ArticlePeerJ. Computer science2020
A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss.
Article in PeerJ. Computer science, 2020. 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.
- AI-assisted endoscopic detection of Helicobacter pylori infection in atrophic gastritis: a multicenter diagnostic study.Surgical endoscopy · 2026Article
- A Cascaded Quantized Spiking Neural Network for Real-Time ECG Arrhythmia Detection on Edge Hardware.Sensors (Basel, Switzerland) · 2026Article
- pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.Journal of medical systems · 2026Article
- Multi-region and multi-image convolutional neural network model for detecting gastric helicobacter pylori infection.Surgical endoscopy · 2026Article
- A Novel Multi-Dynamic Coupled Neural Mass Model of SSVEP.Biomimetics (Basel, Switzerland) · 2025Article
- [Research on arrhythmia classification algorithm based on adaptive multi-feature fusion network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge.Health and quality of life outcomes · 2023Article
- Review of deep learning: concepts, CNN architectures, challenges, applications, future directions.Journal of big data · 2021Article
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Authors and funding
5 authors.
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Abstract
backgroundHeart arrhythmia, as one of the most important cardiovascular diseases (CVDs), has gained wide attention in the past two decades. The article proposes a hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss.
methodsIn the method, a convolution neural network is used to extract the morphological features. The reason behind this is that the morphological characteristics of patients have inter-patient variations, which makes it difficult to accurately describe using traditional hand-craft ways. Then the extracted morphological features are combined with the RR intervals features and input into the multilayer perceptron for heartbeat classification. The RR intervals features contain the dynamic information of the heartbeat. Furthermore, considering that the heartbeat classes are imbalanced and would lead to the poor performance of minority classes, a focal loss is introduced to resolve the problem in the article.
resultsTested using the MIT-BIH arrhythmia database, our method achieves an overall positive predictive value of 64.68%, sensitivity of 68.55%,
conclusionsOur method is simple yet effective, which is potentially used for personal automatic heartbeat classification in remote medical monitoring. The source code is provided on https://github.com/JackAndCole/Deep-Neural-Network-For-Heartbeat-Classification.
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