PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 537228

[Automatic classification of phonocardiogram--processed by linear predictive analysis (author's transl)].

A Iwata·N Ogawa·N Ishii·N Suzumura·K Ikegaya·M Yokoi·Z Uozumi
View original source ↗Export metadata ↓

Abstract

The source did not provide an abstract. Follow the original record for more information.

Explore related subjects

ComputersElectrocardiographyHeart Septal DefectsHeart Valve DiseasesHumansMathematicsPhonocardiographyEnglish AbstractJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

A Iwata, N Ogawa, N Ishii, N Suzumura, K Ikegaya, M Yokoi, Z Uozumi. 1979-06-30. [Automatic classification of phonocardiogram--processed by linear predictive analysis (author's transl)].. https://pubmed.ncbi.nlm.nih.gov/537228/

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

KEEP EXPLORING

Related citations

PubMed2007

Evaluation of sites for the location of WEEE recycling plants in Spain.

As a consequence of new European legal regulations for treatment of waste electrical and electronic equipment (WEEE), recycling plants have to be installed in Spain. In this context, this contribution describes a method for ranking of Spanish municipalities according to their appropriateness for the installation of these plants. In order to rank the alternatives, the discrete multi-criteria decision method PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations), combined with a surveys of experts, is applied. As existing plants are located in North and East Spain, a significant concentration of top ranking municipalities can be observed in South and Central Spain. The method does not present an optimal structure of the future recycling system, but provides a selection of good alternatives for potential locations of recycling plants.

Computers↗
PubMed2007

Overview of informatics for high content screening.

With the growing use of high content screening (HCS) and analysis in drug discovery and systems biology, informatics has come to the forefront as a critical technology to effectively utilize the massive volumes of high content data and images being generated. Informatics technologies are required to transform HCS data and images into useful information and then into knowledge to drive decision making in an efficient and cost effective manner. In this chapter, we provide an overview of informatics tools and technologies for HCS, discuss some of the challenges of harnessing the huge and growing volumes of HCS data, and provide insight to help toward implementing or selecting, and utilizing a high content informatics solution to meet your organization's needs.

Computers↗