Segment clustering methodology for unsupervised Holter recordings analysis

Jose Luis Rodríguez-Sotelo; Diego Peluffo-Ordoñez; German Castellanos Dominguez

doi:10.1117/12.2073882

28 January 2015 Segment clustering methodology for unsupervised Holter recordings analysis

Jose Luis Rodríguez-Sotelo, Diego Peluffo-Ordoñez, German Castellanos Dominguez

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 92870M (2015) https://doi.org/10.1117/12.2073882
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

Abstract

Cardiac arrhythmia analysis on Holter recordings is an important issue in clinical settings, however such issue implicitly involves attending other problems related to the large amount of unlabelled data which means a high computational cost. In this work an unsupervised methodology based in a segment framework is presented, which consists of dividing the raw data into a balanced number of segments in order to identify fiducial points, characterize and cluster the heartbeats in each segment separately. The resulting clusters are merged or split according to an assumed criterion of homogeneity. This framework compensates the high computational cost employed in Holter analysis, being possible its implementation for further real time applications. The performance of the method is measure over the records from the MIT/BIH arrhythmia database and achieves high values of sensibility and specificity, taking advantage of database labels, for a broad kind of heartbeats types recommended by the AAMI.

Citation Download Citation

Jose Luis Rodríguez-Sotelo, Diego Peluffo-Ordoñez, and German Castellanos Dominguez "Segment clustering methodology for unsupervised Holter recordings analysis", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 92870M (28 January 2015); https://doi.org/10.1117/12.2073882

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Electrocardiography

Databases

Heart

Principal component analysis

Error analysis

Expectation maximization algorithms

Feature selection

Show All Keywords

Keywords/Phrases

Search In:

Publication Years