Driver fatigue monitoring system using support vector machines

Driver fatigue is one of the leading causes of traffic accidents. This paper presents a real-time non-intrusive fatigue monitoring system which exploits the driver's facial expression to detect and alert fatigued drivers. The presented approach adopts the Viola-Jones classifier to detect the driver's facial features. The correlation coefficient template matching method is then applied to derive the state of each feature on a frame by frame basis. A Support Vector Machine (SVM) is finally integrated within the system to classify the facial appearance as either fatigued or otherwise. Using this simple and cheap implementation, the overall system achieved an accuracy of 95.2%, outperforming other developed systems employing expensive hardware to reach the same objective.peer-reviewe

Impact of sensor and measurement timing errors on model-based insulin sensitivity

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Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) /=3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. RESULTS: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. CONCLUSIONS: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT

Neonatal Glycemia and Neurodevelopmental Outcomes at 2 Years

From McKinlay, C. J. D., Alsweiler, J. M., Ansell, J. M., Anstice, N. S., Chase, J. G., Gamble, G. D., … Harding, J. E. (2015). Neonatal Glycemia and Neurodevelopmental Outcomes at 2 Years. New England Journal of Medicine, 373(16), 1507–1518. https://doi.org/10.1056/NEJMoa1504909 Copyright © 2015 Massachusetts Medical Society. Reprinted with permission.Neonatal hypoglycemia is a common and readily treatable risk factor for neurologic impairment in children. Although associations between prolonged symptomatic neonatal hypoglycemia and brain injury are well established,1 the effect of milder hypoglycemia on neurologic development is uncertain.2 Consequently, large numbers of newborns are screened and treated for low blood glucose concentrations, which involves heel-stick blood tests, substantial costs, and the possibility of iatrogenic harm. Under current guidelines,3 up to 30% of neonates are considered to be at risk for hypoglycemia, 15% receive a diagnosis of hypoglycemia, and approximately 10% require admission to a neonatal intensive care unit,4 costing an estimated $2.1 billion annually in the United States alone.5 Associated formula feeding and possible separation of mother and baby reduce breast-feeding rates,6 with potentially adverse effects on broader infant health and development. In addition, pain-induced stress in neonates, such as repeated heel sticks, may itself impair brain development.7 Thus, to determine appropriate glycemic thresholds for treatment, there have been repeated calls for studies of the effect of neonatal hypoglycemia on long-term development.2,8 We report the results of the Children with Hypoglycaemia and Their Later Development (CHYLD) study, a large prospective cohort study of term and late-preterm neonates born at risk for hypoglycemia. The study investigated the relation between the duration, frequency, and severity of low glucose concentrations in the neonatal period and neuropsychological development at 2 years.Supported by grants from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD069622), the Health Research Council of New Zealand (10-399), and the Auckland Medical Research Foundation (1110009)

Using Continuous Glucose Monitoring Data and Detrended Fluctuation Analysis to Determine Patient Condition: A Review.

Patients admitted to critical care often experience dysglycemia and high levels of insulin resistance, various intensive insulin therapy protocols and methods have attempted to safely normalize blood glucose (BG) levels. Continuous glucose monitoring (CGM) devices allow glycemic dynamics to be captured much more frequently (every 2-5 minutes) than traditional measures of blood glucose and have begun to be used in critical care patients and neonates to help monitor dysglycemia. In an attempt to obtain a better insight relating biomedical signals and patient status, some researchers have turned toward advanced time series analysis methods. In particular, Detrended Fluctuation Analysis (DFA) has been a topic of many recent studies in to glycemic dynamics. DFA investigates the "complexity" of a signal, how one point in time changes relative to its neighboring points, and DFA has been applied to signals like the inter-beat-interval of human heartbeat to differentiate healthy and pathological conditions. Analyzing the glucose metabolic system with such signal processing tools as DFA has been enabled by the emergence of high quality CGM devices. However, there are several inconsistencies within the published work applying DFA to CGM signals. Therefore, this article presents a review and a "how-to" tutorial of DFA, and in particular its application to CGM signals to ensure the methods used to determine complexity are used correctly and so that any relationship between complexity and patient outcome is robust

Continuous Glucose Monitoring and Tight Glycaemic Control in Critically Ill Patients

