hence, EOG signals have been useful in eye movement controlled rehabilitation systems. Denoising and accurate identification of the type of eye movement in EOG signals are the major challenges in their analysis. The state-of-the-art techniques for EOG signal analysis concerning denoising and eye movement extraction are based on multi-resolution analysis using wavelet bases, such as Haar or Daubechies. However, these wavelets are designed for general purpose signal processing applications and hence are not optimized for the EOG signal structures. In this paper, we propose a new multi-resolution basis specific to the analysis of EOG signals. The scaling and wavelet functions for the basis are derived from the signatures of blinks and saccades respectively, and hence we name them as blinklets and saclets accordingly, thereby forming a new multi-resolution basis. These descriptors are found to be more effective than standard wavelets for EOG signals, signal denoising, and for identifying the different eye movement signatures such as saccades, blinks, smooth pursuits, and fixations, as tested on the Physiosig and Centre for Biomedical Cybernetics Eye Movement (CBC-EM) EOG Databases.

Multimodal prediction of trait emotional intelligence-Through affective changes measured using non-contact based physiological measures.

Inability to efficiently deal with emotionally laden situations, often leads to poor interpersonal interactions. This adversely affects the individual's psychological functioning. A higher trait emotional intelligence (EI) is not only associated with psychological wellbeing, educational attainment, and job-related success, but also with willingness to seek professional and non-professional help for personal-emotional problems, depression and suicidal ideation. Thus, it is important to identify low (EI) individuals who are more prone to mental health problems than their high EI counterparts, and give them the appropriate EI training, which will aid in preventing the onset of various mood related disorders. Since people may be unaware of their level of EI/emotional skills or may tend to fake responses in self-report questionnaires in high stake situations, a system that assesses EI using physiological measures can prove affective. We present a multimodal method for detecting the level of trait Emotional intelligence using non-contact based autonomic sensors. To our knowledge, this is the first work to predict emotional intelligence level from physiological/autonomic (cardiac and respiratory) response patterns to emotions. Trait EI of 50 users was measured using Schutte Self Report Emotional Intelligence Test (SSEIT) along with their cardiovascular and respiratory data, which was recorded using FMCW radar sensor both at baseline and while viewing affective movie clips. We first examine relationships between users' Trait EI scores and autonomic response and reactivity to the clips. Our analysis suggests a significant relationship between EI and autonomic response and reactivity. We finally attempt binary EI level detection using linear SVM. We also attempt to classify each sub factor of EI, namely-perception of emotion, managing own emotions, managing other's emotions, and utilization of emotions. The proposed method achieves an EI classification accuracy of 84%, while accuracies ranging from 58 to 76% is achieved for recognition of the sub factors. This is the first step towards identifying EI of an individual purely through physiological responses. Limitation and future directions are discussed.

Certainty-Based Reduced Sparse Solution for Dense Array EEG Source Localization.

The EEG source localization is an ill-posed problem. It involves estimation of the sources which outnumbers the number of measurements. For a given measurement at a given time all sources are not active this makes the problem as sparse inversion problem. This paper presents a new approach for dense array EEG source localization. This paper aims at reducing the solution space to only most certain sources and thereby reducing the problem of ill-posedness. This employs a two-stage method, where the first stage finds the most certain sources that are likely to produce the observed EEG by using a statistical measure of sources, the second stage solves the inverse problem by restricting the solution space to only most certain sources and their neighbors. This reduces the solution space for other source localization methods hence improvise their accuracy in localizing the active neurological sources in the brain. This method has been validated and applied to real 256 channel data and the results were analyzed.

Topographical assessment of neurocortical connectivity by using directed transfer function and partial directed coherence during meditation.

Due to the presence of nonlinearity and volume conduction in electroencephalography (EEG), sometimes it's challenging to find out the actual brain network from neurodynamical alteration. In this paper, two well-known time-frequency brain connectivity measures, namely partial directed coherence (PDC) and directed transfer function (DTF), have been applied to evaluate the performance analysis of EEG signals obtained during meditation. These measures are implemented to the multichannel meditation EEG data to get the directed neural information flow. Mostly the assessment of PDC and DTF is entirely subjective and there are probabilities to have erroneous connectivity estimation. To avoid the subjective evaluation, the performance results are compared in terms of absolute energy, signal-to-noise ratio (SNR) and relative SNR (R-SNR) scale. In most of the cases, the PDC result is found to be more efficient than DTF. The limitation of DTF and PDC in terms of the time-varying multivariate autoregressive (MVAR) model is highlighted. The time-varying MVAR model can track the neurodynamical changes better than any other method. In the present study, we would like to show that the PDC-based connectivity gives a better understanding of the non-symmetric relation in EEG obtained during Kriya Yoga meditation in comparison to DTF. However, it needs to be investigated further to warrant this claim.

Highly Efficient Compression Algorithms for Multichannel EEG.

