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
Real-time evaluation of patient monitoring algorithms for critical care at the bedside.
Rapid interpretation of physiological time-series data and accurate assessment of patient state are crucial to patient monitoring in critical care. Algorithms that use artificial intelligence techniques have the potential to help achieve these tasks, but their development requires well-annotated patient data. ⋯ The alarm annotations in real time at the bedside indicate that about 89% of these alarms were clinically-relevant true positives; 6% were true positives without clinical relevance; and 5% were false positives. These findings show an improved specificity of the alarm algorithms in the newer generation of bedside monitoring systems and demonstrate that the designed data acquisition system enables real-time evaluation of patient monitoring algorithms for critical care.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Optimizing the tactile display of physiological information: vibro-tactile vs. electro-tactile stimulation, and forearm or wrist location.
Anesthesiologists use physiological data monitoring systems with visual and auditory displays of information to monitor patients in the operating room (OR). The efficacy of visual-audio systems may impose an increase in patient risk when the demand for constant switching of attention between the patient and the visual monitoring system is high. This is evidenced by auditory alarms frequently being neglected in a noisy OR environment. ⋯ A post-study questionnaire was completed by each subject to assess the comfort and usability of the three prototypes. We found that both VF and VW were superior to the EF in both accuracy and comfort and, that there were no differences between the wrist and the forearm. In conclusion, the tactile-display prototypes designed to alert the clinician of adverse changes in a patient's physiological state efficaciously and unobtrusively delivered these data and warranted further investigation and development.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Estimation of effects of visually-induced motion sickness using independent component analysis.
To quantify the effect of visually-induced motion sickness on the autonomic nervous system, the authors previously proposed a new physiological index rho max representing the maximum cross-correlation coefficient between blood pressure variability and heart rate variability whose frequency components are limited to the Mayer wave band. However, rho max requires measurement of continuous blood pressure with an expensive and bulky measuring device. In the present study, an easier method for obtaining rho max with measurement of neither continuous blood pressure nor ECG but using finger photoplethysmography (PPG) only has been developed. ⋯ Two experiments in which subjects performed the Valsalva maneuver and then they watched a swaying video image were carried out to evaluate the adequacy of the proposed method. The experimental results have shown that the proposed method worked successfully as good as the conventional method. This means that the proposed method can contribute to increase in the number of subjects because multiple subjects can be used even in a single experiment.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Eigenvector methods for analysis of human PPG, ECG and EEG signals.
This paper presents eigenvector methods for analysis of the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The features representing the PPG, ECG, EEG signals were obtained by using the eigenvector methods. In addition to this, the problem of selecting relevant features among the features available for the purpose of discrimination of the signals was dealt with. Some conclusions were drawn concerning the efficiency of the eigenvector methods as a feature extraction method used for representing the signals under study.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Comparative Study Clinical TrialClassifying depth of anesthesia using EEG features, a comparison.
Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. ⋯ The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and Bayesian classifier.