Critically ill patients often exhibit abnormal glycaemia that can lead to severe complications and potentially death. In critically ill adults, hyperglycaemia is a common problem that has been associated with increased morbidity and mortality. In contrast, critically ill infants often suffer from hypoglycaemia, which may cause seizures and permanent brain injury. Further complicating the matter, both of these conditions are diagnosed by blood glucose (BG) measurements, often taken several hours apart, and, as a result, these conditions can remain poorly managed or go completely undetected. Emerging ‘continuous’ glucose monitoring (CGM) devices with 1-5 minute measurement intervals have the potential to resolve many issues associated with conventional intermittent BG monitoring. The objective of this research was to investigate and develop methods and models to optimise the clinical use of CGM devices in critically ill patients. For critically ill adults, an in-silico study was conducted to quantify the potential benefits of introducing CGM devices into the intensive care unit (ICU). Mathematical models of CGM error characteristics were implemented with existing, clinically validated, models of the insulin-glucose regulatory system, to simulate the behaviour of CGM devices in critically ill patients. An alarm algorithm was also incorporated to provide a warning at the onset of predicted hypoglycaemia, allowing a virtual dextrose intervention to be administered as a preventative measure. The results of the in-silico study showed a potential reduction in nurse workload of approximately 75% and a significant reduction in hypoglycaemia, while also providing insight into the optimal rescue dose size and resulting dynamics of glucose recovery. During 2012, ten patients were recruited into a pilot clinical trial of CGM devices in critical care with a primary goal of assessing the reliability of CGM devices in this environment, with a specific interest in the effects of CGM device type and sensor site on sensor glucose (SG) data. Results showed the mean absolute relative difference of SG data across the cohort was between 12-24% and CGM devices were capable of monitoring some patients with a high degree of accuracy. However, certain illnesses, drugs and therapies can potentially affect sensor performance, and one particular set of results suggested severe oedema may have affected sensor performance. A novel and first of its kind metric, the Trend Compass was developed and used to assesses trend accuracy of SG in a mathematically precise fashion without approximation, and, importantly, does so independent of glucose level or sensor bias, unlike any other such metrics. In this analysis, the trend accuracy between CGM devices was typically good. A recent hypothesis suggesting that glucose complexity is associated with mortality was also investigated using the clinical CGM data. The results showed that complexity results from detrended fluctuation analysis (DFA) were influenced far more by CGM device type than patient outcome. In addition, the location of CGM sensors had no significant effect on complexity results in this data set. Thus, while this emerging analytical method has shown positive results in the literature, this analysis indicates that those results may be misleading given the impact of technology outweighing that of physiology. This particular result helps to further delineate the range of potential applications and insight that CGM devices might offer in this clinical scenario. In critically ill infants, CGM devices were used to investigate hypoglycaemia during the first 48 hours after birth. More than 50 CGM data sets were obtained from several studies of CGM in infants at risk of hypoglycaemia at the Waikato hospital neonatal ICU (NICU). In light of concerns regarding CGM accuracy, particularly during the first few hours of monitoring and/or at low BG levels, an alternative, novel calibration scheme was developed to increase the reliability of SG data. The recalibration algorithm maximised the value of very accurate calibration BG measurements from a blood gas analyser (BGA), by forcing SG data to pass through these calibration BG measurements. Recalibration increased all metrics of hypoglycaemia (number, duration, severity and hypoglycaemic index) as the factory CGM calibration was found to be reporting higher values at low BG levels due to its least squares calibration approach based on the assumption of a less accurate calibration glucose meter. Thus, this research defined new calibration methods to directly optimise the use of CGM devices in this clinical environment, where accurate reference BG measurements are available. Furthermore, this work showed that metrics such as duration or area under curve were far more robust to error than the typically used counted-incidence metrics, indicating how clinical assessment may have to change when using these devices. The impact of errors in calibration measurements on metrics used to classify hypoglycaemia was also assessed. Across the cohort, measurement error, particularly measurement bias, had a larger effect on hypoglycaemia metrics than delays in entering calibration measurements. However, for patients with highly variable glycaemia, timing error can have a significantly larger impact on output SG data than measurement error. Unusual episodes of hypoglycaemia could be successfully identified using a stochastic model, based on kernel density estimation, providing another level of information to aid decision making when assessing hypoglycaemia. Using the developed algorithms/tools, with CGM data from 161 infants, the incidence of hypoglycaemia was assessed and compared to results determined using BG measurements alone. Results from BG measurements showed that ~17% of BG measurements identified hypoglycaemia and over 80% of episodes occurred in the first day after birth. However, with concurrent BG and SG data available, the SG data consistently identified hypoglycaemia at a higher rate suggesting the BG measurements were not capturing some episodes. Duration of hypoglycaemia in SG data varied from 0-10+%, but was typically in the range 4-6%. Hypoglycaemia occurred most frequently on the first day after birth and an optimal measurement protocol for at risk infants would likely involve CGM for the first week after birth with frequent intermittent BG measurements for the first day. Overall, CGM devices have the potential to increase the understanding of certain glycaemic abnormalities and aid in the diagnosis/treatment of other conditions in critically ill patients. This research has used a range of prospective and retrospective clinical studies to develop methods to further optimise the use of CGM devices within the critically ill clinical environment, as well as delineating where they are less useful or less robust. These latter results clearly define areas where clinical practice needs to adapt when using these devices, as well as areas where device makers could target technological improvements for best effect. Although further investigations are required before these devices are regularly implemented in day-to-day clinical practice, as an observational tool they are capable of providing useful information that is not currently available with conventional intermittent BG monitoring

Full of sound and fury, signifying something : the impact of autonomic arousal on EGM gambling

An experiment was conducted to observe the influence of autonomic arousal on subsequent gambling behavior. Thirty-seven male and 32 female regular Electronic Gaming Machine (EGM) players were recruited through newspaper advertisements. Participants were randomly assigned to either: (1) a control condition, or (2) an experimental condition that introduced a loud white-noise event (80 db) at fixed 120 s intervals throughout the 5-min EGM gambling session. Galvanic Skin Response (GSR) measurements showed that the manipulation was successful in elevating autonomic arousal. The results showed differences in behavioral response to the manipulation based on prior experience with gambling problems. Persons with many gambling problems had lower average bet-sizes in the white-noise condition compared to the control, while those with few or no problems had higher average bet-sizes. The results suggest that arousal may provide different signals to gamblers with few versus many problems. Gamblers with many problems may interpret their arousal as a sign that they will soon lose money, while gamblers with few or no problems may associate feelings of arousal exclusively with winning