The difficulty associated with processing and understanding the high dimensionality of electroencephalogram (EEG) data requires developing efficient and robust compression algorithms. In this paper, different lossless compression techniques of single and multichannel EEG data, including Huffman coding, arithmetic coding, Markov predictor, linear predictor, context-based error modeling, multivariate autoregression (MVAR), and a low complexity bivariate model have been examined and their performances have been compared. Furthermore, a high compression algorithm named general MVAR and a modified context-based error modeling for multichannel EEG have been proposed. The resulting compression algorithm produces a higher relative compression ratio of 70.64% on average compared with the existing methods, and in some cases, it goes up to 83.06%. The proposed methods are designed to compress a large amount of multichannel EEG data efficiently so that the data storage and transmission bandwidth can be effectively used. These methods have been validated using several experimental multichannel EEG recordings of different subjects and publicly available standard databases. The satisfactory parametric measures of these methods, namely percent-root-mean square distortion, peak signal-to-noise ratio, root-mean-square error, and cross correlation, show their superiority over the state-of-the-art compression methods.

Serum-based diagnostic prediction of oral submucous fibrosis using FTIR spectrometry.

Oral submucous fibrosis (OSF) is found to have the highest malignant potentiality among all other pre-cancerous lesions. However, its detection prior to tissue biopsy can be challenging in clinics. Moreover, biopsy examination is invasive and painful. Hence, there is an urgent need of new technology that facilitates accurate diagnostic prediction of OSF prior to biopsy. Here, we used FTIR spectroscopy coupled with chemometric techniques to distinguish the serum metabolic signatures of OSF patients (n=30) and healthy controls (n=30). Serum biochemical analyses have been performed to further support the FTIR findings. Absorbance intensities of 45 infrared wavenumbers differed significantly between OSF and normal serum FTIR spectra representing alterations in carbohydrates, proteins, lipids and nucleic acids. Nineteen prominent significant wavenumbers (P≤0.001) at 1020, 1025, 1035, 1039, 1045, 1078, 1055, 1100, 1117, 1122, 1151, 1169, 1243, 1313, 1398, 1453, 1544, 1650 and 1725cm-1 provided excellent segregation of OSF spectra from normal using multivariate statistical techniques. These findings provided essential information on the metabolic features of blood serum of OSF patients and established that FTIR spectroscopy coupled with chemometric analysis can be potentially useful in the rapid and accurate preoperative screening/diagnosis of OSF.

"Omics" in oral cancer: New approaches for biomarker discovery.

OBJECTIVES: In this review paper, we explored the application of "omics" approaches in the study of oral cancer (OC). It will provide a better understanding of how "omics" approaches may lead to novel biomarker molecules or molecular signatures with potential value in clinical practice. A future direction of "omics"-driven research in OC is also discussed. METHODS: Studies on "omics"-based approaches [genomics/proteomics/transcriptomics/metabolomics] were investigated for differentiating oral squamous cell carcinoma,oral sub-mucous fibrosis, oral leukoplakia, oral lichen planus, oral erythroplakia from normal cases. Electronic databases viz., PubMed, Springer, and Google Scholar were searched. RESULTS: One eighty-one studies were included in this review. The review shows that the fields of genomics, transcriptomics, proteomics, and metabolomics-based marker identification have implemented advanced tools to screen early changes in DNA, RNA, protein, and metabolite expression in OC population. CONCLUSIONS: It may be concluded that despite advances in OC therapy, symptomatic presentation occurs at an advanced stage, where various curative treatment options become very limited. A molecular level study is essential for detecting an OC biomarker at an early stage. Modern "Omics" strategies can potentially make a major contribution to meet this need.

Analysis of cognitive fatigue using EEG parameters.

Continuous and repetitive performance of a task is likely to induce a drop in alertness levels of an individual. Changes in Electroencephalogram (EEG) have been proposed in literature as a marker of alertness during repeated performance of a cognitive task set. The present paper investigates the increase in fatigue levels and resultant drop in alertness of subjects during continuous performance of cognitive tasks by analyzing changes in energy of EEG frequency bands. The trends reflected in the EEG parameters correspond to a gradual increase in fatigue levels of subjects with increase in cognitive loading.

Diagnostic and Prognostic Utility of Non-Invasive Multimodal Imaging in Chronic Wound Monitoring: a Systematic Review.

Monitoring chronic wound [CW] healing is a challenging issue for clinicians across the world. Moreover, the health and cost burden of CW are escalating at a disturbing rate due to a global rise in population of elderly and diabetic cases. The conventional approach includes visual contour, sketches, or more rarely tracings. However, such conventional techniques bring forth infection, pain, allergies. Furthermore, these methods are subjective as well as time-consuming. As such, nowadays, non-touching and non-invasive CW monitoring system based on imaging techniques are gaining importance. They not only reduce patients' discomfort but also provide rapid wound diagnosis and prognosis. This review provides a survey of different types of CW characteristics, their healing mechanism and the multimodal non-invasive imaging methods that have been used for their diagnosis and prognosis. Current clinical practices as well as personal health systems [m-health and e-health] for CW monitoring have been discussed.

A Multimodal System for Assessing Alertness Levels Due to Cognitive Loading.

