Review

kaggle ecg dataset (Kaggle Inc)

About

News

Press Release

Team

Advisors

Partners

Contact

Bioz Stars

Bioz vStars

Buy from Supplier

Structured Review

Kaggle Inc kaggle ecg dataset
Kaggle Ecg Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/kaggle+ecg+dataset/pm42270808-22-9-9?v=Kaggle+Inc
Average 86 stars, based on 1 article reviews

kaggle ecg dataset - by Bioz Stars, 2026-07

86/100 stars

Images

Image Search Results

Architecture of the SimCardioNet model for ECG classification, integrating pre-processing, CNN-based feature extraction, contrastive learning through SimCLR, and classification using ResNet blocks.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Architecture of the SimCardioNet model for ECG classification, integrating pre-processing, CNN-based feature extraction, contrastive learning through SimCLR, and classification using ResNet blocks.

Article Snippet: We evaluate SimCardioNet across three distinct ECG image datasets: (1) a 4-class Pakistani clinical ECG dataset (Dataset I), (2) an external Kaggle electrocardiography dataset for out-of-distribution validation (Dataset II), and (3) the large-scale PTB-XL benchmark (Dataset III) covering five diagnostic superclasses.

Techniques: Extraction

PCA visualization of the 128-dimensional projection head features, showing the separation of ECG classes along the first two principal components.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: PCA visualization of the 128-dimensional projection head features, showing the separation of ECG classes along the first two principal components.

Techniques:

PCA visualization of self-supervised ECG feature representations from the Dataset III, illustrating class-wise distributions and overlap among normal and pathological conditions.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: PCA visualization of self-supervised ECG feature representations from the Dataset III, illustrating class-wise distributions and overlap among normal and pathological conditions.

Techniques:

t-SNE visualization of the 128-dimensional projection head features, illustrating the clustering and separation of ECG classes in a 2D space.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: t-SNE visualization of the 128-dimensional projection head features, illustrating the clustering and separation of ECG classes in a 2D space.

Techniques:

t-SNE projection of self-supervised ECG feature embeddings from the Dataset III, illustrating local clustering behavior and class-wise distribution of normal and pathological recordings.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: t-SNE projection of self-supervised ECG feature embeddings from the Dataset III, illustrating local clustering behavior and class-wise distribution of normal and pathological recordings.

Techniques:

Conv1 activation maps for sample (Class: 0), showing the extraction of low-level features such as rhythm and shape from the ECG signal, with activation focused on specific regions of the waveform of Dataset I.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv1 activation maps for sample (Class: 0), showing the extraction of low-level features such as rhythm and shape from the ECG signal, with activation focused on specific regions of the waveform of Dataset I.

Techniques: Activation Assay, Extraction

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Techniques: Activation Assay, Extraction

Conv2 activation maps for sample (Class: 0), highlighting intermediate-level features such as the P-QRS-T wave patterns and focusing on the shape of the ECG waveform Dataset I.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv2 activation maps for sample (Class: 0), highlighting intermediate-level features such as the P-QRS-T wave patterns and focusing on the shape of the ECG waveform Dataset I.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Techniques: Activation Assay

Conv3 activation maps for sample (Class: 0), highlighting deep features and advanced patterns in the ECG signal, such as arrhythmias and myocardial infarctions Dataset I.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv3 activation maps for sample (Class: 0), highlighting deep features and advanced patterns in the ECG signal, such as arrhythmias and myocardial infarctions Dataset I.

Techniques: Activation Assay

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Conv3 activation maps for sample (Class: 0), highlighting deep features and advanced patterns in the ECG signal, such as arrhythmias and myocardial infarctions of Dataset II.

Techniques: Activation Assay

Visualization of conv1 activation maps for a normal ECG recording, showing channel-wise responses of the first convolutional layer to different temporal and morphological signal patterns.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Visualization of conv1 activation maps for a normal ECG recording, showing channel-wise responses of the first convolutional layer to different temporal and morphological signal patterns.

Techniques: Activation Assay

Mean confusion matrix for cross-validation (CV) in multi-class ECG classification of Dataset I.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Mean confusion matrix for cross-validation (CV) in multi-class ECG classification of Dataset I.

Techniques: Biomarker Discovery

Mean confusion matrix for cross-validation (CV) in multi-class ECG classification of Dataset II.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Mean confusion matrix for cross-validation (CV) in multi-class ECG classification of Dataset II.

Techniques: Biomarker Discovery

Mean confusion matrix for cross-validation in multi-class ECG classification of Dataset III.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Mean confusion matrix for cross-validation in multi-class ECG classification of Dataset III.

Techniques: Biomarker Discovery

ECG waveform samples with true and predicted labels.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: ECG waveform samples with true and predicted labels.

Techniques:

Grad-CAM heatmaps highlighting regions driving the model’s predictions. Warm colors indicate higher contribution; overlays show attention focused on ECG waveforms rather than page artifacts od Dataset I.

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Figure Lengend Snippet: Grad-CAM heatmaps highlighting regions driving the model’s predictions. Warm colors indicate higher contribution; overlays show attention focused on ECG waveforms rather than page artifacts od Dataset I.

