PubMed HealthSearch

PubMed · 6734156

A computer system for analysis and transmission of spirometry waveforms using volume sampling.

Abstract

A microprocessor-controlled data gathering system for telemetry and analysis of spirometry waveforms was implemented using a completely digital design. Spirometry waveforms were obtained from an optical shaft encoder attached to a rolling seal spirometer. Time intervals between 10-ml volume changes (volume sampling) were stored. The digital design eliminated problems of analog signal sampling. The system measured flows up to 12 liters/sec with 5% accuracy and volumes up to 10 liters with 1% accuracy. Transmission of 10 waveforms took about 3 min. Error detection assured that no data were lost or distorted during transmission. A pulmonary physician at the central hospital reviewed the volume-time and flow-volume waveforms and interpretations generated by the central computer before forwarding the results and consulting with the rural physician. This system is suitable for use in a major hospital, rural hospital, or small clinic because of the system's simplicity and small size.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D V Ostler, R M Gardner, R O Crapo. 1984. A computer system for analysis and transmission of spirometry waveforms using volume sampling.. https://doi.org/10.1016/s0010-4809(84)80014-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Seeing motion behind occluders.

The visual system has no difficulty maintaining the identity of an object as it disappears and reappears behind stationary occluders. In the natural world, a moving object may differ from occluders by many characteristics (colour, depth, shape and so on). Scene segmentation based on these characteristics is thought to happen early in visual processing, and to influence how objects, including moving objects, are identified. What happens if the only characteristic distinguishing an object is its direction of motion? Experiments with random dot displays show that one dot moving in a constant trajectory is readily detected among identical dots in brownian motion. Detection declines sharply if the trajectory is intermittently broken, but improves if occluders obscure the breaks in the trajectory. It is not sufficient that these occluders be perceived as segmented from the rest of the display (such as by colour or depth). Rather, it is critical that the occluders do not contain motion that is similar in direction to that of the target trajectory. We conclude that detection of the trajectory is due to the integration of information within a network of low-level motion detectors and is not dependent on segmentation processes.

Computers

Commercial applications of speech interface technology: an industry at the threshold.

Speech interface technology, which includes automatic speech recognition, synthetic speech, and natural language processing, is beginning to have a significant impact on business and personal computer use. Today, powerful and inexpensive microprocessors and improved algorithms are driving commercial applications in computer command, consumer, data entry, speech-to-text, telephone, and voice verification. Robust speaker-independent recognition systems for command and navigation in personal computers are now available; telephone-based transaction and database inquiry systems using both speech synthesis and recognition are coming into use. Large-vocabulary speech interface systems for document creation and read-aloud proofing are expanding beyond niche markets. Today's applications represent a small preview of a rich future for speech interface technology that will eventually replace keyboards with microphones and loud-speakers to give easy accessibility to increasingly intelligent machines.

Computers

Speech technology in the year 2001.

This paper introduces the session "Technology in the Year 2001" and is the first of four papers dealing with the future of human-machine communication by voice. In looking to the future it is important to recognize both the difficulties of technological forecasting and the frailties of the technology as it exists today--frailties that are manifestations of our limited scientific understanding of human cognition. The technology to realize truly advanced applications does not yet exist and cannot be supported by our presently incomplete science of speech. To achieve this long-term goal, the authors advocate a fundamental research program using a cybernetic approach substantially different from more conventional synthetic approaches. In a cybernetic approach, feedback control systems will allow a machine to adapt to a linguistically rich environment using reinforcement learning.

Computers