Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging.

Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.

A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches.

Dehydration in the human body arises due to inadequate replenishment of fluids. An appropriate level of hydration is essential for optimal functioning of the human body, and complications ranging from mild discomfort to, in severe cases, death, could result from a neglected imbalance in fluid levels. Regular and accurate monitoring of hydration status can provide meaningful information for people operating in stressful environmental conditions, such as athletes, military professionals and the elderly. In this study, we propose a non-invasive hydration monitoring technique employing non-ionizing electromagnetic power in the microwave band to estimate the changes in the water content of the whole body. Specifically, we investigate changes in the attenuation coefficient in the frequency range 2-3.5 GHz between a pair of planar antennas positioned across a participant's arm during various states of hydration. Twenty healthy young adults (10M, 10F) underwent controlled hypohydration and euhydration control bouts. The attenuation coefficient was compared among trials and used to predict changes in body mass. Volunteers lost 1.50±0.44% and 0.49±0.54% body mass during hypohydration and euhydration, respectively. The microwave transmission-based attenuation coefficient (2-3.5 GHz) was accurate in predicting changes in hydration status. The corresponding regression analysis demonstrates that building separate estimation models for dehydration and rehydration phases offer better predictive performance (88%) relative to a common model for both the phases (76%).

Towards quantifying the communication aspect of resilience in disaster-prone communities.

In this study, we investigate the communication networks of urban, suburban, and rural communities from three US Midwest counties through a stochastic model that simulates the diffusion of information over time in disaster and in normal situations. To understand information diffusion in communities, we investigate the interplay of information that individuals get from online social networks, local news, government sources, mainstream media, and print media. We utilize survey data collected from target communities and create graphs of each community to quantify node-to-node and source-to-node interactions, as well as trust patterns. Monte Carlo simulation results show the average time it takes for information to propagate to 90% of the population for each community. We conclude that rural, suburban, and urban communities have different inherent properties promoting the varied flow of information. Also, information sources affect information spread differently, causing degradation of information speed if any source becomes unavailable. Finally, we provide insights on the optimal investments to improve disaster communication based on community features and contexts.

Serum amyloid A and mitochondrial DNA in extracellular vesicles are novel markers for detecting traumatic brain injury in a mouse model.

This study investigates the potential use of circulating extracellular vesicles' (EVs) DNA and protein content as biomarkers for traumatic brain injury (TBI) in a mouse model. Despite an overall decrease in EVs count during the acute phase, there was an increased presence of exosomes (CD63+ EVs) during acute and an increase in microvesicles derived from microglia/macrophages (CD11b+ EVs) and astrocytes (ACSA-2+ EVs) in post-acute TBI phases, respectively. Notably, mtDNA exhibited an immediate elevation post-injury. Neuronal (NFL) and microglial (Iba1) markers increased in the acute, while the astrocyte marker (GFAP) increased in post-acute TBI phases. Novel protein biomarkers (SAA, Hp, VWF, CFD, CBG) specific to different TBI phases were also identified. Biostatistical modeling and machine learning identified mtDNA and SAA as decisive markers for TBI detection. These findings emphasize the importance of profiling EVs' content and their dynamic release as an innovative diagnostic approach for TBI in liquid biopsies.

Challenges and Opportunities in Deep Reinforcement Learning With Graph Neural Networks: A Comprehensive Review of Algorithms and Applications.

Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation systems, and gaming. Similarly, graph neural networks (GNNs) have also demonstrated their superior performance in supervised learning for graph-structured data. In recent times, the fusion of GNN with DRL for graph-structured environments has attracted a lot of attention. This article provides a comprehensive review of these hybrid works. These works can be classified into two categories: 1) algorithmic contributions, where DRL and GNN complement each other with an objective of addressing each other's shortcomings and 2) application-specific contributions that leverage a combined GNN-DRL formulation to address problems specific to different applications. This fusion effectively addresses various complex problems in engineering and life sciences. Based on the review, we further analyze the applicability and benefits of fusing these two domains, especially in terms of increasing generalizability and reducing computational complexity. Finally, the key challenges in integrating DRL and GNN, and potential future research directions are highlighted, which will be of interest to the broader machine learning community.

Integrated approach for optimal sensor placement and state estimation: A case study on water distribution networks.

