• Touch Your Patient

    Tactile Display of Physiological Monitoring

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    Each new anesthesia monitor requires careful observation from the clinician to detect any abnormality, exceeding the bounds of human concentration. In addition, observing monitors limits the amount of time that clinicians can spend observing patients directly. To avoid missing an abnormality, alarms are set to attract the clinician's attention to significant state changes. Such alarms are triggered only when a potentially critical situation is imminent - they do not help track small changes in a patient's condition.
    We propose to develop a new means of connecting clinicians with their patients. By bridging the gap between monitoring equipment and human attention, our device would act as a 'tap on the shoulder' prompting scrutiny of monitors at the first sign of change in their patient's conditions, allowing action to be taken before a critical situation develops. To do this without adding to the already busy streams of auditory and visual information, we propose to harness the relatively under-utilized sense of touch. Using technology developed for Virtual Reality, sensory receptors will be stimulated, via a tactile device, to provide pulses of vibration that correspond to changes in the patient. Tactile communication can provide subtle cues, rather than outright alarms. It does not detract from patient observation and will not disturb other individuals in the clinical environment. Our experiments suggest that tactile signals compete more successfully for attention than auditory ones.
  • Online Monitoring of Physiological Parameters in Critical Care

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    A number of different monitors are attached to patients under anesthesia or in the intensive care unit. To detect an abnormality in a patient's state, the clinician must repeatedly look at the numerous monitors. As the number of monitored parameters increases, so the demands on the clinician escalate. The clinician can set simple alarm limits on each monitor to warn of a change in heart rate or a change in blood pressure. However, these alarm limits carry the problem of false alarms as they are based on a single parameter. In addition, small changes in a patient's state, within the alarm limits, are not recognized by the system.
    We propose to apply methods that are used to indicate faults in engineering processes to this problem. These methods have been successfully used in other industries. We want to see if they can be used to provide better indications of faults, or abnormalities, during anesthesia and in the intensive care unit. Using advanced data processing techniques, we hope to support the role of the clinician. To improve on single parameter alarm systems, we also propose to combine multiple parameters in a single monitoring system. A sensitive multi-parameter system is essential as the number of monitors in the clinical environment continues to increase.
    The benefits of the system we are proposing extend beyond the clinician's bedside diagnosis. Currently, each individual monitor collects very large amounts of physiological data. Data from the monitors may be stored for future analysis.
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  • Field Measurement of Human Biological Rhythms

    Development of Ambulatory Tools for the Assessment of Human Circadian Rhythms: An international project

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  • Smart Capnography

    Improving the capnogram processing and alarm generation during anesthesia

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    To ensure safety during anesthesia, anesthesiologists monitor the patient to track changes in heart rate or blood pressure. The monitoring of the carbon dioxide (CO2) concentration in the inhaled and exhaled breath has significantly improved the safety of anesthesia. This measurement is called capnography. The resulting capnogram is an important parameter to survey because it allows for the evaluation of defects in the artificial breathing circuitry, such as a leak in the breathing circuit. The anesthesiologist can detect changes in the capnogram and react appropriately to restore normal condition. However, the anesthesiologist must attend to other tasks and is usually unable to continuously monitor the capnogram. We are developing new methods for analyzing the breathing of the patient with a computerized monitoring system. We seek to develop a smart capnogram analysis system to generate intelligent alarms in abnormal clinical situations.
  • eVENT

    An expert system for detecting ventilatory events during anesthesia

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    In an operating room, there are many monitors that collect and display information about the function of the patient's body. But it is not possible for a person to do their work and watch both the patient and the monitors, all at the same time. Therefore, it is possible to make a mistake or miss something important, like a change in the patient's breathing.
    We are working to develop a computer that has built-in knowledge to help the doctors and nurses to pay attention in the operating room. We have trained the computer to automatically detect important changes in the patient. These changes are combined in a set of rules that help the doctors to make a rapid and correct decision about actions they need to take. The rules include letting the doctor know if there has been a serious problem (such as accidental anesthetic overdose or leaks in the breathing circuit). The rules were developed with the help of expert anesthesiologists. In this study, we are testing the part called eVENT that monitors the patient's breathing.
  • iKnow

    Knowledge Authoring Tool

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    This project is to develop a knowledge authoring tool to be used by the every day anesthesiologist. Rules will be created by the clinician to be used in a real-time decision support inference engine. In addition the tool will provide memory aids, 'just in time' information and encourage the implementation of clinical guidelines. This project is an essential step in the development of an Intelligent Anesthesia Navigator (IAN). It extends the focus of our team who have already developed methods to extract key features from the mass of clinical physiological data and have developed a real time inference engine. We now need to harvest the expert knowledge that can be used in the expert system. The iKnow software is available for download. An introductory video and demonstration can been viewed on online.
  • Neuromuscolar Blockade Control

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    Conventional incremental bolus administration of neuromuscular blocking (NMB) drugs is associated with limitations in intraoperative control, potential delays in recovery, and residual blockade in the postanesthetic period. To overcome such limitations, we developed the Neuromuscular Blockade Advisory System (NMBAS). The NMBAS advises the anesthesiologist on the timing and dose of NMB drugs based on the history of the patient's electromyographic responses. It uses a novel form of modeling that combines model swapping and continuous adaptation to accommodate the patient variation seen with NMB drugs. New methods of handling nonlinearities at the neuromuscular junction to allow application of adaptive control techniques were used. In a clinical trial we have demonstrated that compared to standard practice, NMBAS-guided care was associated with improved NMB quality and higher TOF ratios at the end of surgery, potentially reducing the risk of residual NMB and improving perioperative patient safety. We are developing a closed-loop version of this system.
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  • Epileptic Seizure Detection & Prediction

