Background: The data supporting post-operative admission and vital sign monitoring post-bariatric surgery is scarce. This study aimed to determine whether abnormalities in the vital signs of patients ...for the first 24hr after laparoscopic bariatric surgery were significantly correlated with deviation from expected postoperative course. Methods: We retrospectively reviewed records of patients at our high-volume bariatric center. Using univariable/multivariable analysis and stratified analysis we examined association of abnormalities in the vital signs (temperature>100.4, or for three consecutive readings: heart rate>90, systolic blood pressure<90, respiratory rate>24) with deviation from expected postoperative course (LOS>4 days, unplanned returns to OR, emergency room visit and/or readmission within 30 days). Results: A total of 2270 patients were included. Eighty-eight percent were women. Mean age was 37.1±12 years, and mean preoperative BMI was 45.2±7 kg/m2. The majority of patients were Hispanic (59%) and African-Americans (23%). Sixty-seven percent underwent SG, while thirty-three percent underwent RYGB. Eight percent had complicated postoperative course (n=187). There was no significant association between abnormal vital signs and complicated postoperative course for 6, 12, and 24hr of postoperative monitoring. The overall sensitivity of abnormal vital signs was 46.5% for occurrence of complication, and the specificity was 41.4%. The results were replicated in subgroup analysis by bariatric procedure and by specific complication. Conclusions: Twenty-four-hour monitoring of postoperative bariatric patients, alone, was not a sensitive or specific factor in early identification of post-bariatric surgery complications. Vital signs should be used in conjunction with clinical examination and laboratory results for early diagnosis and treatment of complications.
Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO2), by measuring subtle color changes on the skin surface using ...a video camera. For patients in a hospital ward, the main challenges in the development of continuous and robust non-contact monitoring techniques are the identification of time periods and the segmentation of skin regions of interest (ROIs) from which vital signs can be estimated. We propose a deep learning framework to tackle these challenges. Approach: This paper presents two convolutional neural network (CNN) models. The first network was designed for detecting the presence of a patient and segmenting the patient's skin area. The second network combined the output from the first network with optical flow for identifying time periods of clinical intervention so that these periods can be excluded from the estimation of vital signs. Both networks were trained using video recordings from a clinical study involving 15 pre-term infants conducted in the high dependency area of the neonatal intensive care unit (NICU) of the John Radcliffe Hospital in Oxford, UK. Main results: Our proposed methods achieved an accuracy of 98.8% for patient detection, a mean intersection-over-union (IOU) score of 88.6% for skin segmentation and an accuracy of 94.5% for clinical intervention detection using two-fold cross validation. Our deep learning models produced accurate results and were robust to different skin tones, changes in light conditions, pose variations and different clinical interventions by medical staff and family visitors. Significance: Our approach allows cardio-respiratory signals to be continuously derived from the patient's skin during which the patient is present and no clinical intervention is undertaken.
Vital signs, including heart rate and body temperature, are useful in detecting or monitoring medical conditions, but are typically measured in the clinic and require follow-up laboratory testing for ...more definitive diagnoses. Here we examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models. Our results demonstrate that vital sign data collected from wearables give a more consistent and precise depiction of resting heart rate than do measurements taken in the clinic. Vital sign data collected from wearables can also predict several clinical laboratory measurements with lower prediction error than predictions made using clinically obtained vital sign measurements. The length of time over which vital signs are monitored and the proximity of the monitoring period to the date of prediction play a critical role in the performance of the machine learning models. These results demonstrate the value of commercial wearable devices for continuous and longitudinal assessment of physiological measurements that today can be measured only with clinical laboratory tests.
Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. ...Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject's body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.
Continuous monitoring of vital signs by using wearable wireless devices may allow for timely detection of clinical deterioration in patients in general wards in comparison to detection by standard ...intermittent vital signs measurements. A large number of studies on many different wearable devices have been reported in recent years, but a systematic review is not yet available to date.
The aim of this study was to provide a systematic review for health care professionals regarding the current evidence about the validation, feasibility, clinical outcomes, and costs of wearable wireless devices for continuous monitoring of vital signs.
