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Time-series features are the characteristics of data periodically collected over time. The calculation of time-series features helps in understanding the underlying patterns and structure of the data, as well as in visualizing the data. The manual calculation and selection of time-series feature from a large temporal dataset are time-consuming. It requires researchers to consider several signal-processing algorithms and time-series analysis methods to identify and extract meaningful features from the given time-series data. These features are the core of a machine learning-based predictive model and are designed to describe the informative characteristics of the time-series signal. For accurate stress monitoring, it is essential that these features are not only informative but also well-distinguishable and interpretable by the classification models. Recently, a lot of work has been carried out on automating the extraction and selection of times-series features. In this paper, a correlation-based time-series feature selection algorithm is proposed and evaluated on the stress-predict dataset. The algorithm calculates a list of 1578 features of heart rate and respiratory rate signals (combined) using the tsfresh library. These features are then shortlisted to the more specific time-series features using Principal Component Analysis (PCA) and Pearson, Kendall, and Spearman correlation ranking techniques. A comparative study of conventional statistical features (like, mean, standard deviation, median, and mean absolute deviation) versus correlation-based selected features is performed using linear (logistic regression), ensemble (random forest), and clustering (k-nearest neighbours) predictive models. The correlation-based selected features achieved higher classification performance with an accuracy of 98.6% as compared to the conventional statistical feature's 67.4%. The outcome of the proposed study suggests that it is vital to have better analytical features rather than conventional statistical features for accurate stress classification. [ABSTRACT FROM AUTHOR]

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the "Stress-Predict Dataset", created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring. [ABSTRACT FROM AUTHOR]

Security constrained unit commitment is an essential part of the day-ahead energy markets. The presence of discrete and continuous variables makes it a complex, mixed-integer, and time-hungry optimization problem. Grid operators solve unit commitment problems multiple times daily with only minor changes in the operating conditions. Solving a large-scale unit commitment problem requires considerable computational effort and a reasonable time. However, the solution time can be improved by exploiting the fact that the operating conditions do not change significantly in the day-ahead market clearing. Therefore, in this paper, a novel multi-layer data-driven approach is proposed, which significantly improves the solution time (90% time-reduction on average for the three studied systems). The proposed approach not only provides a near-optimal solution (<1% optimality gap) but also ensures that it is feasible for the stable operation of the system (0% infeasible predicted solutions). The efficacy of the developed algorithm is demonstrated through numerical simulations on three test systems, namely a 4-bus system and the IEEE 39-bus and 118-bus systems, and promising results are obtained. [ABSTRACT FROM AUTHOR]

Brand placement has gradually increased in its importance due to the diversity of marketing communication strategies used by the brand leaders. The product manufacturers have developed a strong competition regarding placing their goods in the media text. To understand brand placement techniques, the present study analyzed 16 top-grossing Pakistani films, from 2014 to 2018. The content analysis was used to investigate product categories, brand visibility, visual placement, brand association with characters, context of placement, and integration of brand with the storyline in the film content. The findings revealed fast-food restaurants and banks as brand leaders in the overall data. The visual placements, denoted as seen placement, were abundant, with the placements mostly in neutral scenes. Overall, there was a lack of association of brands with the film characters. Over a period of time, there was an increase in the integration of brand placement to the storyline and central placement was prevalent. Contributing to scarcity of literature on brand placement in Pakistan, the study provided insight from the example of a struggling film industry which started its revival just about 20 years ago. [ABSTRACT FROM AUTHOR]

Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios, speedy assessment of food resources from agricultural land is critical as it supports aid activity in the disaster‐hit areas. In this article, a deep learning approach was presented for the detection and segmentation of coconut trees in aerial imagery provided through the AI competition organised by the World Bank in collaboration with OpenAerialMap and WeRobotics. Masked Region‐based Convolution Neural Network (Mask R‐CNN) approach was used for identification and segmentation of coconut trees. For the segmentation task, Mask R‐CNN model with ResNet50 and ResNet101 based architectures was used. Several experiments with different configuration parameters were performed and the best configuration for the detection of coconut trees with more than 90% confidence factor was reported. For the purpose of evaluation, Microsoft COCO dataset evaluation metric namely mean average precision (mAP) was used.An overall 91% mean average precision for coconut trees' detection was achieved. [ABSTRACT FROM AUTHOR]

