Call for Papers
Focus Theme: “Machine Learning and Data Analytics in Pervasive Health”
Guest editors: Nuria Oliver, Oscar Mayora, Michael Marschollek
Deadline: July 28, 2017
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Archive (2016–2006)

Fetal Heart Rate Deceleration Detection Using a Discrete Cosine Transform Implementation of Singular Spectrum Analysis

Journal: Methods of Information in Medicine
Subtitle: A journal stressing, for more than 50 years, the methodology and scientific fundamentals of organizing, representing and analyzing data, information and knowledge in biomedicine and health care
ISSN: 0026-1270
Issue: 2007 (Vol. 46): Issue 2 2007
Pages: 196-201

Fetal Heart Rate Deceleration Detection Using a Discrete Cosine Transform Implementation of Singular Spectrum Analysis

P. A. Warrick 1, 4, D. Precup 2, E. F. Hamilton 3, 4, R. E. Kearney1

1Biomedical Engineering Department, McGill University, Montréal, Québec, Canada 2School of Computer Science, McGill University, Montréal, Québec, Canada 3Department of Obstetrics and Gynecology, McGill University, Montréal, Québec, Canada 4LMS Me


Fetal heart rate, Signal Processing, Obstetrics, computer-assisted models, statistical decision support techniques


Objectives: To develop a singular-spectrum analysis (SSA) based change-point detection algorithm applicable to fetal heart rate (FHR) monitoring to improve the detection of deceleration events. Methods: We present a method for decomposing a signal into near-orthogonal components via the discrete cosine transform (DCT) and apply this in a novel online manner to change-point detection based on SSA. The SSA technique forms models of the underlying signal that can be compared over time; models that are sufficiently different indicate signal change points. To adapt the algorithm to deceleration detection where many successive similar change events can occur, we modify the standard SSA algorithm to hold the reference model constant under such conditions, an approach that we term “base-hold SSA”. The algorithm is applied to a database of 15 FHR tracings that have been preprocessed to locate candidate decelerations and is compared to the markings of an expert obstetrician. Results: Of the 528 true and 1285 false decelerations presented to the algorithm, the base-hold approach improved on standard SSA, reducing the number of missed decelerations from 64 to 49 (21.9%) while maintaining the same reduction in false-positives (278). Conclusions: The standard SSA assumption that changes are infrequent does not apply to FHR analysis where decelerations can occur successively and in close proximity; our base-hold SSA modification improves detection of these types of event series.

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