Journal Article

ECG classification using combination of linear and non-linear features with neural network

Abstract

In this paper, we present an approach to improve the accuracy and reliability of ECG classification. The proposed method combines features analysis of linear and non-linear ECG dynamics. Non-linear features are represented by complexity measures of assessment of ordinal network non-stationarity. We describe the basic concept of ECG partitioning and provide an experiment on PQRST complex data. The results demonstrate that the proposed technique effectively detects abnormalities via automatic feature extraction and improves the state-of-the-art detection performance on one of the standard collections of heartbeat signals, the ECG5000 dataset.

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Authors

Biloborodova, Tetiana
Skarga-Bandurova, Inna
Skarha-Bandurov, Illia
Yevsieieva, Yelyzaveta
Biloborodov, Oleh

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2022
Date of RADAR deposit: 2022-07-07

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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