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 2015
Smart photoplethysmographic sensor for pulse wave registration at different vascular depths.
The aim of this paper is to propose a smart optical sensor for cardiovascular activity monitoring at different tissue layers. Photoplethysmography (PPG) is a noninvasive optical technique for monitoring mainly blood volume changes in the examined tissue. However, different important physiological parameters, such as oxygen saturation, heart and breathing rate, dynamics of skin micro-circulation, vasomotion activity etc., can be extracted from the registered PPG signal. ⋯ Compared to the existing sensors, the system enables to select the optimal LED (light emitting diode) and photo detector couple in order to obtain the pulse wave signal from the interested blood vessels with the highest possible signal to noise ratio. In this study, the designed PPG sensor was tested for the pulse wave registration from radial artery. The highest efficiency and signal to noise ratio was achieved using infrared LED (940 nm) and photo-diode pair.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
An enhanced cerebral recovery index for coma prognostication following cardiac arrest.
Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. ⋯ We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Comparative StudyComparison of HRV parameters derived from photoplethysmography and electrocardiography signals.
Heart rate variability (HRV) has become a useful tool in analysis of cardiovascular system in both research and clinical fields. HRV has been also used in other applications such as stress level estimation in wearable devices. HRV is normally obtained from ECG as the time interval of two successive R waves. ⋯ Our results show that the smallest error happens in SDNN and SD2 with relative error of 2.46% and 2%, respectively. The most affected parameter is pNN50 with relative error of 29.89%. In addition, in our trial, using the maximum of PPG gave better results than its second derivative.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Peripheral venous blood oxygen saturation can be non-invasively estimated using photoplethysmography.
Measurement of peripheral venous oxygen saturation (SvO2) is currently performed using invasive catheters or direct blood draw. The purpose of this study was to non-invasively determine SvO2 using a variation of pulse oximetry techniques. Artificial respiration-like modulations applied to the peripheral vascular system were used to infer regional SvO2 using photoplethysmography (PPG) sensors. ⋯ The median difference between the two saturations was 3.6%, while the difference between paired measurements in each subject was statistically significant (p=0.002). These results demonstrate the feasibility of this method for real-time, low cost, non-invasive estimation of SvO2. Further validation of this method is warranted.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram.
Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive RR estimation suffer from a lack of robustness, resulting in the fact that they are not used in clinical practice. ⋯ Our proposed methodology resulted in a mean-absolute-error (MAE) of 1.98 breaths per minute (bpm), outperformed other fusing strategies (mean fusion: 2.95 bpm; median fusion: 2.33 bpm; ML: 2.30 bpm). It also outperformed the best single algorithm (2.39 bpm) and the benchmark algorithm proposed for use with Capnobase (2.22 bpm). We conclude that the proposed fusion methodology can be used to combine RR estimates from multiple sources derived from the PPG, to infer a reliable and robust estimation of the respiratory rate in an unsupervised manner.