Uniqueness of Iris Pattern Based on the Auto-Regressive Model.

In this paper, we evaluate the uniqueness of a hypothetical iris recognition system that relies upon a nonlinear mapping of iris data into a space of Gaussian codewords with independent components. Given the new data representation, we develop and apply a sphere packing bound for Gaussian codewords and a bound similar to Daugman's to characterize the maximum iris population as a function of the relative entropy between Gaussian codewords of distinct iris classes. As a potential theoretical approach leading toward the realization of the hypothetical mapping, we work with the auto-regressive model fitted into iris data, after some data manipulation and preprocessing. The distance between a pair of codewords is measured in terms of the relative entropy (log-likelihood ratio statistic is an alternative) between distributions of codewords, which is also interpreted as a measure of iris quality. The new approach to iris uniqueness is illustrated using two toy examples involving two small datasets of iris images. For both datasets, the maximum sustainable population is presented as a function of image quality expressed in terms of relative entropy. Although the auto-regressive model may not be the best model for iris data, it lays the theoretical framework for the development of a high-performance iris recognition system utilizing a nonlinear mapping from the space of iris data to the space of Gaussian codewords with independent components.

Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements.

Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32-64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks.

Effects of brewing conditions on the antioxidant capacity of twenty-four commercial green tea varieties.

A novel paper-based Nanoceria Reducing Antioxidant Capacity (NanoCerac) assay for antioxidant detection (Sharpe, Frasco, Andreescu, & Andreescu, 2012), has been adapted for the first time as a high-throughput method, in order to measure the effect of brewing conditions and re-infusion on the antioxidant capacity of twenty-four commercial green teas. The oxygen radical absorbance capacity (ORAC) assay, frequently applied to complex foods and beverages, was used as a comparator measure of antioxidant capacity. A novel measure of sustained antioxidant capacity, the total inherent antioxidant capacity (TI-NanoCerac and TI-ORAC) was measured by infusing each tea six times. Effects of brewing conditions (temperature, brew time, etc.) were assessed using one popular tea as a standard. Both NanoCerac and ORAC assays correlated moderately (R(2) 0.80 ± 0.19). The average first-brew NanoCerac, TI-NanoCerac, first-brew ORAC and TI-ORAC were: 0.73 ± 0.1 GAE/g tea; 2.4 ± 0.70 mmolGAE/g tea; 1.0 ± 0.3 mmolTE/g tea and 2.1 ± 0.71 mmolTE/g tea respectively. Brewing conditions including water temperature and infusion time significantly affected antioxidant capacity. The high-throughput adaptation of the original NanoCerac assay tested here offered advantages over ORAC, including portability and rapid analysis.

Energy intake estimation from counts of chews and swallows.

Current, validated methods for dietary assessment rely on self-report, which tends to be inaccurate, time-consuming, and burdensome. The objective of this work was to demonstrate the suitability of estimating energy intake using individually-calibrated models based on Counts of Chews and Swallows (CCS models). In a laboratory setting, subjects consumed three identical meals (training meals) and a fourth meal with different content (validation meal). Energy intake was estimated by four different methods: weighed food records (gold standard), diet diaries, photographic food records, and CCS models. Counts of chews and swallows were measured using wearable sensors and video analysis. Results for the training meals demonstrated that CCS models presented the lowest reporting bias and a lower error as compared to diet diaries. For the validation meal, CCS models showed reporting errors that were not different from the diary or the photographic method. The increase in error for the validation meal may be attributed to differences in the physical properties of foods consumed during training and validation meals. However, this may be potentially compensated for by including correction factors into the models. This study suggests that estimation of energy intake from CCS may offer a promising alternative to overcome limitations of self-report.

Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing.

The number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques. First, an unsupervised clustering technique, Affinity Propagation (AP), was used to automatically identify the number of segments within a meal. Second, performance of the unsupervised AP method was compared to a supervised learning approach based on Agglomerative Hierarchical Clustering (AHC). While the AP method was able to obtain 90% accuracy in predicting the number of food items, the AHC achieved an accuracy >95%. Experimental results suggest that the proposed models of automatic meal segmentation may be utilized as part of an integral application for objective Monitoring of Ingestive Behavior in free living conditions.

Automatic food intake detection based on swallowing sounds.