The objective of the design and operation of any water distribution network (WDN) includes meeting the desired demand at sufficient pressure at all nodes. However, this requires situational awareness; in other words, the knowledge of system state variables such as pressure and flow throughout the network. In this work, a hybrid approach is developed for sensor placement (SP) and state estimation (SE) that exploits the underlying correlation structure in the data, along with the principles governing the flow through circular pipes. The problem of SP in WDN is addressed since measuring the state variables throughout the network is not practical. The problem of SE that maps to a matrix completion problem under certain physical and logical constraints is solved later. The completed matrix represents the state of WDN at any given time. Benchmark networks used in literature were used to evaluate the proposed approach. The mean absolute percentage error (MAPE) of less than 5% was obtained while estimating the head available at nodes. The knowledge of the states in the entire network could help operate the network adaptively.

Early Detection of Pancreatic Cancers Using Liquid Biopsies and Hierarchical Decision Structure.

OBJECTIVE: Pancreatic cancer (PC) is a silent killer, because its detection is difficult and to date no effective treatment has been developed. In the US, the current 5-year survival rate of 11%. Therefore, PC has to be detected as early as possible. METHODS AND PROCEDURES: In this work, we have combined the use of ultrasensitive nanobiosensors for protease/arginase detection with information fusion based hierarchical decision structure to detect PC at the localized stage by means of a simple Liquid Biopsy. The problem of early-stage detection of pancreatic cancer is modelled as a multi-class classification problem. We propose a Hard Hierarchical Decision Structure (HDS) along with appropriate feature engineering steps to improve the performance of conventional multi-class classification approaches. Further, a Soft Hierarchical Decision Structure (SDS) is developed to additionally provide confidences of predicted labels in the form of class probability values. These frameworks overcome the limitations of existing research studies that employ simple biostatistical tools and do not effectively exploit the information provided by ultrasensitive protease/arginase analyses. RESULTS: The experimental results demonstrate that an overall mean classification accuracy of around 92% is obtained using the proposed approach, as opposed to 75% with conventional multi-class classification approaches. This illustrates that the proposed HDS framework outperforms traditional classification techniques for early-stage PC detection. CONCLUSION: Although this study is only based on 31 pancreatic cancer patients and a healthy control group of 48 human subjects, it has enabled combining Liquid Biopsies and Machine Learning methodologies to reach the goal of earliest PC detection. The provision of both decision labels (via HDS) as well as class probabilities (via SDS) helps clinicians identify instances where statistical model-based predictions lack confidence. This further aids in determining if more tests are required for better diagnosis. Such a strategy makes the output of our decision model more interpretable and can assist with the diagnostic procedure. CLINICAL IMPACT: With further validation, the proposed framework can be employed as a decision support tool for the clinicians to help in detection of pancreatic cancer at early stages.

Experimental Validation of Diffraction Lithography for Fabrication of Solid Microneedles.

Microneedles are highly sought after for medicinal and cosmetic applications. However, the current manufacturing process for microneedles remains complicated, hindering its applicability to a broader variety of applications. As diffraction lithography has been recently reported as a simple method for fabricating solid microneedles, this paper presents the experimental validation of the use of ultraviolet light diffraction to control the liquid-to-solid transition of photosensitive resin to define the microneedle shape. The shapes of the resultant microneedles were investigated utilizing the primary experimental parameters including the photopattern size, ultraviolet light intensity, and the exposure time. Our fabrication results indicated that the fabricated microneedles became taller and larger in general when the experimental parameters were increased. Additionally, our investigation revealed four unique crosslinked resin morphologies during the first growth of the microneedle: microlens, first harmonic, first bell-tip, and second harmonic shapes. Additionally, by tilting the light exposure direction, a novel inclined microneedle array was fabricated for the first time. The fabricated microneedles were characterized with skin insertion and force-displacement tests. This experimental study enables the shapes and mechanical properties of the microneedles to be predicted in advance for mass production and wide practical use for biomedical or cosmetic applications.

Pen-Type Electrodermal Activity Sensing System for Stress Detection Based on Likelihood Ratios.

Psychological stress experienced during academic testing is a significant performance factor for some students. While a student may be able to recognize and self-report exam stress, unobtrusive tools to track stress in real time and in association with specific test problems are lacking. This effort pursued the design and initial assessment of an electrodermal activity (EDA) sensor mounted to a pen/pencil 'trainer:' a holder into which a pen/pencil is inserted that can help a person learn how to properly grip a writing instrument. This small assembly was held in the hand of each subject during early experiments and can be used for follow-on, mock test-taking scenarios. In these experiments, data were acquired with this handheld device for each of 36 subjects (Kansas State University Internal Review Board Protocol #9864) while they viewed approximately 30 minutes of emotion-evoking videos. Data collected by the EDA sensor were analyzed by an EDA signal processing app, which calculated and stored parameters associated with significant phasic EDA peaks while allowing intermediate peak detection processes to be visualized. These peak data were then subjected to a hypothesis driven stress-detection test that employed likelihood ratios to identify 'relaxed' versus 'stressed' events. For these initial testing scenarios, which were free of hand motions, this pen-type EDA sensing system discerned 'relaxed' versus 'stressed' phasic responses with 87.5% accuracy on average, where subject self-assessments of perceived stress levels were used to establish ground truth.

