Mario G Iobbi | PubMed Health

PubMed2007

Oximetry feedback flow control simulation for oxygen therapy.

OBJECTIVES: For many with Chronic Obstructive Pulmonary Disease (COPD), arterial oxygen saturation while receiving Long-Term Oxygen Therapy (LTOT) falls below an acceptable threshold (SpO(2) < 90%) for extended periods during routine daily activities. Using a closed-loop controller, we have evaluated a simulated method to automatically regulate the oxygen flow-rate in response to the measured oxygen demand. METHODS: The closed-loop control scheme was implemented in a computer simulation on Simulink. Feedback from a pulse oximeter was used to maintain a target SpO(2) of 91% by changing the oxygen flow-rate to the patient. The controller was evaluated using a model to approximate the patient's arterial oxygen saturation response, including hypoxic events from artificial disturbances as well as recorded patient oximetry data. RESULTS: The simulated controller produced improvement in arterial oxygen saturation throughout a wide range of disturbance frequencies. It suppressed disturbances with periods greater than a couple of minutes by more than -10 dB. When evaluated with patient oximetry recordings, the controller on average reduced the time spent with arterial blood saturation below threshold by 76%. Given the same volume of oxygen, the closed-loop controller also produced a 63% improvement compared to fixed flow-rate LTOT. CONCLUSIONS: The simulation findings indicate an optimized matching between oxygen supply and demand, maintaining SpO(2) above threshold to improve therapeutic efficacy compared to standard LTOT.

Algorithms↗

PubMed2003

Modeling reaching impairment after stroke using a population vector model of movement control that incorporates neural firing-rate variability.

The directional control of reaching after stroke was simulated by including cell death and firing-rate noise in a population vector model of movement control. In this model, cortical activity was assumed to cause the hand to move in the direction of a population vector, defined by a summation of responses from neurons with cosine directional tuning. Two types of directional error were analyzed: the between-target variability, defined as the standard deviation of the directional error across a wide range of target directions, and the within-target variability, defined as the standard deviation of the directional error for many reaches to a single target. Both between- and within-target variability increased with increasing cell death. The increase in between-target variability arose because cell death caused a nonuniform distribution of preferred directions. The increase in within-target variability arose because the magnitude of the population vector decreased more quickly than its standard deviation for increasing cell death, provided appropriate levels of firing-rate noise were present. Comparisons to reaching data from 29 stroke subjects revealed similar increases in between- and within-target variability as clinical impairment severity increased. Relationships between simulated cell death and impairment severity were derived using the between- and within-target variability results. For both relationships, impairment severity increased similarly with decreasing percentage of surviving cells, consistent with results from previous imaging studies. These results demonstrate that a population vector model of movement control that incorporates cosine tuning, linear summation of unitary responses, firing-rate noise, and random cell death can account for some features of impaired arm movement after stroke.

Action Potentials↗