This paper presents a novel fully automatic food intake detection methodology, an important step toward objective monitoring of ingestive behavior. The aim of such monitoring is to improve our understanding of eating behaviors associated with obesity and eating disorders. The proposed methodology consists of two stages. First, acoustic detection of swallowing instances based on mel-scale Fourier spectrum features and classification using support vector machines is performed. Principal component analysis and a smoothing algorithm are used to improve swallowing detection accuracy. Second, the frequency of swallowing is used as a predictor for detection of food intake episodes. The proposed methodology was tested on data collected from 12 subjects with various degrees of adiposity. Average accuracies of >80% and >75% were obtained for intra-subject and inter-subject models correspondingly with a temporal resolution of 30s. Results obtained on 44.1 hours of data with a total of 7305 swallows show that detection accuracies are comparable for obese and lean subjects. They also suggest feasibility of food intake detection based on swallowing sounds and potential of the proposed methodology for automatic monitoring of ingestive behavior. Based on a wearable non-invasive acoustic sensor the proposed methodology may potentially be used in free-living conditions.

Robust color texture features under varying illumination conditions.

Under varying illumination, both the statistical and structural contents of color texture are modified, leading to changes in the observed texture surface. We model the effect of illumination as a perturbation on an ideal color texture and show that the spectra of the ambient light have a significant impact on the observed texture patterns in the individual color channels. Motivated by studies in human color constancy, we propose a correlation-based transformation that minimizes the effect of illumination variation in color texture analysis. Experimental results are included, which validate the performance of the proposed minvariance model in the analysis of color texture.

Transcriptome-wide gene expression in a rat model of attention deficit hyperactivity disorder symptoms: rats developmentally exposed to polychlorinated biphenyls.

Polychlorinated biphenyls (PCB) exposure in rodents provides a useful model for the symptoms of Attention deficit hyperactivity disorder (ADHD). The goal of this study is to identify genes whose expression levels are altered in response to PCB exposure. The brains from 48 rats separated into two age groups of 24 animals each (4 males and 4 females for each PCB exposure level (control, PCB utero, and PCB lactational)) were harvested at postnatal days 23 and 35, respectively. The RNA was isolated from three brain regions of interest and was analyzed for differences in expression of a set of 27,342 transcripts. Two hundred seventy-nine transcripts showed significant differential expression due to PCB exposure mostly due to the difference between PCB lactational and control groups. The cluster analysis applied to these transcripts revealed that significant changes in gene expression levels in PFC area due to PCB lactational exposure. Our pathway analyses implicated 27 significant canonical pathways and 38 significant functional pathways. Our transcriptome-wide analysis of the effects of PCB exposure shows that the expression of many genes is dysregulated by lactational PCB exposure, but not gestational exposure and has highlighted biological pathways that might mediate the effects of PCB exposure on ADHD-like behaviors seen in exposed animals. Our work should further motivate studies of fatty acids in ADHD, and further suggests that another potentially druggable pathway, oxidative stress, may play a role in PCB induced ADHD behaviors.

Comparison of texture analysis schemes under nonideal conditions.

Several recent advancements in the field of texture analysis prompt some fundamental questions. For instance, what is the true impact of these novel advancements under real-world environments? When do these novel advancements fail to perform? Which methods perform better and under what conditions? In this work, we investigate these and other issues under nonideal image acquisition environments, specifically, environments with changing conditions due to illumination variations and those caused by both affine and nonaffine transformations. We study the performance of nine popular texture analysis algorithms using three different datasets, with varying levels of difficulty. Experiments are performed on nonideal texture datasets under five different setups. We find that most state-of-the-art techniques do not perform well under these conditions. To a large extent, their performance under nonideal conditions depends critically on the nature of the textural surface. Moreover, most techniques fail to perform reliably when the number of classes in the dataset is increased significantly, over the regular-size datasets used in previous work. Multiscale features performed reasonably well against variations caused by illumination and rotation but are prone to fail under changes in scale. Surprisingly, the performance for most of the algorithms is generally stable on structured or periodic textures, even with variations in illumination or affine transformations.

Detection of periods of food intake using Support Vector Machines.

Studies of obesity and eating disorders need objective tools of Monitoring of Ingestive Behavior (MIB) that can detect and characterize food intake. In this paper we describe detection of food intake by a Support Vector Machine classifier trained on time history of chews and swallows. The training was performed on data collected from 18 subjects in 72 experiments involving eating and other activities (for example, talking). The highest accuracy of detecting food intake (94%) was achieved in configuration where both chews and swallows were used as predictors. Using only swallowing as a predictor resulted in 80% accuracy. Experimental results suggest that these two predictors may be used for differentiation between periods of resting and food intake with a resolution of 30 seconds. Proposed methods may be utilized for development of an accurate, inexpensive, and non-intrusive methodology to objectively monitor food intake in free living conditions.

Preliminary assessment of a model to predict mold contamination based on microbial volatile organic compound profiles.

