Biomedical subjects

M Factor

Publications and source records attributed to M Factor.

6 recordsLinked to original sources

PubMed1992

BIO-SPEAD: a parallel computing environment to accelerate development of biologic signal processing algorithms.

We have created BIO-SPEAD (pronounced speed), a BIOlogical Signal Processing Environment for Algorithm Development. BIO-SPEAD is designed to accelerate development of complex algorithms which integrate information derived from single or multiple physiologic waveforms. BIO-SPEAD currently performs all of the basic analyses of several arterial blood pressure waveforms, and allows the user to utilize the results of those low-level analyses for development of more complex algorithms. We utilized a parallel programming architecture called the Process Trellis which keeps the different tasks, or processes, within BIO-SPEAD independent of each other. Additionally, we have developed a graphics interface to enable the user to visualize the waveform under analysis, the low-level system analysis, and the internal workings of the algorithm under development. The system has been used for several algorithm development projects and has demonstrated its utility.

Algorithms↗

PubMed1992

The multi-trellis software architecture and the intelligent cardiovascular monitor.

A real-time, intelligent cardiovascular monitor is complex. It must process multiple waveforms, recognize artifacts, extract pertinent parameters, recognize a patient's clinical state, analyze the problem and formulate a response. This paper presents the multi-trellis (a collection of process trellises), a software architecture for building such a monitor. A process trellis is a uniform hierarchical framework for heterogeneous program modules. The multi-trellis extension allows one to compile several process trellis programs with widely varying run-time requirements into a single executable program that it is efficient, predictable and usable. Our prototype consists of two process trellises. The lower trellis contains processes to analyze three different analog signals: the blood pressure from a non-invasive monitor and an arterial catheter, and the ECG. The upper trellis contains processes to help detect evolving hemodynamic trends, identify abnormalities, and present a succinct summary to the clinician. Our prototype shows that the multi-trellis is a demonstrably useful software architecture for building these real-time, intelligent monitors.

Artificial Intelligence↗

PubMed1991

A parallel computing interface to facilitate development of physiologic signal processing algorithms.

We have created a system to aid in the development of algorithms related to the blood pressure waveform. The system performs all of the basic analyses of the waveform, and allows the user to utilize the results of those analyses for the algorithm under development. We have used a parallel programming architecture which keeps the different tasks, or processes, within the system independent of each other. Additionally, we have developed a graphics interface to enable the user to visualize the waveform, the system analysis, and the internal workings of the algorithm under development.

Algorithms↗

PubMed1990

A parallel software architecture for building intelligent medical monitors.

Intensive care units become more complicated each day as the number of devices developed to monitor various aspects of a patient's status continues to increase. Intelligent monitors attempt to reduce this complexity by interpreting the data and presenting a high level summary to the clinician. We propose an innovative parallel software architecture for constructing intelligent medical monitors: the process trellis. The process trellis is an explicitly parallel structure, and therefore can take advantage of the performance gains available from parallel computing hardware. It does not, however, presuppose any expertise in parallel programming on the part of the application programmer. A prototype cardiovascular monitor has been built using this parallel software architecture. Preliminary testing of the monitor has shown that real-time cardiovascular monitoring, including data calculations, symbolic classification, and interpretation can be accomplished in real-time.

Expert Systems↗

PubMed1990

Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm.

Using a parallel implementation of the multi-state Kalman filtering algorithm, we have developed an accurate method of reliably detecting and identifying trends, abrupt changes, and artifacts from multiple physiologic data streams in real-time. The Kalman filter algorithm was implemented within an innovative software architecture for parallel computation: a parallel process trellis. Examples, processed in real-time, of both simulated and actual data serve to illustrate the potential value of the Kalman filter as a tool in physiologic monitoring.

Algorithms↗

PubMed1990

DYNASCENE: an approach to computer-based intelligent cardiovascular monitoring using sequential clinical "scenes".

Hemodynamic abnormalities such as hypovolemia typically progress through a sequence of discrete clinical phases or "scenes" (e.g., intravascular volume depletion, vasoconstriction, hypotension). Each scene can be defined by a cluster of hemodynamic trends. A natural approach to modeling the process of hemodynamic monitoring involves identifying these scenes and the temporal relationships among them. This approach has been utilized in the development of DYNASCENE, a parallel programming implementation of a computer-based intelligent hemodynamic monitor. This paper discusses: (1) The rationale for utilizing sequential clinical scenes to represent knowledge of hemodynamic behavior, (2) the design of the DYNASCENE system, and (3) preliminary tests of the DYNASCENE system.

Computer Systems↗