Literature DB >> 15253115

Protocol to assess robustness of ST analysers: a case study.

Franc Jager1, George B Moody, Roger G Mark.   

Abstract

This paper proposes principles and methods for assessing the robustness of ST segment analysers and algorithms. We describe an evaluation protocol, procedures and performance measures suitable for assessing the robustness. An ST analyser is robust if its performance is not critically dependent on the variation of the noise content of input signals and on the choice of the database used for testing, and if its analysis parameters are not critically tuned to the database used for testing. The protocol to assess the robustness includes: (1) a noise stress test addressing the aspect of variation of input signals; (2) a bootstrap evaluation of algorithm performance addressing the aspect of distribution of input signals and (3) a sensitivity analysis addressing the aspect of variation of analyser's architecture parameters. An ST analyser is considered to be robust if the performance measurements obtained during these procedures remain above the predefined critical performance boundaries. We illustrate the use of the robustness protocol and robustness measures by a case study in which we assessed the robustness of our Karhunen-Loève transform based ischaemic ST episode detection and quantification algorithm using the European Society of Cardiology ST-T database.

Mesh:

Year:  2004        PMID: 15253115     DOI: 10.1088/0967-3334/25/3/004

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  2 in total

1.  Correlation between the high-frequency content of the QRS on murine surface electrocardiogram and the sympathetic nerves density in left ventricle after myocardial infarction: Experimental study.

Authors:  Golriz Sedaghat; Ryan T Gardner; Muammar M Kabir; Elyar Ghafoori; Beth A Habecker; Larisa G Tereshchenko
Journal:  J Electrocardiol       Date:  2017-02-03       Impact factor: 1.438

2.  Robust parameter extraction for decision support using multimodal intensive care data.

Authors:  G D Clifford; W J Long; G B Moody; P Szolovits
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2009-01-28       Impact factor: 4.226

  2 in total

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