A systematic and comprehensive search was performed using PubMed/MEDLINE, EMBASE, and Cochrane Central Register of Controlled Trials from January 2009 to September 2019 for studies that evaluated wearable wireless devices for continuous monitoring of vital signs in adults. Outcomes were structured by validation, feasibility, clinical outcomes, and costs. Risk of bias was determined by using the Mixed Methods Appraisal Tool, quality assessment of diagnostic accuracy studies 2nd edition, or quality of health economic studies tool.
In this review, 27 studies evaluating 13 different wearable wireless devices were included. These studies predominantly evaluated the validation or the feasibility outcomes of these devices. Only a few studies reported the clinical outcomes with these devices and they did not report a significantly better clinical outcome than the standard tools used for measuring vital signs. Cost outcomes were not reported in any study. The quality of the included studies was predominantly rated as low or moderate.
Wearable wireless continuous monitoring devices are mostly still in the clinical validation and feasibility testing phases. To date, there are no high quality large well-controlled studies of wearable wireless devices available that show a significant clinical benefit or cost-effectiveness. Such studies are needed to help health care professionals and administrators in their decision making regarding implementation of these devices on a large scale in clinical practice or in-home monitoring.
Wearable technologies will play an important role in advancing precision medicine by enabling measurement of clinically-relevant parameters describing an individual's health state. The lifestyle and ...fitness markets have provided the driving force for the development of a broad range of wearable technologies that can be adapted for use in healthcare. Here we review existing technologies currently used for measurement of the four primary vital signs: temperature, heart rate, respiration rate, and blood pressure, along with physical activity, sweat, and emotion. We review the relevant physiology that defines the measurement needs and evaluate the different methods of signal transduction and measurement modalities for the use of wearables in healthcare.
Continuous monitoring of vital signs such as respiration and heart rate is essential to detect and predict conditions that may affect the patient's well‐being. To detect these vital signs most ...medical systems use contact sensors. They are not feasible for long term monitoring and are not repeatable. Vital signs using facial video‐noncontact monitoring are becoming increasingly important. Researchers in the last few years although considerable progress has been made, challenging datasets absence timing of assessment process and the technology still has some limitations such as time consuming nature and lack of computer portability. To solve those problems, we propose a contactless video based vital signs detection framework for continuous health monitoring using feature optimization and hybrid neural network. In the proposed technique, modified war strategy optimization algorithm is proposed to segment the face portion from the input video frames. Then, we utilize the known data acquisition models to extract vital signs from the segmented face portions are heart rate, blood pressure, respiratory rate and oxygen saturation. An improved neural network structure (Lifting Net) is further used to achieve the adaptive extraction of deep hidden features for specific signs, for realizing the high precision of human health monitoring. The Hughes effect or dimensionality issue affects detection accuracy in sign classification when there are fewer training instances relative to the number of spectral features. The problem can be overcome through feature optimization here Northern goshawk optimization algorithm is used to select optimal best features which reduces the data dimensionality issue. Furthermore, hybrid deep ensemble reinforcement learning classifier is proposed for the human vital sign detection and classification which ensures the early detection of patient abnormality. Finally, we validate our framework using benchmark video datasets such as TokyoTechrPPG, PURE and COHFACE. To proves the effectiveness of proposed technique using simulation results and comparative analysis.
Existing vital sign monitoring systems in the neonatal intensive care unit (NICU) require multiple wires connected to rigid sensors with strongly adherent interfaces to the skin. We introduce a pair ...of ultrathin, soft, skin-like electronic devices whose coordinated, wireless operation reproduces the functionality of these traditional technologies but bypasses their intrinsic limitations. The enabling advances in engineering science include designs that support wireless, battery-free operation; real-time, in-sensor data analytics; time-synchronized, continuous data streaming; soft mechanics and gentle adhesive interfaces to the skin; and compatibility with visual inspection and with medical imaging techniques used in the NICU. Preliminary studies on neonates admitted to operating NICUs demonstrate performance comparable to the most advanced clinical-standard monitoring systems.
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