Detection of body position changes from the ECG using a Laplacian noise model

Ana Minchole, Leif Sörnmo, Pablo Laguna

Research output: Contribution to journalArticlepeer-review

10 Citations (SciVal)

Abstract

Body position changes (BPCs) are manifested as shifts in the electrical axis of the heart, which may lead to ST changes in the ECG, misclassified as ischemic events. This paper presents a novel BPC detector based on a Laplacian noise model. It is assumed that a BPC can be modelled as a step-like change in the two coefficient series that result from the Karhunen-Loeve transform of the QRS complex and the ST-T segment. The generalized likelihood ratio test is explored for detection, where the statistical parameters of the Laplacian model are subject to estimation. Two databases are studied: one for assessing detection performance in healthy subjects who perform BPCs, and another for assessing the false alarm rate in ECGs recorded during percutaneous transluminal coronary angiography. The resulting probability of detection (P-D) and probability of false alarm (P-F) are 0.94 and 0.00, respectively, whereas the false alarm rate in ischemic recordings is 1 event/h. The proposed detector outperforms an existing detector based on the Gaussian noise model which achieved a P-D/P-F of 0.90/0.01 and a false alarm rate of 2 events/h. Analysis of the log-likelihood function for the Gaussian and Laplacian noise models show that latter model is more adequate. (C) 2014 Published by Elsevier Ltd.
Original languageEnglish
Pages (from-to)189-196
JournalBiomedical Signal Processing and Control
Volume14
DOIs
Publication statusPublished - 2014

Subject classification (UKÄ)

  • Medical Engineering

Keywords

  • Postural changes
  • Laplacian noise
  • Detection theory
  • Ischemia detection

Fingerprint

Dive into the research topics of 'Detection of body position changes from the ECG using a Laplacian noise model'. Together they form a unique fingerprint.

Cite this