An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

An Outlier Detection and Feature Ranking based Ensemble Learning for ECG Analysis

Automated classification of each heartbeat class from the ECG signal is important to diagnose cardiovascular diseases (CVDs) more quickly. ECG data acquired from the real-time or clinical databases contains exceptional values or extreme values called outliers. The separation and removal of outliers...

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Bibliographic Details
Main Authors: Ardeti, V.A (Author), Kolluru, V.R (Author), Patjoshi, R.K (Author), Varghese, G.T (Author)
Format: Article
Language:English
Published: Science and Information Organization 2022
Subjects:
Anomaly detection
Biomedical signal processing
Cardiology
Classification (of information)
Data handling
Decision trees
Deep learning
Diseases
ECG data
Electrocardiography
Ensemble classification
Ensemble learning
Feature extraction
Feature ranking
Improved inter quartile range
Inter quartile ranges
Majority voting
Optimized random forest
Outlier detection
Outlier Detection
Random forests
Statistics
Support vector machines
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