Temperature estimation for MR-guided microwave hyperthermia using block-based compressed sensing.

Mild hyperthermia has been clinically employed as an adjuvant for radiation/chemotherapy and is under investigation for precise thermally-mediated delivery of cancer therapeutic agents. Magnetic Resonance Imaging (MRI) facilitates non-invasive, real-time spatial thermometry for monitoring and guiding hyperthermia procedures. Long image acquisition time during MR-guided hyperthermia may fail to capture rapid changes in temperature. This may lead to unwanted heating of healthy tissue and/or temperature rise above hyperthermic range. We have developed a block-based compressed sensing approach to reconstruct volumetric MR-derived microwave hyperthermia temperature profiles using a subset of measured data. This algorithm exploits the sparsity of MR images due to the presence of inter- and intra-slice correlation of hyperthermic MR-derived temperature profiles. We have evaluated the performance of our developed algorithm on a phantom and in vivo in mice using previously implemented microwave applicators. This algorithm reconstructs 3D temperature profiles with PSNR of 33 dB - 49 dB in comparison to the original profiles. In summary, this study suggests that microwave hyperthermia induced temperature profiles can be reconstructed using subsamples to reduce MR image acquisition time.

Smart bed based daytime behavior prediction in Children with autism spectrum disorder - A Pilot Study.

Monitoring the sleep patterns of children with autism spectrum disorders (ASD) and understanding how sleep quality influences their daytime behavior is an important issue that has received very limited attention. Polysomnography (PSG) is commonly used as a gold standard for evaluating sleep quality in children and adults. However, the intrusive nature of sensors used as part of PSG can themselves affect sleep and is, therefore, not suitable for children with ASD. In this study, we evaluate an unobtrusive and inexpensive bed system for in-home, long-term sleep quality monitoring using ballistocardiogram (BCG) signals. Using the BCG signals from this smart bed system, we define "restlessness" as a surrogate sleep quality estimator. Using this sleep feature, we build predictive models for daytime behavior based on 1-8 previous nights of sleep. Specifically, we use two supervised machine learning algorithms namely support vector machine (SVM) and artificial neural network (ANN). For all daytime behaviors, we achieve more than 78% and 79% accuracy of correctly predicting behavioral issues with both SVM and ANN classifiers, respectively. Our findings indicate the usefulness of our designed bed system and how the restlessness feature can improve the prediction performance.

P300 Latency Estimation Using Least Mean Squares Filter.

Event-related potentials (ERPs) are the brain response directly related to specific events or stimuli. The P300 ERP is a positive deflection nominally 300ms post-stimulus that is related to mental decision making processes and also used in P300-based speller systems. Single-trial estimation of P300 responses will help to understand the underlying cognitive process more precisely and also to improve the speed of speller brain-computer interfaces (BCIs). This paper aims to develop a single-trial estimation of the P300 amplitudes and latencies by using the least mean squares (LMS) adaptive filtering method. Results for real data from people with amyotrophic lateral sclerosis (ALS) have shown that the LMS filter can be effectively used to estimate P300 latencies.

A Pilot Study of an Unobtrusive Bed-Based Sleep Quality Monitor for Severely Disabled Autistic Children.

The link between daytime performance and sleep quality for severely disabled autistic children is not entirely understood. This paper presents nighttime data collected from a child with severe disabilities during a three-night pilot study conducted at Heartspring, Wichita, KS, using a bed-based system capable of unobtrusively tracking parameters for sleep quality assessment. The 'average sample correlation coefficient signal-to-noise ratio' is compared for ballistocardiograms acquired using four electromechanical film sensors versus four load cell sensors. The "best" signal or sensing modality depends on the subject's sleeping position. These results affirm the importance of a bed system that is robust in its ability to track sleep quality accurately regardless of sleeping position.

Onboard tagging for real-time quality assessment of photoplethysmograms acquired by a wireless reflectance pulse oximeter.