Electrochemically based technologies are rapidly moving from the laboratory to bedside applications and wearable devices, like in the field of cardiovascular disease. Major efforts have focused on the biosensor component in contrast with those employed in creating more suitable detection algorithms for long-term real-world monitoring solutions. The calibration curve procedure presents major limitations in this context. The objective is to propose a new algorithm, compliant with current clinical guidelines, which can overcome these limitations and contribute to the development of trustworthy wearable or telemonitoring solutions for home-based care. A total of 123 samples of phosphate buffer solution were spiked with different concentrations of troponin, the gold standard method for the diagnosis of the acute coronary syndrome. These were classified as normal or abnormal according to established clinical cut-off values. Off-the-shelf screen-printed electrochemical sensors and cyclic voltammetry measurements (sweep between -1 and 1 V in a 5 mV step) was performed to characterize the changes on the surface of the biosensor and to measure the concentration of troponin in each sample. A logistic regression model was developed to accurately classify these samples as normal or abnormal. The model presents high predictive performance according to specificity (94%), sensitivity (92%), precision (92%), recall (92%), negative predictive value (94%) and F-score (92%). The area under the curve of the precision-recall curve is 97% and the positive and negative likelihood ratios are 16.38 and 0.082, respectively. Moreover, high discriminative power is observed from the discriminate odd ratio (201) and the Youden index (0.866) values. The promising performance of the proposed algorithm suggests its capability to overcome the limitations of the calibration curve procedure and therefore its suitability for the development of trustworthy home-based care solutions. [ABSTRACT FROM AUTHOR]

Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies. [ABSTRACT FROM AUTHOR]

Physiological pressure measurement is one of the most common applications of sensors in healthcare. Particularly, continuous pressure monitoring provides key information for early diagnosis, patient-specific treatment, and preventive healthcare. This paper presents a thin-film flexible wireless pressure sensor for continuous pressure measurement in a wide range of medical applications but mainly focused on interface pressure monitoring during compression therapy to treat venous insufficiency. The sensor is based on a pressure-dependent capacitor (C) and printed inductive coil (L) that form an inductor-capacitor (LC) resonant circuit. A matched reader coil provides an excellent coupling at the fundamental resonance frequency of the sensor. Considering varying requirements of venous ulceration, two versions of the sensor, with different sizes, were finalized after design parameter optimization and fabricated using a cost-effective and simple etching method. A test setup consisting of a glass pressure chamber and a vacuum pump was developed to test and characterize the response of the sensors. Both sensors were tested for a narrow range (0–100 mmHg) and a wide range (0–300 mmHg) to cover most of the physiological pressure measurement applications. Both sensors showed good linearity with high sensitivity in the lower pressure range <100 mmHg, providing a wireless monitoring platform for compression therapy in venous ulceration. [ABSTRACT FROM AUTHOR]

Diagnosing and treating acute coronary syndromes consumes a significant fraction of the healthcare budget worldwide. The pressure on resources is expected to increase with the continuing rise of cardiovascular disease, other chronic diseases and extended life expectancy, while expenditure is constrained. The objective of this review is to assess if home-based solutions for measuring chemical cardiac biomarkers can mitigate or reduce the continued rise in the costs of ACS treatment. A systematic review was performed considering published literature in several relevant public databases (i.e., PUBMED, Cochrane, Embase and Scopus) focusing on current biomarker practices in high-risk patients, their cost-effectiveness and the clinical evidence and feasibility of implementation. Out of 26,000 references screened, 86 met the inclusion criteria after independent full-text review. Current clinical evidence highlights that home-based solutions implemented in primary and secondary prevention reduce health care costs by earlier diagnosis, improved patient outcomes and quality of life, as well as by avoidance of unnecessary use of resources. Economical evidence suggests their potential to reduce health care costs if the incremental cost-effectiveness ratio or the willingness-to-pay does not surpass £20,000/QALY or €50,000 limit per 20,000 patients, respectively. The cost-effectiveness of these solutions increases when applied to high-risk patients. [ABSTRACT FROM AUTHOR]

