Automatic Classification of Anomalous ECG Heartbeats from Samples Acquired by Compressed Sensing.
Bioengineering (Basel)
; 11(9)2024 Aug 31.
Article
in En
| MEDLINE
| ID: mdl-39329626
ABSTRACT
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%.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Bioengineering (Basel)
Year:
2024
Document type:
Article
Affiliation country:
Italy
Country of publication:
Switzerland