Onboard assessment of photoplethysmogram (PPG) quality could reduce unnecessary data transmission on battery-powered wireless pulse oximeters and improve the viability of the electronic patient records to which these data are stored. These algorithms show promise to increase the intelligence level of former "dumb" medical devices: devices that acquire and forward data but leave data interpretation to the clinician or host system. To this end, the authors have developed a unique onboard feature detection algorithm to assess the quality of PPGs acquired with a custom reflectance mode, wireless pulse oximeter. The algorithm uses a Bayesian hypothesis testing method to analyze four features extracted from raw and decimated PPG data in order to determine whether the original data comprise valid PPG waveforms or whether they are corrupted by motion or other environmental influences. Based on these results, the algorithm further calculates heart rate and blood oxygen saturation from a "compact representation" structure. PPG data were collected from 47 subjects to train the feature detection algorithm and to gauge their performance. A MATLAB interface was also developed to visualize the features extracted, the algorithm flow, and the decision results, where all algorithm-related parameters and decisions were ascertained on the wireless unit prior to transmission. For the data sets acquired here, the algorithm was 99% effective in identifying clean, usable PPGs versus nonsaturated data that did not demonstrate meaningful pulsatile waveshapes, PPGs corrupted by motion artifact, and data affected by signal saturation.

Diffusely increased thyroidal uptake of F-18 fluorodeoxyglucose in a patient with non-Hodgkin's lymphoma and Graves' disease.

Increased uptake of F-18 fluorodeoxyglucose (FDG) has been reported in thyroiditis and hypothyroidism. The authors present a case where increased FDG uptake in the thyroid was subsequently corroborated with a pertechnetate scan and thyroid hormone levels to diagnose previously undetected Graves' disease in a patient of non-Hodgkin's lymphoma being followed-up with positron emission tomography for disease recurrence.

Sacrococcygeal chordoma: Increased (99m)Tc methylene diphosphonate uptake on single photon emission computed tomography/computed tomography bone scintigraphy.

Chordoma is a malignant tumor arising from the remnants of the notochord, and is the most frequent primitive tumor of the sacrum. While most sacral tumors show increased concentration of bone-seeking radiopharmaceuticals, chordomas usually exhibit decreased uptake. The authors present an image of a sacrococcygeal chordoma with osteolysis and increased uptake of 99mTc methylene diphosphonate on planar and single photon emission computed tomography/computed tomography bone scintigraphy.

Two-stage approach for detection and reduction of motion artifacts in photoplethysmographic data.

Corruption of photopleythysmograms (PPGs) by motion artifacts has been a serious obstacle to the reliable use of pulse oximeters for real-time, continuous state-of-health monitoring. In this paper, we propose an automated, two-stage PPG data processing method to minimize the effects of motion artifacts. The technique is based on our prior work related to motion artifact detection (stage 1) [R. Krishnan, B. Natarajan, and S. Warren, "Analysis and detection of motion artifacts in photoplethysmographic data using higher order statistics,'' in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP 2008), Las Vegas, Nevada, Apr. 2008, pp. 613-616] and motion artifact reduction (stage 2) [R. Krishnan, B. Natarajan, and S. Warren, "Motion artifact reduction in photoplethysmography using magnitude-based frequency domain independent component analysis,'' in Proc. 17th Int. Conf. Comput. Commun. Network, St. Thomas, Virgin Islands, Aug. 2008, pp. 1-5]. Regarding stage 1, we present novel and consistent techniques to detect the presence of motion artifact in PPGs given higher order statistical information present in the data. We analyze these data in the time and frequency domains (FDs) and identify metrics to distinguish between clean and motion-corrupted data. A Neyman-Pearson detection rule is formulated for each of the metrics. Furthermore, by treating each of the metrics as observations from independent sensors, we employ hard fusion and soft fusion techniques presented in [Z. Chair and P. Varshney, "Optimal data fusion in multiple sensor detection systems,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 1, no. 1, pp. 98-101, Jan. 1986] and [C. C. Lee and J. J. Chao, "Optimum local decision space partitioning for distributed detection,'' IEEE Trans. Aerosp. Electron. Syst., AES, vol. 25, no. 7, pp. 536-544, Jul. 1989], respectively, in order to fuse individual decisions into a global system decision. For stage two, we propose a motion artifact reduction method that is effective even in the presence of severe subject movement. The approach involves an enhanced preprocessing unit consisting of a motion detection unit (MDU, developed in this paper), period estimation unit, and Fourier series reconstruction unit. The MDU identifies clean data frames versus those corrupted with motion artifacts. The period estimation unit determines the fundamental frequency of a corrupt frame. The Fourier series reconstruction unit reconstructs the final preprocessed signal by utilizing the spectrum variability of the pulse waveform. Preprocessed data are then fed to a magnitude-based FD independent component analysis unit. This helps reduce motion artifacts present at the frequencies of the reconstruction components. Experimental results are presented to demonstrate the efficacy of the overall motion artifact reduction method.