This paper proposes a scheme for assessing the alertness levels of an individual using simultaneous acquisition of multimodal physiological signals and fusing the information into a single metric for quantification of alertness. The system takes electroencephalogram, high-speed image sequence, and speech data as inputs. Certain parameters are computed from each of these measures as indicators of alertness and a metric is proposed using a fusion of the parameters for indicating alertness level of an individual at an instant. The scheme has been validated experimentally using standard neuropsychological tests, such as the Visual Response Test (VRT), Auditory Response Test (ART), a Letter Counting (LC) task, and the Stroop Test. The tests are used both as cognitive tasks to induce mental fatigue as well as tools to gauge the present degree of alertness of the subject. Correlation between the measures has been studied and the experimental variables have been statistically analyzed using measures such as multivariate linear regression and analysis of variance. Correspondence of trends obtained from biomarkers and neuropsychological measures validate the usability of the proposed metric.

Analysis of loss of alertness due to cognitive fatigue using motif synchronization of EEG records.

Continuous and repetitive performance of cognitive tasks is likely to cause a change in the alertness levels of an individual. The present paper investigates the changes in EEG as a marker of alertness during an experiment designed to induce cognitive fatigue in subjects by continuous and repetitive performance of standard neuropsychological tests. Electroencephalogram (EEG), speech data and high-speed ocular images are recorded during the task set comprising of an Auditory Response Test (ART) Psychomotor Vigilance Test (PVT), a Letter Counting (LC) task, and a variant of the Stroop Task. EEG recorded during the ART is analyzed in the present work. Variation in network parameters in brain networks formed from the EEG records using the Motif Synchronization technique is employed to trace the change in alertness levels of subjects due to increase in cognitive fatigue.

Source imaging of simple finger movements captured during auditory response tasks using space-time-frequency based sparsity constraints.

Neural Information Flow during simple cognitive or motor actions is a significant research topic; one of which is a simple finger movement. In this paper, Electro-encephalograph (EEG) Source Imaging has been used to model the Neural Information flow, during performance of a simple Auditory Response Task (ART), specifically by employment of a space-time-frequency sparsity constrained based algorithm, namely, Spatio-temporal Unifying Tomography (STOUT). The estimated sources are then studied according to location defined based on Destrieux atlas. Then, relative power carried in each scout at each instant has been used to create a neural information flow map, which is then compared with sources computed with standardized low resolution brain electromagnetic tomography (sLORETA).

Nanoscale Surface Characterization of Human Erythrocytes by Atomic Force Microscopy: A Critical Review.

Erythrocytes (red blood cells, RBCs), the most common type of blood cells in humans are well known for their ability in transporting oxygen to the whole body through hemoglobin. Alterations in their membrane skeletal proteins modify shape and mechanical properties resulting in several diseases. Atomic force microscopy (AFM), a new emerging technique allows non-invasive imaging of cell, its membrane and characterization of surface roughness at micrometer/nanometer resolution with minimal sample preparation. AFM imaging provides direct measurement of single cell morphology, its alteration and quantitative data on surface properties. Hence, AFM studies of human RBCs have picked up pace in the last decade. The aim of this paper is to review the various applications of AFM for characterization of human RBCs topology. AFM has been used for studying surface characteristics like nanostructure of membranes, cytoskeleton, microstructure, fluidity, vascular endothelium, etc., of human RBCs. Various modes of AFM imaging has been used to measure surface properties like stiffness, roughness, and elasticity. Topological alterations of erythrocytes in response to different pathological conditions have also been investigated by AFM. Thus, AFM-based studies and application of image processing techniques can effectively provide detailed insights about the morphology and membrane properties of human erythrocytes at nanoscale.

Functional network changes associated with sleep deprivation and fatigue during simulated driving: validation using blood biomarkers.

OBJECTIVE: Network analysis of electroencephalograph (EEG) signals to study the effect of fatigue and sleep deprivation in human drivers and its validation using blood biochemical parameters. METHODS: We present a new method of detection of human fatigue and sleepiness by studying the variation of functional interdependencies among EEG signals from various channels. An experiment has been designed to induce fatigue in 12 subjects through several stages over 36 h of sleep deprivation. The functional interdependency among the signals has been computed using synchronisation likelihood (SL), which measures the dynamical (both linear and non-linear) interdependency between two or more non-stationary time series. A network structure has been generated based on the likelihood values and is characterised by a number of standard network-characterising parameters at each stage. Finally, the trends in the network parameters have been validated using biochemical analysis of three blood parameters: glucose, blood urea and creatinine. RESULTS: An increasing trend in the degree of connectivity and clustering coefficient and a decreasing trend in the characteristic path length have been observed in some bands of signals at successive stages of the experiment. CONCLUSIONS: Synchronisation of specific bands of the EEG signals from different cortical areas has been observed along with variation in network parameters at increased levels of fatigue and sleep deprivation. SIGNIFICANCE: The results indicate that the network parameters may be used to detect and quantify the level of fatigue and sleepiness.