Validation of a new algorithm for the BPM-100 electronic oscillometric office blood pressure monitor.

BACKGROUND: To test the accuracy of a new algorithm for the BPM-100, an automated oscillometric blood pressure (BP) monitor, using stored data from an independently conducted validation trial comparing the BPM-100(Beta) with a mercury sphygmomanometer. DESIGN: Raw pulse wave and cuff pressure data were stored electronically using embedded software in the BPM-100(Beta), during the validation trial. The 391 sets of measurements were separated objectively into two subsets. A subset of 136 measurements was used to develop a new algorithm to enhance the accuracy of the device when reading higher systolic pressures. The larger subset of 255 measurements (three readings for 85 subjects) was used as test data to validate the accuracy of the new algorithm. METHODS: Differences between the new algorithm BPM-100 and the reference (mean of two observers) were determined and expressed as the mean difference +/- SD, plus the percentage of measurements within 5, 10, and 15 mmHg. RESULTS: The mean difference between the BPM-100 and reference systolic BP was -0.16 +/- 5.13 mmHg, with 73.7% < or = 5 mmHg, 94.9% < or = 10 mmHg and 98.8% < or = 15 mmHg. The mean difference between the BPM-100 and reference diastolic BP was -1.41 +/- 4.67 mmHg, with 78.4% < or = 5 mmHg, 92.5% < or = 10 mmHg, and 99.2% < or = 15 mmHg. These data improve upon that of the BPM-100(Beta) and pass the AAMI standard, and 'A' grade BHS protocol. CONCLUSION: This study illustrates a new method for developing and testing a change in an algorithm for an oscillometric BP monitor utilizing collected and stored electronic data and demonstrates that the new algorithm meets the AAMI standard and BHS protocol.

Restoration of use of paralyzed limb muscles using sensory nerve signals for state control of FES-assisted walking.

A real-time functional electrical stimulation (FES) state controller was designed that utilized sensory nerve cuff signals from the cat forelimb to control the timing of stimulation of the Palmaris Longus (PalL) muscle during walking on the treadmill. Sensory nerve signals from the median and superficial radial nerves provided accurate, reliable feedback related to foot contact and lift-off which, when analyzed with single threshold Schmitt triggers, produced valuable state information about the step cycle. The study involved three experiments: prediction of the timing of muscle activity in an open-loop configuration with no stimulation, prediction of the timing of muscle activity in a closed-loop configuration that included stimulation of the muscle over natural PaIL electromyogram (EMG), and temporary paralysis of selected forelimb muscles coupled with the use of the state controller to stimulate the PalL in order to return partial support function to the anesthetized limb. The FES state controller was tested in a variety of walking conditions, including different treadmill speeds and slopes. The results obtained in these experiments demonstrate that nerve cuff signals can provide a useful source of feedback to FES systems for control of limb function.

Gait phase information provided by sensory nerve activity during walking: applicability as state controller feedback for FES.

In this study, we extracted gait-phase information from natural sensory nerve signals of primarily cutaneous origin recorded in the forelimbs of cats during walking on a motorized treadmill. Nerve signals were recorded in seven cats using nerve cuff or patch electrodes chronically implanted on the median, ulnar, and/or radial nerves. Features in the electroneurograms that were related to paw contact and lift-off were extracted by threshold detection. For four cats, a state controller model used information from two nerves (either median and radial, or ulnar and radial) to predict the timing of palmaris longus activity during walking. When fixed thresholds were used across a variety of walking conditions, the model predicted the timing of EMG activity with a high degree of accuracy (average error = 7.8%, standard deviation = 3.0%, n = 14). When thresholds were optimized for each condition, predictions were further improved (average error = 5.5%, standard deviation = 2.3%, n = 14). The overall accuracy with which EMG timing information could be predicted using signals from two cutaneous nerves for two constant walking speeds and three treadmill inclinations for four cats suggests that natural sensory signals may be implemented as a reliable source of feedback for closed-loop control of functional electrical stimulation (FES).