Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Modeling state entropy of the EEG and auditory evoked potentials: hypnotic and analgesic interactions.
Because of the complexity of raw electroencephalogram (EEG), for the anesthesiologist it is very difficult to evaluate the patient's hypnosis state. Because of this, several depth of anesthesia monitors have been developed, and are in current use at the operating room (OR). These monitors convert the information supplied by the EEG or derived signals into a simple, easy to understand index. ⋯ Hypnotic and analgesic drugs interact in different ways throughout the anaesthesia stages. The models obtained captured the different dynamic interaction of drugs, during the induction and maintenance phases, demonstrating that the model must have incorporated all this information in order to perform satisfactorily. Other information like haemodynamic variables might be included in the search for the optimum model.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Changes in Heart Rate Variability in patients under local anesthesia.
Spectral analysis of Heart Rate Variability (HRV) is widely used for the assessment of cardiovascular autonomic control. Several studies have shown the effect of anesthetic agents on HRV parameters. In this study a systematic approach of HRV analysis has been employed. ⋯ Using this methodology electrocardiogram (ECG) signals from 14 patients undergoing local anesthesia (brachial plexus block) were analyzed with parametric Autoregressive (AR) method. The results showed that the LF/HF ratio values calculated from the HRV signal decreases within an hour of the application of the brachial plexus block compared to the values at the start of the procedure. This change was noticed in approximately 80% of the patients.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Intelligent monitoring of critical pathological events during anesthesia.
Expert algorithms in the field of intelligent patient monitoring have rapidly revolutionized patient care thereby improving patient safety. Patient monitoring during anesthesia requires cautious attention by anesthetists who are monitoring many modalities, diagnosing clinically critical events and performing patient management tasks simultaneously. The mishaps that occur during day-to-day anesthesia causing disastrous errors in anesthesia administration were classified and studied by Reason [1]. ⋯ When detecting absolute hypovolaemia (AHV), moderate level of agreement was observed between RT-SAAM and the human expert (anesthetist) during surgical procedures. RT-SAAM is a clinically useful diagnostic tool which can be easily modified for diagnosing additional critical pathological events like relative hypovolaemia, fall in cardiac output, sympathetic response and malignant hyperpyrexia during surgical procedures. RT-SAAM is currently being tested at the Auckland City Hospital with ethical approval from the local ethics committees.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Real-time development of patient-specific alarm algorithms for critical care.
The state-of-the-art monitoring systems for critical care measure vital signs and generate alerts based on the logic of general patient population models, but they lack the capabilities of accurately correlating physiological data with clinical events and of adapting to individual patient's characteristics that do not fit the population models. This research examines the feasibility of developing patient-specific alarm algorithms in real time at the bedside and evaluates the potential of these algorithms in helping improve patient monitoring. Modular components that facilitate real-time development of alarm algorithms were added to a system that simultaneously collects physiological data and clinical annotations at the bedside. ⋯ The performance of patient-specific alarm algorithms improved as training data increased. Neural networks with eight hours of training data on average achieved a sensitivity of 0.96, a specificity of 0.99, a positive predictive value of 0.79, and an accuracy of 0.99; these figures were 0.84, 0.98, 0.72, and 0.98 respectively for the classification trees. These results suggest that real-time development of patient-specific alarm algorithms is feasible using machine learning techniques.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Automatic identification of spike-wave events and non-convulsive seizures with a reduced set of electrodes.
Epileptiform activity in the brain, whether localized or generalized, constitutes an important category of abnormal electroencephalogram (EEG). Seizures are episodes of relatively brief disturbances of mental, motor or sensory activity caused by paroxysmal cerebral activity. They are not always accompanied by the characteristic convulsions that we commonly associate with the word epilepsy. ⋯ The proposed signal processing algorithm is based on the detection of spike-wave events obtained from a wavelet analysis of the EEG signal, combined with an analysis of the complexity of the EEG using fractal dimension estimates. We show that this algorithm has excellent sensitivity and specificity. In particular, the fractal analysis is a key factor in the removal of falsely detected spike-wave events (false positives) that can be caused by voluntary or involuntary artifacts such as fast eyelid flutter.