What does this research mean for the field?

Both filter and wrapper feature selection techniques can successfully identify reduced subsets of electrocardiographic recording sites capable of accurately detecting myocardial infarction from body surface potential maps, achieving up to 82.8% classification accuracy with 24 sites. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

September 2, 2005Open Access

Mining for diagnostic information in body surface potential maps: A comparison of feature selection techniques

DFDewar FinlayUniversity of Ulster CNChris NugentRutgers, The State University of New Jersey PMPaul McCullaghRoyal Devon & Exeter NHS Foundation Trust

Structured PICO

Can feature selection techniques identify optimal subsets of electrocardiographic recording sites from body surface potential maps to accurately detect myocardial infarction?

Population

116 subjects with recordings used to assess the presence or absence of Myocardial Infarction (MI)

Intervention

Feature selection techniques (Single Variable Classifier based filter and Sequential Forward Selection based wrapper approaches) to select subsets of 3, 6, 9, 12, 24 and 32 electrocardiographic recording sites from 192 lead body surface potential maps (BSPMs)

Comparator

Comparison between filter and wrapper approaches

Outcome

Classification accuracy for the presence or absence of Myocardial Infarction (MI)surrogate

Feature selection techniques can identify reduced subsets of electrocardiographic leads from body surface potential maps that maintain high diagnostic accuracy for myocardial infarction.

Abstract

BACKGROUND: In body surface potential mapping, increased spatial sampling is used to allow more accurate detection of a cardiac abnormality. Although diagnostically superior to more conventional electrocardiographic techniques, the perceived complexity of the Body Surface Potential Map (BSPM) acquisition process has prohibited its acceptance in clinical practice. For this reason there is an interest in striking a compromise between the minimum number of electrocardiographic recording sites required to sample the maximum electrocardiographic information. METHODS: In the current study, several techniques widely used in the domains of data mining and knowledge discovery have been employed to mine for diagnostic information in 192 lead BSPMs. In particular, the Single Variable Classifier (SVC) based filter and Sequential Forward Selection (SFS) based wrapper approaches to feature selection have been implemented and evaluated. Using a set of recordings from 116 subjects, the diagnostic ability of subsets of 3, 6, 9, 12, 24 and 32 electrocardiographic recording sites have been evaluated based on their ability to correctly asses the presence or absence of Myocardial Infarction (MI). RESULTS: It was observed that the wrapper approach, using sequential forward selection and a 5 nearest neighbour classifier, was capable of choosing a set of 24 recording sites that could correctly classify 82.8% of BSPMs. Although the filter method performed slightly less favourably, the performance was comparable with a classification accuracy of 79.3%. In addition, experiments were conducted to show how (a) features chosen using the wrapper approach were specific to the classifier used in the selection model, and (b) lead subsets chosen were not necessarily unique. CONCLUSION: It was concluded that both the filter and wrapper approaches adopted were suitable for guiding the choice of recording sites useful for determining the presence of MI. It should be noted however that in this study recording sites have been suggested on their ability to detect disease and such sites may not be optimal for estimating body surface potential distributions.

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