ArticleJournal of healthcare engineering2017
Patient-Specific Deep Architectural Model for ECG Classification.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017-2023.Frontiers in physiology · 2023Pooled it
- Cross-domain transfer learning strategy enhances interpretability of deep learning model explanations.Scientific reports · 2026Article
- Integration of Machine Learning Techniques in ECG-Based Multiclass Arrhythmia Classification with Explainability Analysis.Biosensors · 2026Article
- Towards Feasible Home ECG Monitoring: AI-Driven Detection of Clinically Critical Arrhythmias Using Single-Lead Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.Sensors (Basel, Switzerland) · 2025Article
- Advancing cardiac diagnostics: high-accuracy arrhythmia classification with the EGOLF-net model.Frontiers in physiology · 2025Article
- Swim-Rep fusion net: A new backbone with Faster Recurrent Criss Cross Polarized Attention.PloS one · 2025Article
- Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.Sensors (Basel, Switzerland) · 2024Article
- Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model.Heliyon · 2024Article
- Methodological identification of anomalies episodes in ECG streams: a systematic mapping study.BMC medical research methodology · 2024Article
- Cardiac arrhythmia detection using deep learning approach and time frequency representation of ECG signals.BMC medical informatics and decision making · 2023Article
- An ECG Classification Method Based on Multi-Task Learning and CoT Attention Mechanism.Healthcare (Basel, Switzerland) · 2023Article
- Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms.IEEE journal of translational engineering in health and medicine · 2023Article
- Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion.Frontiers in physiology · 2023Article
- Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label.IEEE journal of translational engineering in health and medicine · 2022Article
- Interpatient ECG Arrhythmia Detection by Residual Attention CNN.Computational and mathematical methods in medicine · 2022Article
- Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model.Physics in medicine and biology · 2021Article
- Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network.Entropy (Basel, Switzerland) · 2021Article
- Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems.Computational intelligence and neuroscience · 2021Article
- Classification of heart sounds based on the combination of the modified frequency wavelet transform and convolutional neural network.Medical & biological engineering & computing · 2020Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heartbeat classification is a crucial step for arrhythmia diagnosis during electrocardiographic (ECG) analysis. The new scenario of wireless body sensor network- (WBSN-) enabled ECG monitoring puts forward a higher-level demand for this traditional ECG analysis task. Previously reported methods mainly addressed this requirement with the applications of a shallow structured classifier and expert-designed features. In this study, modified frequency slice wavelet transform (MFSWT) was firstly employed to produce the time-frequency image for heartbeat signal. Then the deep learning (DL) method was performed for the heartbeat classification. Here, we proposed a novel model incorporating automatic feature abstraction and a deep neural network (DNN) classifier. Features were automatically abstracted by the stacked denoising auto-encoder (SDA) from the transferred time-frequency image. DNN classifier was constructed by an encoder layer of SDA and a softmax layer. In addition, a deterministic patient-specific heartbeat classifier was achieved by fine-tuning on heartbeat samples, which included a small subset of individual samples. The performance of the proposed model was evaluated on the MIT-BIH arrhythmia database. Results showed that an overall accuracy of 97.5% was achieved using the proposed model, confirming that the proposed DNN model is a powerful tool for heartbeat pattern recognition.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.