Hypertension is a major driver of cardiovascular disease with a prevalence of 32-34% in adults worldwide. This poses a formidable unmet challenge for healthcare systems, highlighting the need for enhanced treatment strategies. Since 2017, eight major sham-controlled randomised controlled trials have examined the effectiveness and safety of renal denervation (RDN) as therapy for BP control. Although most trials demonstrated a reduction in systolic 24-hour/daytime ambulatory BP compared to control groups, open to discussion is whether major adverse cardiovascular events (MACE)-driven RDN trials are necessary or whether the proof of BP reduction as a surrogate for better cardiovascular outcomes is sufficient. We conducted an analysis of the statistical methods used in various trials to assess endpoint definitions and determine the necessity for MACE-driven outcome data. Such comprehensive analysis provides further evidence to confidently conclude that RDN significantly reduces blood pressure compared to sham controls. Importantly, this enables the interpolation of RDN trial endpoints with other studies that report on outcome data, such as pharmacological trials which demonstrate a significant reduction in MACE risk with a decrease in BP. Moreover, limitations associated with directly evaluating outcome data further support the use of BP as a surrogate endpoint. For example, conducting lengthier trials with larger numbers of participants to ensure robust statistical power presents a substantial challenge to evaluating outcome data. Thus, in light of the crucial need to tackle hypertension, there are notable advantages of considering BP as a surrogate for outcome data., (© 2024. The Author(s).)

Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of sensor and reference data during the development phase. One of the most challenging tasks in physiological or pathological stress monitoring is the labeling of the physiological signals collected during an experiment. Commonly, different types of self-reporting questionnaires are used to label the perceived stress instances. These questionnaires only capture stress levels at a specific point in time. Moreover, self-reporting is subjective and prone to inaccuracies. This paper explores the potential feasibility of unsupervised learning clustering classifiers such as Affinity Propagation, Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH), K-mean, Mini-Batch K-mean, Mean Shift, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) for implementation in stress monitoring wearable devices. Traditional supervised machine learning (linear, ensembles, trees, and neighboring models) classifiers require hand-crafted features and labels while on the other hand, the unsupervised classifier does not require any labels of perceived stress levels and performs classification based on clustering algorithms. The classification results of unsupervised machine learning classifiers are found comparable to supervised machine learning classifiers on two publicly available datasets. The analysis and results of this comparative study demonstrate the potential of unsupervised learning for the development of non-invasive, continuous, and robust detection and monitoring of physiological and pathological stress., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Iqbal, Elahi, Wijns and Shahzad.)

Aims: Ambulatory video-EEG monitoring has been utilized as a cost-effective alternative to inpatient video-EEG monitoring for non-surgical diagnostic evaluation of symptoms suggestive of epileptic seizures. We aimed to assess incidence of epileptiform discharges in ambulatory video-EEG recordings according to seizure symptom history obtained during clinical evaluation., Methods: This was a retrospective cohort study. We queried seizure symptoms from 9,221 consecutive ambulatory video-EEG studies in 35 states over one calendar year. We assessed incidence of epileptiform discharges for each symptom, including symptoms that conformed to a category heading, even if not included in the ILAE 2017 symptom list. We report incidences, odds ratios, and corresponding p values using Fisher's exact test and univariate logistic regression. We applied multivariable logistic regression to generate odds ratios for the six symptom categories that are controlled for the presence of other symptoms., Results: History that included motor symptoms (OR=1.53) or automatisms (OR=1.42) was associated with increased occurrence of epileptiform discharges, whereas history of sensory symptoms (OR=0.76) predicted lack of epileptiform discharges. Patient-reported symptoms that were associated with increased occurrence of epileptiform discharges included lip-smacking, moaning, verbal automatism, aggression, eye-blinking, déjà vu, muscle pain, urinary incontinence, choking and jerking. On the other hand, auditory hallucination memory deficits, lightheadedness, syncope, giddiness, fibromyalgia and chronic pain predicted absence of epileptiform discharges. The majority of epileptiform discharges consisted only of interictal sharp waves or spikes., Conclusions: Our study shows that the use of ILAE 2017 symptom categories may help guide ambulatory video-EEG studies.