Identification of mold growth based on microbial volatile organic compounds (MVOCs) may be a viable alternative to current bioaerosol assessment methodologies. A feed-forward back propagation (FFBP) artificial neural network (ANN) was developed to correlate MVOCs with bioaerosol levels in built environments. A cross-validation MATLAB script was developed to train the ANN and produce model results. Entech Bottle-Vacs were used to collect chemical grab samples at 10 locations in northern NY during 17 sampling periods from July 2006 to August 2007. Bioaerosol samples were collected concurrently with chemical samples. An Anderson N6 impactor was used in conjunction with malt extract agar and dichloran glycerol 18 to collect viable mold samples. Non-viable samples were collected with Air-O-Cell cassettes. Chemical samples and bioaerosol samples were used as model inputs and model targets, respectively. Previous researchers have suggested the use of MVOCs as indicators of mold growth without the use of a pattern recognition program limiting their success. The current proposed strategy implements a pattern recognition program making it instrumental for field applications. This paper demonstrates that FFBP ANN may be used in conjunction with chemical sampling in built environments to predict the presence of mold growth.

Detection of food intake from swallowing sequences by supervised and unsupervised methods.

Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.

The energetics of obesity: a review: monitoring energy intake and energy expenditure in humans.

Writing about obesity research is a challenging task. While the rising obesity epidemic drastically raised public awareness of the problem, the causes behind the epidemic are still poorly understood. The etiology of obesity is a subject of ongoing scientific debate with widely varying views and strong opinions. Is it mostly genetic or environmental in nature? Is obesity caused by changes in our diet or changes in lifestyle and physical activity or both? Modern research literature quite often offers conflicting findings. Publications in popular media like the one in Time magazine add to the controversy by making quick and strongly worded summaries of academic research. Although the root causes of obesity remains a topic of active research, this review concentrates on the fundamental components of weight regulation in humans and their relative contribution to the energy equation. A better understanding of the energetics of obesity may provide some insight into the etiology of the obesity epidemic. The energetics of obesity also showcases an engineering challenge: development of techniques to accurately measure individual components of the energy equation.

Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior.

Our understanding of etiology of obesity and overweight is incomplete due to lack of objective and accurate methods for monitoring of ingestive behavior (MIB) in the free-living population. Our research has shown that frequency of swallowing may serve as a predictor for detecting food intake, differentiating liquids and solids, and estimating ingested mass. This paper proposes and compares two methods of acoustical swallowing detection from sounds contaminated by motion artifacts, speech, and external noise. Methods based on mel-scale Fourier spectrum, wavelet packets, and support vector machines are studied considering the effects of epoch size, level of decomposition, and lagging on classification accuracy. The methodology was tested on a large dataset (64.5 h with a total of 9966 swallows) collected from 20 human subjects with various degrees of adiposity. Average weighted epoch-recognition accuracy for intravisit individual models was 96.8%, which resulted in 84.7% average weighted accuracy in detection of swallowing events. These results suggest high efficiency of the proposed methodology in separation of swallowing sounds from artifacts that originate from respiration, intrinsic speech, head movements, food ingestion, and ambient noise. The recognition accuracy was not related to body mass index, suggesting that the methodology is suitable for obese individuals.

Detection and prediction of concentrations of neurotransmitters using voltammetry and pattern recognition.

Neurotransmitters (NTs) are substances in the brain which are responsible for the transmission of neurological impulses. Changes in their concentrations are associated with numerous behavioral and physiological processes and neurological disorders. As opposed to the traditional chromatographic and capillary electrophoresis, using electrochemical sensors is a fast and inexpensive way to determine concentrations of NTs. In this study we measure the combination of dopamine (DA) and serotonin (SE) with glassy carbon electrodes and differential pulse voltammetry. The major challenge using this method is to differentiate between different NTs, since the signal obtained from the electrode represents the interactive effect of both NTs present. We address this problem through methods of pattern recognition which relate the voltammetric measurements provided by the sensor to the concentration of individual NTs. Two methods of pattern recognition were applied (PCR and PLS-regression). The best rates of correct classification for the validation sets ranged at 42-62% (DA) and 33-50% (SE). When the ranges for correct prediction were extended to include one level above and below the true concentration level, the rates values ranged at 81-91% (DA) and 91-100%(SE). These findings suggest that pattern recognition can be used to model the interaction between different neurotransmitters to predict actual concentrations of neurotransmitters using voltammetry.

Automatic recognition of postures and activities in stroke patients.

