BACKGROUND: Concussion is known to cause transient autonomic and cerebrovascular dysregulation that generally recovers; however, few studies have focused on individuals with an extensive concussion history.
METHODS: The case was a 26-year-old male with a history of 10 concussions, diagnosed for bipolar type II disorder, mild attention-deficit hyperactivity disorder, and a history of migraines/headaches. The case was medicated with Valproic Acid and Escitalopram. Sensor-based baseline data were collected within six months of his injury and on days 1-5, 10, and 14 post-injury. Symptom reporting, heart rate variability (HRV), neurovascular coupling (NVC), and dynamic cerebral autoregulation (dCA) assessments were completed using numerous biomedical devices (i.e., transcranial Doppler ultrasound, 3-lead electrocardiography, finger photoplethysmography).
RESULTS: Total symptom and symptom severity scores were higher for the first-week post-injury, with physical and emotional symptoms being the most impacted. The NVC response showed lowered activation in the first three days post-injury, while autonomic (HRV) and autoregulation (dCA) were impaired across all testing visits occurring in the first 14 days following his concussion.
CONCLUSIONS: Despite symptom resolution, the case demonstrated ongoing autonomic and autoregulatory dysfunction. Larger samples examining individuals with an extensive history of concussion are warranted to understand the chronic physiological changes that occur following cumulative concussions through biosensing devices.

(1) Background: The objective of this study was to predict the vascular health status of elderly women during exercise using pulse wave data and Temporal Convolutional Neural Networks (TCN); (2) Methods: A total of 492 healthy elderly women aged 60-75 years were recruited for the study. The study utilized a cross-sectional design. Vascular endothelial function was assessed non-invasively using Flow-Mediated Dilation (FMD). Pulse wave characteristics were quantified using photoplethysmography (PPG) sensors, and motion-induced noise in the PPG signals was mitigated through the application of a recursive least squares (RLS) adaptive filtering algorithm. A fixed-load cycling exercise protocol was employed. A TCN was constructed to classify flow-mediated dilation (FMD) into \"optimal\", \"impaired\", and \"at risk\" levels; (3) Results: TCN achieved an average accuracy of 79.3%, 84.8%, and 83.2% in predicting FMD at the \"optimal\", \"impaired\", and \"at risk\" levels, respectively. The results of the analysis of variance (ANOVA) comparison demonstrated that the accuracy of the TCN in predicting FMD at the impaired and at-risk levels was significantly higher than that of Long Short-Term Memory (LSTM) networks and Random Forest algorithms; (4) Conclusions: The use of pulse wave data during exercise combined with the TCN for predicting the vascular health status of elderly women demonstrated high accuracy, particularly in predicting impaired and at-risk FMD levels. This indicates that the integration of exercise pulse wave data with TCN can serve as an effective tool for the assessment and monitoring of the vascular health of elderly women.

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users\' stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study.

The educational environment plays a vital role in the development of students who participate in athletic pursuits both in terms of their physical health and their ability to detect fatigue. As a result of recent advancements in deep learning and biosensors benefitting from edge computing resources, we are now able to monitor the physiological fatigue of students participating in sports in real time. These devices can then be used to analyze the data using contemporary technology. In this paper, we present an innovative deep learning framework for forecasting fatigue in athletic students following physical exercise. It addresses the issue of lack of precision computational models and extensive data analysis in current approaches to monitoring students\' physical activity. In our study, we classified fatigue and non-fatigue based on photoplethysmography (PPG) signals. Several deep learning models are compared in the study. Using limited training data, determining the optimal parameters for PPG presents a significant challenge. For datasets containing many data points, several models were trained using PPG signals: a deep residual network convolutional neural network (ResNetCNN) ResNetCNN, an Xception architecture, a bidirectional long short-term memory (BILSTM), and a combination of these models. Training and testing datasets were assigned using a fivefold cross validation approach. Based on the testing dataset, the model demonstrated a proper classification accuracy of 91.8%.

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Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

The development of non-contact techniques for monitoring human vital signs has significant potential to improve patient care in diverse settings. By facilitating easier and more convenient monitoring, these techniques can prevent serious health issues and improve patient outcomes, especially for those unable or unwilling to travel to traditional healthcare environments. This systematic review examines recent advancements in non-contact vital sign monitoring techniques, evaluating publicly available datasets and signal preprocessing methods. Additionally, we identified potential future research directions in this rapidly evolving field.

Photoplethysmography (PPG) is widely utilized in wearable healthcare devices due to its convenient measurement capabilities. However, the unrestricted behavior of users often introduces artifacts into the PPG signal. As a result, signal processing and quality assessment play a crucial role in ensuring that the information contained in the signal can be effectively acquired and analyzed. Traditionally, researchers have discussed signal quality and processing algorithms separately, with individual algorithms developed to address specific artifacts. In this paper, we propose a quality-aware signal processing mechanism that evaluates incoming PPG signals using the signal quality index (SQI) and selects the appropriate processing method based on the SQI. Unlike conventional processing approaches, our proposed mechanism recommends processing algorithms based on the quality of each signal, offering an alternative option for designing signal processing flows. Furthermore, our mechanism achieves a favorable trade-off between accuracy and energy consumption, which are the key considerations in long-term heart rate monitoring.

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates\' determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman\'s coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman\'s coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

Intradialytic hypotension (IDH) is a severe complication of hemodialysis (HD) with a significant impact on morbidity and mortality. In this study, we used a wearable device for the continuous monitoring of hemodynamic vitals to detect hemodynamic changes during HD and attempted to identify IDH. End-stage kidney disease patients were continuously monitored 15 min before starting the session and until 15 min after completion of the session, measuring heart rate (HR), noninvasive cuffless systolic and diastolic blood pressure (SBP and DBP), stroke volume (SV), cardiac output (CO), and systemic vascular resistance (SVR). Data were analyzed retrospectively and included comparing BP measured by the wearable devices (recorded continuously every 5 s) and the cuff-based devices. A total of 98 dialysis sessions were included in the final analysis, and IDH was identified in 22 sessions (22.5%). Both SBP and DBP were highly correlated (r > 0.62, p < 0.001 for all) between the wearable device and the cuff-based measurements. Based on the continuous monitoring, patients with IDH had earlier and more profound reductions in SBP and DBP during the HD treatment. In addition, nearly all of the advanced vitals differed between groups. Further studies should be conducted in order to fully understand the potential of noninvasive advanced continuous monitoring in the prediction and prevention of IDH events.