Automatic Classification of Anomalous ECG Heartbeats from Samples Acquired by Compressed Sensing.

Autor: Picariello E; Department of Engineering, University of Sannio, 82100 Benevento, Italy., Picariello F; Department of Engineering, University of Sannio, 82100 Benevento, Italy., Tudosa I; Department of Engineering, University of Sannio, 82100 Benevento, Italy., Rajan S; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada., De Vito L; Department of Engineering, University of Sannio, 82100 Benevento, Italy.
Jazyk: angličtina
Zdroj: Bioengineering (Basel, Switzerland) [Bioengineering (Basel)] 2024 Aug 31; Vol. 11 (9). Date of Electronic Publication: 2024 Aug 31.
DOI: 10.3390/bioengineering11090883
Abstrakt: In this paper, a method for the classification of anomalous heartbeats from compressed ECG signals is proposed. The method operating on signals acquired by compressed sensing is based on a feature extraction stage consisting of the evaluation of the Discrete Cosine Transform (DCT) coefficients of the compressed signal and a classification stage performed by means of a set of k-nearest neighbor ensemble classifiers. The method was preliminarily tested on five classes of anomalous heartbeats, and it achieved a classification accuracy of 99.40%.
Databáze: MEDLINE
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