PubMed Health⌕ Search

PubMed · 8060020

A fuzzy pattern recognition method to classify esophageal motility records.

Abstract

Analysis and classification of esophageal motility records were investigated using signal processing and fuzzy-set pattern recognition techniques. A set of parameters has been extracted from the raw records and has previously been used as characterizing features. Improvements to these procedures were obtained by extracting these features from processed data, and some additional parameters were developed. The new set of features was used in the design of a fuzzy classifier, and classification accuracy was estimated using the leave-one-out technique. To our knowledge, this is the first report of the application of automatic classification to esophageal motility records.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F E Abou-Chadi, F A Ezzat, A A Sif el-Din. A fuzzy pattern recognition method to classify esophageal motility records.. https://doi.org/10.1007/bf02368227

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

KEEP EXPLORING

Related citations

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