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A time local subset feature selection for prediction of sudden cardiac death from ECG signal

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Abstract

Prediction of sudden cardiac death continues to gain universal attention as a promising approach to saving millions of lives threatened by sudden cardiac death (SCD). This study attempts to promote the literature from mere feature extraction analysis to developing strategies for manipulating the extracted features to target improvement of classification accuracy. To this end, a novel approach to local feature subset selection is applied using meticulous methodologies developed in previous studies of this team for extracting features from non-linear, time-frequency, and classical processes. We are therefore enabled to select features that differ from one another in each 1-min interval before the incident. Using the proposed algorithm, SCD can be predicted 12 min before the onset; thus, more propitious results are achieved. Additionally, through defining a utility function and employing statistical analysis, the alarm threshold has effectively been determined as 83%. Having selected the best combination of features, the two classes are classified using the multilayer perceptron (MLP) classifier. The most effective features would subsequently be discussed considering their prevalence in the rank-based selection. The results indicate the significant capacity of the proposed method for predicting SCD as well as selecting the appropriate processing method at any time before the incident.

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Acknowledgements

We would like to show our gratitude to Ms. Farahnaz Fayaz (School of Electrical Engineering, Payam Noor University, Tehran, Iran) for sharing her pearls of wisdom with us during the course of this research and writing it. We are also immensely grateful to Dr. Morteza Zanganeh Soroush and Dr. Mohammad Hassan Zokaei Ashtiani for their comments on the earlier version of the manuscript, although any errors are of our own and should not tarnish the reputation of these esteemed persons.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, College of Engineering, University of Tehran, N Kargar St., Tehran, Iran

    Elias Ebrahimzadeh, Mohammad Sajad Manuchehri, Babak Nadjar Araabi & Hamid Soltanian-Zadeh

  2. Department of Biomedical Engineering, Faculty of Engineering, Shahed University, Tehran, Iran

    Sana Amoozegar

  3. Image Analysis Laboratory, Department of Radiology, Henry Ford Hospital, Detroit, MI, USA

    Hamid Soltanian-Zadeh

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  1. Elias Ebrahimzadeh
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Correspondence to Elias Ebrahimzadeh.

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Ebrahimzadeh, E., Manuchehri, M.S., Amoozegar, S. et al. A time local subset feature selection for prediction of sudden cardiac death from ECG signal. Med Biol Eng Comput 56, 1253–1270 (2018). https://doi.org/10.1007/s11517-017-1764-1

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