Stroke is the leading cause of disability in the United States. It is estimated that 700,000 people in the United States will experience a stroke each year and that there are over 5 million Americans living with a stroke. In this paper we describe a novel methodology for automatic recognition of postures and activities in patients with stroke that may be used to provide behavioral enhancing feedback to patients with stroke as part of a rehabilitation program and potentially enhance rehabilitation outcomes. The recognition methodology is based on Support Vector classification of the sensor data provided by a wearable shoe-based device. The proposed methodology was validated in a case study involving an individual with a chronic stroke with impaired motor function of the affected lower extremity and impaired walking ability. The results suggest that recognition of postures and activities may be performed with very high accuracy.

Toward objective monitoring of ingestive behavior in free-living population.

Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions.

Exploring QT interval changes as a precursor to the onset of ventricular fibrillation/tachycardia.

In the present study, we have retrospectively analyzed the corrected QT (QTc) interval before spontaneous episodes of sudden cardiac arrest in patients with a wearable cardioverter defibrillator. Corrected QT interval was measured for all normal beats from 32 recordings of baseline rhythm and compared to normal rhythm before a paired spontaneous cardiac arrhythmia. Before arrhythmia, the QTc (505 +/- 73 ms) was not significantly longer than the baseline rhythm (497 +/- 73 ms) (P = .23). Considering ventricular tachycardia (VT) events only (12 patients), event QTc (526 +/- 75 ms) was not significantly longer than baseline QTc (520 +/- 74 ms) (P = .41). Considering fast VT/ventricular fibrillation (VF) events only (20 patients), event QTc (494 +/- 70 ms) was not significantly longer than baseline QTc (483 +/- 71 ms) (P = .26). The influence of QTc as a measure to indicate an impending VT event in a variety of VT/VF patients remains unclear.

Non-invasive monitoring of chewing and swallowing for objective quantification of ingestive behavior.

A methodology of studying of ingestive behavior by non-invasive monitoring of swallowing (deglutition) and chewing (mastication) has been developed. The target application for the developed methodology is to study the behavioral patterns of food consumption and producing volumetric and weight estimates of energy intake. Monitoring is non-invasive based on detecting swallowing by a sound sensor located over laryngopharynx or by a bone-conduction microphone and detecting chewing through a below-the-ear strain sensor. Proposed sensors may be implemented in a wearable monitoring device, thus enabling monitoring of ingestive behavior in free-living individuals. In this paper, the goals in the development of this methodology are two-fold. First, a system comprising sensors, related hardware and software for multi-modal data capture is designed for data collection in a controlled environment. Second, a protocol is developed for manual scoring of chewing and swallowing for use as a gold standard. The multi-modal data capture was tested by measuring chewing and swallowing in 21 volunteers during periods of food intake and quiet sitting (no food intake). Video footage and sensor signals were manually scored by trained raters. Inter-rater reliability study for three raters conducted on the sample set of five subjects resulted in high average intra-class correlation coefficients of 0.996 for bites, 0.988 for chews and 0.98 for swallows. The collected sensor signals and the resulting manual scores will be used in future research as a gold standard for further assessment of sensor design, development of automatic pattern recognition routines and study of the relationship between swallowing/chewing and ingestive behavior.

EEG indices of tonic pain-related activity in the somatosensory cortices.

OBJECTIVE: To identify EEG features that index pain-related cortical activity, and to identify factors that can mask the pain-related EEG features and/or produce features that can be misinterpreted as pain-specific. METHODS: The EEG was recorded during three conditions presented in counterbalanced order: a tonic cold pain condition, and pain anticipation and arithmetic control conditions. The EEG was also recorded while the subjects made a wincing facial expression to estimate the contribution of scalp EMG artifacts to the pain-related EEG features. RESULTS: Alpha amplitudes decreased over the contralateral temporal scalp and increased over the posterior scalp during the cold pain condition. There was an increase in gamma band activity during the cold pain condition at most electrode locations that was due to EMG artifacts. CONCLUSIONS: The decrease in alpha over the contralateral temporal scalp during cold pain is consistent with pain-related activity in the primary somatosensory cortex and/or the somatosensory association areas located in the parietal operculum and/or insula. This study also identified factors that might mask the pain-related EEG features and/or generate EEG features that could be misinterpreted as being pain-specific. These include (but are not limited to) an increase in alpha generated in the visual cortex that results from attention being drawn towards the pain; the widespread increase in gamma band activity that results from scalp EMG generated by the facial expressions that often accompany pain; and the possibility that non-specific changes in the EEG over time mask the pain-related EEG features when the pain and control conditions are given in the same order across subjects. SIGNIFICANCE: This study identified several factors that need to be controlled and/or isolated in order to successfully record EEG features that index pain-related activity in the somatosensory cortices.