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 2013
An EEG diagnosis system for quasi brain death based on complexity and energy analyses.
Electroencephalogram (EEG) is often used in confirmatory test for brain death determination in clinical practice. Because the EEG measuring and monitoring is relatively safe and reliable for deep comatose patients, it is believed to be valuable for reducing the risk of diagnosis or prevent mistaken diagnosis of brain death. In this paper, we present EEG complexity analysis and EEG energy analyses for the EEG acquisition of 35 adult patients. ⋯ In EEG energy analysis, we firstly accumulate the EEG energy from the extracted components that are related to the brain activities. Then, we evaluate the energy differences between deep comatose patients and brain death. The empirical results reported in this paper suggest some promising directions and valuable clues for clinical practice.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Ambulatory respiratory rate detection using ECG and a triaxial accelerometer.
Continuous monitoring of respiratory rate in ambulatory conditions has widespread applications for screening of respiratory diseases and remote patient monitoring. Unfortunately, minimally obtrusive techniques often suffer from low accuracy. In this paper, we describe an algorithm with low computational complexity for combining multiple respiratory measurements to estimate breathing rate from an unobtrusive chest patch sensor. ⋯ The three respiration rates are combined by a weighted average using weights based on quality metrics for each signal. The algorithm was evaluated on 15 elderly subjects who performed spontaneous and metronome breathing as well as a variety of activities of daily living (ADLs). When compared to a reference device, the mean absolute error was 1.02 breaths per minute (BrPM) during metronome breathing, 1.67 BrPM during spontaneous breathing, and 2.03 BrPM during ADLs.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Classification of EEG bursts in deep sevoflurane, desflurane and isoflurane anesthesia using AR-modeling and entropy measures.
A study relating signal patterns of burst onsets in burst suppression EEG to the anesthetic agent or anesthesia induction protocol is presented. A dataset of 82 recordings of sevoflurane, isoflurane and desflurane anesthesia underlies the study. 3 second segments from the onset of altogether 3214 bursts are described using AR model parameters, spectral entropy and sample entropy as features. ⋯ The results indicate that no clear cut distinction can be made between the burst patterns induced by the mentioned anesthetics although bursts of certain properties are more common in certain patient groups. Several directions for further investigations are proposed based on visual inspection of the recordings.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Effect of respiratory-induced intensity variations on finger SpO2 measurements in volunteers.
Photoplethysmographic (PPG) signals were recorded from the fingers of 16 healthy volunteers with periods of timed and forced respiration. The aim of this pilot study was to compare estimations of arterial oxygen saturation (SpO2) recorded using a dedicated pulse oximetry system while subjects were breathing regularly with and without a mouthpiece containing a flow resistor. ⋯ SpO2 values were calculated from the pre-recorded PPG signals. Mean SpO2 values were 95.4% with the flow resistor compared with 97.3% with no artificial resistance, with statistical significance demonstrated using a Student's t-test (P = 0.006).
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Automated detection of sleep apnea in infants using minimally invasive sensors.
To address the difficult and necessity of early detection of sleep apnea hypopnea syndrome in infants, we present a study into the effectiveness of pulse oximetry as a minimally invasive means of automated diagnosis of sleep apnea in infants. Overnight polysomnogram data from 328 infants were used to extract time-domain based oximetry features and scored arousal data for each subject. These records were then used to determine apnea events and to train a classifier model based on linear discriminants. Performance of the classifier was evaluated using a leave-one-out cross-validation scheme and an accuracy of 68% was achieved, with a specificity of 68.6% and a sensitivity of 55.9%.