Notes

Photo-crosslinked hyaluronic acid hydrogel like a biomimic extracellular matrix in order to recapitulate inside vivo features of cancers of the breast tissues.
This algorithm may also have comparative results with state-of-the-art methods after introducing lossless encoding, which is hardly absent from the latter. This study indicates the high potential of using stochastic modeling in PPG compression, especially for reflective PPG collected by wearable devices where the amplitudes of signals can be significantly affected by respiration.Clinical Relevance-This research establishes a new approach of photoplethysmography compression, which contributes to remote and telehealth monitoring in wearable devices.Over the last decade Vibro-Acoustic Therapy (VAT) was used for several clinical applications. This paper investigates the use of AcusticA®, an innovative VAT solution represented by a wooden chaise longue that follows the construction principles of a "musical instrument that stimulates the whole body" in relation to the sound frequencies produced by the music tracks. Ten healthy young subjects were enrolled for this study. Wearable sensors were used to monitor the human physiological response during the VAT session but also during a traditional acoustic therapy (AT) to highlight similarity and differences of those stimulations. Signals from heart activity, brain activity and electrodermal activity were analyzed to investigate the response during the non-stimulated and the stimulated phases. Additionally, two supervised classification algorithms were used to investigate whether the extracted instances could be grouped into two different groups. The results identify a trend of the attention and meditation features extracted from brain activity, which pointed out the relax efficacy of the VAT.Clinical Relevance - There are not significant differences (p less then 0.05) in the physiological response between the VAT and the AT stimulation, but during the VAT the alpha coefficients were significant different during the stimulated phase. Finally, the classification algorithms were able to classify the groups with an accuracy equal to 100% in the best case.A challenge to solve when analyzing multimorbidity patterns in elderly people is the management of a high number of characteristics associated with each patient. The main variables to study multimorbidity are diseases, however other variables should be considered to better classify the people included in each pattern. Age, sex, social class and medication are frequently used in the typing of each multimorbidity pattern. Subsequently the cardinality of the set of features that characterize a patient is very high and normally, the set is compressed to obtain a patient vector of new variables whose dimension is noticeably smaller than that of the initial set. To minimize the loss of information by compression, traditionally Principal Component Analysis (PCA) based projection techniques have been used, which although they are generally a good option, the projection is linear, which somehow reduces its flexibility and limits the performance. As an alternative to the PCA based techniques, in this paper, it is proposed to use autoencoders, and it is shown the improvement in the obtained multimorbidity patterns from the compressed database, when the registered data on about a million patients (5 years' follow-up) are processed. This work demonstrates that autoencoders retain a larger amount of information in each pattern and results are more consistent with clinical experience than other approaches frequently found in the literature.Clinical relevance- From an epidemiological perspective, the contribution is relevant, since it allows for a more precise analysis of multimorbidity patterns, leading to better approaches to patient health strategies.With the quick development of dry electrode electroencephalography (EEG) acquisition technology, EEG-based sleep quality evaluation attracts more attention for its objective and quantitative merits. However, there hasn't been a standard experimental paradigm. This situation hinders the development of sleep quality evaluation method and technique. In this paper, we experimentally examine the performance of four typical experimental paradigms for EEG-based sleep quality evaluation and develop a new EEG dataset recorded by dry-electrode headset. MRTX849 To eliminate individual variation caused by subjects, we evaluate the four experimental paradigms using domain adaptation (DA) methods. Experimental results demonstrate that a relaxing paradigm is more effective than other attention concentration paradigms and achieves the average accuracy of 76.01%. Domain Adversarial Neural Network outperforms other DA methods and obtains 18.69% improvement on accuracy compared with transfer component analysis.Video-based motion analysis gave rise to contactless respiration rate monitoring that measures subtle respiratory movement from a human chest or belly. In this paper, we revisit this technology via a large video benchmark that includes six categories of practical challenges. We analyze two video properties (i.e. pixel intensity variation and pixel movement) that are essential for respiratory motion analysis and various signal extraction approaches (i.e. from conventional to recent Convolutional Neural Network (CNN)-based methods). We find that pixel movement can better quantify respiratory motion than pixel intensity variation in various conditions. We also conclude that the simple conventional approach (e.g. Zerophase Component Analysis) can achieve better performance than CNN that uses data training to define the extraction of respiration signal, which thus raises a more general question whether CNN can improve video-based physiological signal measurement.Early inter-hospital ambulance transport of premature babies is associated with more severe brain injury. The mechanism is unclear, but they are exposed to excessive noise and vibration. Smart-routing may help minimise these exposure levels and potentially improve outcomes.An app for Android smartphones was developed to collect vibration, noise and location data during ambulance journeys. Four smartphones, with the app installed, were provided to the local neonatal transport group to attach to their incubator trolleys. An example of route comparison was performed on the roads used between Nottingham City Hospital (NCH) and Leicester Royal Infirmary (LRI).Almost 1,700 journeys were recorded over the space of a year. 39 of these journeys travelled from NCH to LRI, comprising of 9 different routes. Analysis was performed on all recorded data which travelled along each road. For routes from NCH to LRI, the route with least vibration was also the quickest. Noise levels, however, were found to increase with vehicle speed.
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