Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method.
Technol Health Care
; 32(S1): 95-105, 2024.
Article
de En
| MEDLINE
| ID: mdl-38759040
ABSTRACT
BACKGROUND:
Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature.OBJECTIVE:
This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases.METHODS:
Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database.RESULTS:
The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters.CONCLUSION:
The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Troubles du rythme cardiaque
/
Électrocardiographie
/
Analyse en ondelettes
Limites:
Humans
Langue:
En
Journal:
Technol Health Care
Sujet du journal:
ENGENHARIA BIOMEDICA
/
SERVICOS DE SAUDE
Année:
2024
Type de document:
Article
Pays d'affiliation:
Chine
Pays de publication:
Pays-Bas