Notes

Double heteroatoms doped SBA-15 templated permeable co2 for symmetric supercapacitor throughout two redox additive electrolyte.
e., SIPS-Btotal), obtaining a Pearson correlation of 0.77 for all the subjects after cross-validated evaluation. In addition, these features can predict development of psychosis with high accuracy above 90%, outperforming classification using clinical variables only. This improved predictive power ultimately can help provide early treatment and improve quality of life for those at risk for developing psychosis.Sleep quality (SQ) is one of the most well-known factors in daily work performance. Sleep is usually analyzed using polysomnography (PSG) by attaching electrodes to the bodies of participants, which is likely sleep destructive. As a result, investigating SQ using a more easy-to-use and cost-effective methodology is currently a hot topic. To avoid overfitting concerns, one likely methodology for predicting SQ can be achieved by reducing the number of utilized signals. In this paper, we propose three methodologies based on electronic health records and heart rate variability (HRV). To evaluate the performance of the proposed methods, several experiments have been conducted using the Osteoporotic Fractures in Men (MrOS) sleep dataset. The experimental results reveal that a deep neural network methodology can achieve an accuracy of 0.6 in predicting light, medium, and deep SQ using only ECG signals recorded during PSG. This outcome demonstrates the capability of using HRV features, which are effortlessly measurable by easy-to-use and cost-effective wearable devices, in predicting SQ.Malignant ventricular arrhythmia (especially ventricular fibrillation (VF)) is the main reason which causes sudden cardiac death (SCD). This paper presents an automatic SCD-patient classifier we developed to identify patients with unexpected VF using 60-minutes continuous single-lead electrocardiograms (ECG) signals before that. Patients are classified as having SCD if the majority of their recorded ventricular repolarization (VR) is recognized as characteristic of unexpected VF. Thus, the classifier's underlying task is to recognize individual VR delineated from single-lead ECG signals as SCD VR, where VR from non-SCD patients are used as controls. With the reported clinical practices of SCD, we extracted five morphological and temporal features (both commonly used and newly developed ones) from ECG signals for VR classification. To evaluate classification performance, we trained and tested k nearest neighbor classifier, a decision tree classifier, and a Naïve Bayes classifier using five-fold cross validation on 36 one-hour ECG signals (18 from patients at risk of SCD and 18 from control people). We compared the performance of these three classifiers, and the patient-classification sensitivity is approximately 98.02-99.51%. Moreover, the k nearest neighbor with a higher accuracy (98.89%) and specificity (98.27%) performed better than the other two. Importantly, the results show obvious superiorities of performance over that in the same duration and of usefulness over several minutes given by related works.Clinical Relevance- This could be integrated into a real-time, long-term out-of-hospital SCD predictor to improve the warning veracity and bring forward the warning time, especially for patients with implantable cardiac defibrillators or pacemakers, etc..Wandering pattern classification is important for early recognition of cognitive deterioration and other health conditions in people with dementia (PWD). In this paper, we leverage the orientation data available on mobile devices to recognize dementia-related wandering patterns. In particular, we propose to use deep learning (DL) with long short-term memory networks (LSTM) as classifiers for detecting travel patterns including direct, pacing, lapping and random. Experimental results on a real dataset collected from 14 subjects show that deep LSTM classifiers perform better than traditional machine learning (ML) classifiers. Our proposed method can thus be potentially used in healthcare applications for dementia related wandering monitoring and management.Clinical Relevance- This demonstrates the potential of using readily available yet non-privacy information to detect dementia-related wandering patterns with high accuracy.The prevalence of personal health data from wearable devices enables new opportunities to understand the impact of behavioral factors on health. Unlike consumer devices that are often auxiliary, such as Fitbit and Garmin, wearable medical devices like continuous glucose monitoring (CGM) devices and insulin pumps are becoming critical in diabetes care to minimize the occurrence of adverse glycemic events. Joint analysis of CGM and insulin pump data can provide unparalleled insights on how to modify treatment regimen to improve diabetes management outcomes. In this paper, we employ a data-driven approach to study the relationship between key behavioral factors and proximal diabetic management indicators. ARV471 order Our dataset includes an average of 161 days of time-matched CGM and insulin pump data from 34 subjects with Type 1 Diabetes (T1D). By employing hypothesis testing and association mining, we observe that smaller meals and insulin doses are associated with better glycemic outcomes compared to larger meals and insulin doses. Meanwhile, the occurrence of interrupted sleep is associated with poorer glycemic outcomes. This paper introduces a method for inferring disrupted sleep from wearable diabetes-device data and provides a baseline for future research on sleep quality and diabetes. This work also provides insights for development of decision-support tools for improving short- and long-term outcomes in diabetes care.Prolonged influence of negative emotions can result in clinical depression or anxiety, and while many prescribed techniques exist, music therapy approaches, coupled with psychotherapy, have shown to help lower depressive symptoms, supplementing traditional treatment approaches. Identifying the appropriate choice of music, therefore, is of utmost importance. Selecting appropriate playlists, however, are challenged by user feedback that may inadvertently select songs that amplify the negative effects. Therefore, this work uses electroencephalogram (EEG) that automatically identifies the emotional impact of music and trains a reinforcement-learning approach to identify an adaptive personalized playlist of music to lead to improved emotional states. This work uses data from 32 users, collected in the publicly available DEAP dataset, to select songs for users that guide them towards joyful emotional states. Using a domain-specific reward-shaping function, a Q-learning agent is able to correctly guide a majority of users to the target emotional states, represented in a common emotion wheel.
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