Publications (170) View all
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Article: Multi-parameter Respiratory Rate Estimation fromthe Photoplethysmogram.
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ABSTRACT: We present a novel method for estimating respiratory rate in real-time from the photoplethysmogram (PPG) obtained from pulse oximetry. Three respiratory induced variations (frequency, intensity, and amplitude) are extracted from the PPG using the Incremental-Merge Segmentation algorithm. Frequency content of each respiratory induced variation is analyzed using Fast Fourier Transforms. The proposed Smart Fusion method then combines the results of the three respiratory induced variations using a transparent mean calculation. It automatically eliminates estimations considered to be unreliable because of detected presence of artifacts in the PPG or disagreement between the different individual respiratory rate estimations. The algorithm has been tested on data obtained from 29 children and 13 adults. Results show that it is important to combine the three respiratory induced variations for robust estimation of respiratory rate. The Smart Fusion showed trends of improved estimation (mean root mean square error 3.0 breaths/min) compared to the individual estimation methods (5.8, 6.2 and 3.9 breaths/min). The Smart Fusion algorithm is being implemented in a mobile phone pulse oximeter device to facilitate the diagnosis of severe childhood pneumonia in remote areas.IEEE transactions on bio-medical engineering 02/2013; · 2.15 Impact Factor -
Article: System Identification and Closed-Loop Control of End-Tidal CO2 in Mechanically Ventilated Patients.
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ABSTRACT: This paper presents a systematic approach to system identification and closed-loop control of end-tidal carbon dioxide partial pressure (PETCO2) in mechanically ventilated patients. An empirical model consisting of a linear dynamic system followed by an affine transform is proposed to derive a low-order and high-fidelity representation that can reproduce the positive and inversely proportional dynamic input-output relationship between PETCO2 and minute ventilation (MV) in mechanically ventilated patients. The predictive capability of the empirical model was evaluated using experimental respiratory data collected from eighteen mechanically ventilated human subjects. The model predicted PETCO2 response accurately with a root-mean-squared error (RMSE) of 0.22+/-0.16mmHg and a coefficient of determination (r2) of 0.81+/-0.18 (mean+/-SD) when a second-order rational transfer function was used as its linear dynamic component. Using the proposed model, a closedloop control method for PETCO2 based on a proportionalintegral (PI) compensator was proposed by systematic analysis of the system root locus. For the eighteen mechanically ventilated patient models identified, the PI compensator exhibited acceptable closed-loop response with a settling time of 1.27+/- 0.20min and a negligible overshoot (0.51+/-1.17%), in addition to zero steady-state PETCO2 set point tracking. The physiologic implication of the proposed empirical model was analyzed by comparing it with the traditional multi-compartmental model widely used in pharmacological modeling.IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society 06/2012; · 1.69 Impact Factor -
Article: How often do anesthesiologists really check their monitors?
Canadian Journal of Anaesthesia 05/2012; 54:44522-44522. · 2.35 Impact Factor -
Article: A monitor-decoupled pharmacodynamic model of propofol in children using state entropy as clinical endpoint.
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ABSTRACT: This paper presents a new monitor-decoupled model of propofol pharmacodynamics (PD) using the state entropy (SE) as the clinical endpoint of interest. In our model, the dynamics of the entropy monitor are separated from the PD response of the patient by explicitly accounting for the model of the entropy monitor in the PD identification process. The monitor model was then excluded from the identified PD model for the patient. The PD model, thus, obtained is distinct from its traditional counterpart in that it reflects the PD response of a patient with the dynamic effects of the monitor included as a specific entity. System identification trials using SE data of 31 pediatric subjects show that the PD models derived from the proposed approach are an improvement on the traditional approach. For the Paedfusor pharmacokinetic (PK) model, population-averaged effect site equilibration rate constant k(e0) was 5.4 and 3.0 for the proposed and traditional PD models ( p < 0.001), respectively. For the Kataria PK model, population-averaged k(e0) was 2.3 and 1.4 (p < 0.01). This significant difference suggests that the effects of the monitor must be considered when searching for the intrinsic PD of a patient that is free from the bias induced by the monitor characteristics.IEEE transactions on bio-medical engineering 12/2011; 59(3):736-43. · 2.15 Impact Factor -
Article: A direct dynamic dose-response model of propofol for individualized anesthesia care.
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ABSTRACT: In an effort to open up new opportunities in individualized anesthesia care, this paper presents a dynamic dose-response model of propofol that relates propofol dose (i.e., infusion rate) directly to a clinical effect. The proposed model consists of a first-order equilibration dynamics plus a nonlinear Hill equation model, each representing the transient distribution of propofol dose from the plasma to the effect site and the steady-state dose-effect relationship. Compared to traditional pharmacokinetic-pharmacodynamic (PKPD) models, the proposed model has structural parsimony and comparable predictive capability, making it more attractive than its PKPD counterpart for identifying an individualized dose-response model in real-time. The efficacy of the direct dynamic dose-response model over a traditional PKPD model was assessed using a mixed effects modeling analysis of the electroencephalogram (EEG)-based state entropty (SE) response to intravenous propofol administration in 34 pediatric subjects. An improvement in the mean-squared error and r(2) value of individual prediction, as well as the Akaike's information criterion (AIC) was seen with the direct dynamic dose-response model.IEEE transactions on bio-medical engineering 11/2011; 59(2):571-8. · 2.15 Impact Factor