    Detection and prediction of seizures using scalp EEG signal analysis

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    As one of the most serious brain disorders, epilepsy is associated with recurrent, unprovoked seizures resulting from an abnormal, sudden, synchronous firing of a neural population. The high incidence of this disease among different age groups around the world labels epilepsy as the most common neurological disorder after strokes. Epileptic seizures cause involuntary and temporary disturbances in consciousness and body movements increasing the chance of accidental injury and death. The objective of this project is to develop a real-time algorithm capable of detecting epileptic seizures as well as predicting their onset in advance. Automatic detection of seizures would be helpful since monitoring the patient's electroencephalogram (EEG) for several days is a necessary step in diagnosis and is an expensive and time consuming process. In addition, predicting the onset of seizures may enable clinicians to control seizures. We have already developed a wavelet-based index to discriminate between ictal and interictal periods in scalp EEG signals. This algorithm is based on measuring the rhythmicity and energy of the EEG signal. The preliminary results show the capability of this method in detecting epileptic seizures with high sensitivity and low false detection rate using surface EEG. The next steps are to complete non-linear analysis of the underlying dynamics of EEG time series to find a measure revealing transition among different brain states in patients with epilepsy.
  • Autonomic-Cardiac Regulation Monitoring

    Homeostasis revisited: Autocatalytic loops in the genesis of stress reactivity

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    Autonomic-cardiac regulation contains many parts such as the heart, blood vessels, nerve fibers, and the brainstem which harmonically interact with each other. In fact, autonomic-cardiac regulation operates through interactions between the autonomic nervous system and the cardiovascular system. We aim to develop a physiologically inspired mathematical model of autonomic-cardiac regulation capable of describing physiological interactions within the in vivo system. We, then, develop a non-invasive model-based method to monitor autonomic-cardiac regulation based on a computationally efficient system identification technique using routine clinical measurements such as heart rate, blood pressure, and cardiac output.
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  • iAssist

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    We have developed a software tool (iAssist) to assist clinicians as they monitor the physiological data that guide their actions during anesthesia. The system tracks the statistical properties of multiple dynamic physiological processes and identifies new trend patterns. iAssist has been tested in real-time in the operating room environment.
  • Nociception

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    Pain is conveyed to the brain using the nociception system, causing an increase in activity of the autonomic nervous system (ANS). The ANS responds by activating the sympathetic nervous system (SNS), which creates a stress response in the body. Anesthesiologists try to minimize the stress response, by giving patients drugs that stop nociception. Anesthesiologists rely on the patient's vital signs to estimate the level of ANS activation. With this estimate, they can decide whether to give the patient more or less of the drugs. Unfortunately, these vital signs alone are not enough to estimate ANS activity, because they are often affected by other factors and don't always give a good estimate. Anesthesiologists therefore have to interpret these signs in light of the patient's health and medical history, as well as the type of surgery being performed. We are developing a nociception monitor that automatically determines the level of activation of a patient's ANS. It will use custom algorithms that analyze very small, fast changes in the patient's heart rate, called heart rate variability (HRV). Previous research has shown that HRV responds to ANS activation much more predictably than other vital signs. While it is impossible for an anesthesiologist to monitor HRV, computer systems are well suited to the task.
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  • Data Visualization

    Visual cues to improve change detection in physiological monitoring

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    Monitoring can be jeopardized by the inability of a clinician to recognize important changes in the visual display of data throughout the duration of the monitoring task. We hypothesized that the addition of a visual cue imparting contextual information to a physiological display would improve the detection ability and response time of a clinician to a change in a patient variable. We have investigated the addition of a visual cue for a single variable and the addition of four visual cues to display the interactions between groups of physiological variables, implemented using ecological interface design theory, to a monitor display.
  • Safe Sedation

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    Children are increasingly given sedation to facilitate non-painful or minimally invasive diagnostic and therapeutic procedures. The intravenous anesthetic agent propofol, which has rapidly titratable and predictable sedative characteristics, has gained popularity for sedation procedures in a number of settings. While propofol has many advantages, rapid administration of a loading dose causes significant respiratory depression by impairment of the chemoreceptor response to carbon dioxide (CO2). Respiratory depression may lead to hypoxemia and the need to provide manual positive pressure ventilation. However, if propofol is administered less rapidly, spontaneous ventilation is maintained as the accumulation of CO2 in the blood from ongoing metabolic processes continues to stimulate the chemoreceptors despite their impaired sensitivity. We are proposing to identify a clinical dosing schedule for the administration of propofol in children that will ensure rapid onset of sedation while maintaining spontaneous ventilation.

a place of mind, The University of British Columbia

Electrical and Computer Engineering

Electrical & Computer Engineering in Medicine (ECEM)
Pediatric Anesthesia Research Team, BC Children's Hospital
1L7-4480 Oak Street, Vancouver, BC Canada V6H 3V4
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