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
Comparison of cardiac output monitoring methods for detecting central hypovolemia due to lower body negative pressure.
Reduction in mean arterial pressure (MAP) is a late indictor of progressive circulatory pathology. Non-invasive monitoring methods that are superior indicators of circulatory compromise would be clinically valuable. With IRB approval, 21 healthy volunteers were subjected to progressive lower body negative pressure (LBNP) until the onset of presyncopal symptoms. ⋯ In terms of discriminating between (a) the 11 subjects who tolerated the protocol (i.e., tolerated higher levels of LBNP); versus (b) the 10 non-tolerant subjects, there was also a significant difference between MF and LTI: the ROC AUC for MF was 0.40 and for LTI was 0.66. There were no significant differences between MF nor EBI, however. In conclusion, LTI is notable as the only method which (a) correlated with decompression: (b) distinguished between decompression to -45 mmHg versus recovery; and (c) distinguished between those subjects who adequately compensated for central hypovolemia (tolerant) and those who did not have such robust physiologic compensation (non-tolerant).
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Conf Proc IEEE Eng Med Biol Soc · Jan 2007
Screening device for identification of patients with paroxysmal atrial fibrillation to prevent ischemic strokes.
The most frequently occurring cardiac arrhythmia in the adulthood is atrial fibrillation. In Germany, the number of sick people is estimated at 800,000. Patients who suffer from atrial fibrillation often do not sense any symptoms of the illness. ⋯ These persons must be recognized, because of their increased stroke risk in order to be able to attend it. In this work, the chest strap CorBelt, developed by the company Corscience GmbH&Co. KG, is equipped with an algorithm for the recognition of atrial fibrillation with the aid of heart rate variability.
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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
Adaptive noise canceling of electrocardiogram artifacts in single channel electroencephalogram.
A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.
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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.