ArticleSensors (Basel, Switzerland)2021
Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Hybrid Multi-Domain ECG Feature Learning with mRMR and CNN-Transformer for Cardiac Disease Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Article
- Enhancing cardiac disease prediction with explainable bidirectional LSTM.Scientific reports · 2025Article
- Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing ECG disease detection accuracy through deep learning models and P-QRS-T waveform features.PloS one · 2025Article
- Learning EKG Diagnostic Models with Hierarchical Class Label Dependencies.Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- ) · 2023Article
- A Deep Learning Architecture Using 3D Vectorcardiogram to Detect R-Peaks in ECG with Enhanced Precision.Sensors (Basel, Switzerland) · 2023Article
- Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model.Sensors (Basel, Switzerland) · 2022Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- ECG Classification Using Orthogonal Matching Pursuit and Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification.Computational intelligence and neuroscience · 2022Article
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Authors and funding
3 authors.
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Abstract
Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of which in electrocardiographic signals is gaining importance. So far, limited studies or optimizations using DNN can be found using ECG databases. To explore and achieve effective ECG recognition, this paper presents a convolutional neural network to perform the encoding of a single QRS complex with the addition of entropy-based features. This study aims to determine what combination of signal information provides the best result for classification purposes. The analyzed information included the raw ECG signal, entropy-based features computed from raw ECG signals, extracted QRS complexes, and entropy-based features computed from extracted QRS complexes. The tests were based on the classification of 2, 5, and 20 classes of heart diseases. The research was carried out on the data contained in a PTB-XL database. An innovative method of extracting QRS complexes based on the aggregation of results from established algorithms for multi-lead signals using the k-mean method, at the same time, was presented. The obtained results prove that adding entropy-based features and extracted QRS complexes to the raw signal is beneficial. Raw signals with entropy-based features but without extracted QRS complexes performed much worse.
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