ArticleBMC medical informatics and decision making2021
A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Disentangling individual heterogeneity reveals robust network and molecular signatures of major depressive disorder with suicidal ideation.Translational psychiatry · 2026Article
- Heterogeneity-Aware, Multiscale Annotation of Shared and Specific Neurobiological Signatures among Major Neurodevelopmental Disorders.Research (Washington, D.C.) · 2026Article
- Gallstone Classification Using Random Forest Optimized by Sand Cat Swarm Optimization Algorithm with SHAP and DiCE-Based Interpretability.Sensors (Basel, Switzerland) · 2025Article
- Identification of Sarin Simulant DMMP Based on a Laminated MOS Sensor Using Article Swarm Optimization-Backpropagation Neural Network.Sensors (Basel, Switzerland) · 2025Article
- Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model.Scientific reports · 2025Article
- Near-infrared prediction of total phosphorus in leaves content in korla fragrant pear with growth period specificity via spectral modeling.Frontiers in plant science · 2025Article
- Current and Future Use of Artificial Intelligence in Electrocardiography.Journal of cardiovascular development and disease · 2023Review
- Artificial intelligence: Revolutionizing pediatric emergency care - A narrative review.International journal of critical illness and injury scienceReview
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Authors and funding
7 authors.
Funding
Abstract
backgroundAs proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. And AECG got many species and great variation. What's more, the ECG result obtained after a long time past, which can not reach the purpose of early warning or real-time disease diagnosis. Therefore, developing an intelligent classification model with an accurate feature extraction method to identify AECG is of quite significance. This study aimed to explore an accurate feature extraction method of ECG and establish a suitable model for identifying AECG and the diagnosis of heart disease.
methodsIn this research, the wavelet combined with four operations and adaptive threshold methods were applied to filter the ECG and extract its feature waves first. Then, a BP neural network (BPNN) intelligent model and a particle swarm optimization (PSO) improved BPNN (PSO-BPNN) intelligent model based on MIT-BIH open database was established to identify ECG. To reduce the complexity of the model, the principal component analysis (PCA) was used to minimize the feature dimension.
resultsWavelet transforms combined four operations and adaptive threshold methods were capable of ECG filtering and feature extraction. PCA can significantly deduce the modeling feature dimension to minimize the complexity and save classification time. The PSO-BPNN intelligent model was suitable for identifying five types of ECG and showed better effects while comparing it with the BPNN model.
conclusionIn summary, it was further concluded that the PSO-BPNN intelligent model would be a suitable way to identify AECG and provide a tool for the diagnosis of heart disease.
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