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Techniques:

Journal: Scientific Reports

Article Title: A hybrid learning framework for automated multiclass electrocardiogram classification with SimCardioNet

doi: 10.1038/s41598-026-36932-1

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: ECG signal with annotated features.

Article Snippet: Furthermore, although the model was exceptionally good on the Kaggle ECG datasets, additional validation on heterogeneous, multi-source datasets is needed to confirm robustness across different patient populations.

Techniques:

Block diagram of the proposed ECG classification framework.

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Block diagram of the proposed ECG classification framework.

Techniques: Blocking Assay

Representative ECG waveforms from the dataset. Horizontal axis: time (seconds); vertical axis: amplitude (mV). Key takeaway: Normal and abnormal ECGs display clear differences in waveform morphology, supporting their use for classification.

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Representative ECG waveforms from the dataset. Horizontal axis: time (seconds); vertical axis: amplitude (mV). Key takeaway: Normal and abnormal ECGs display clear differences in waveform morphology, supporting their use for classification.

Techniques:

Boxplot distribution of statistical features (mean, standard deviation, minimum, maximum, and range) across Normal and Abnormal classes.Key takeaway: Abnormal ECG signals exhibit higher variability, providing discriminative cues for classification.

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Boxplot distribution of statistical features (mean, standard deviation, minimum, maximum, and range) across Normal and Abnormal classes.Key takeaway: Abnormal ECG signals exhibit higher variability, providing discriminative cues for classification.

Techniques: Standard Deviation

Confusion matrix of the Random Forest model for ECG classification, summarizing classification outcomes across Normal and Abnormal classes. Key takeaway: Abnormal ECG signals exhibit higher variability, providing discriminative cues for classification.

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Confusion matrix of the Random Forest model for ECG classification, summarizing classification outcomes across Normal and Abnormal classes. Key takeaway: Abnormal ECG signals exhibit higher variability, providing discriminative cues for classification.

Techniques:

Sample ECG signals from Swin Transformer analysis.

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Sample ECG signals from Swin Transformer analysis.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: ECG signal with annotated features.

Article Snippet: The model was trained for 30 epochs using the publicly available Kaggle ECG image dataset with a 20% validation split.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Block diagram of the proposed ECG classification framework.

Article Snippet: The model was trained for 30 epochs using the publicly available Kaggle ECG image dataset with a 20% validation split.

Techniques: Blocking Assay

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Article Snippet: The model was trained for 30 epochs using the publicly available Kaggle ECG image dataset with a 20% validation split.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Article Snippet: The model was trained for 30 epochs using the publicly available Kaggle ECG image dataset with a 20% validation split.

Techniques: Standard Deviation

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Article Snippet: The model was trained for 30 epochs using the publicly available Kaggle ECG image dataset with a 20% validation split.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Sample ECG signals from Swin Transformer analysis.

Article Snippet: The model was trained for 30 epochs using the publicly available Kaggle ECG image dataset with a 20% validation split.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: ECG signal with annotated features.

Article Snippet: Kaggle ECG Heartbeat Dataset , 4997 , 2079 , 2918 , 3497 (70%) , 500 (10%) , 1000 (20%) , Kaggle Link.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Block diagram of the proposed ECG classification framework.

Article Snippet: Kaggle ECG Heartbeat Dataset , 4997 , 2079 , 2918 , 3497 (70%) , 500 (10%) , 1000 (20%) , Kaggle Link.

Techniques: Blocking Assay

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Article Snippet: Kaggle ECG Heartbeat Dataset , 4997 , 2079 , 2918 , 3497 (70%) , 500 (10%) , 1000 (20%) , Kaggle Link.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Article Snippet: Kaggle ECG Heartbeat Dataset , 4997 , 2079 , 2918 , 3497 (70%) , 500 (10%) , 1000 (20%) , Kaggle Link.

Techniques: Standard Deviation

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Article Snippet: Kaggle ECG Heartbeat Dataset , 4997 , 2079 , 2918 , 3497 (70%) , 500 (10%) , 1000 (20%) , Kaggle Link.

Techniques:

Journal: Scientific Reports

Article Title: Automated heart disease detection using Swin Transformer and ECG signal processing: a high-accuracy approach

doi: 10.1038/s41598-025-23097-6

Figure Lengend Snippet: Sample ECG signals from Swin Transformer analysis.

Article Snippet: Kaggle ECG Heartbeat Dataset , 4997 , 2079 , 2918 , 3497 (70%) , 500 (10%) , 1000 (20%) , Kaggle Link.

Techniques:

A system for analyzing ECG results in the detection of cardiac events.

Journal: Scientific Reports

Article Title: Hybrid deep learning framework for heart disease prediction using ECG signal images

doi: 10.1038/s41598-025-10062-6

Figure Lengend Snippet: A system for analyzing ECG results in the detection of cardiac events.

Article Snippet: This information is taken from the ECG Heartbeat classification Kaggle dataset .

Techniques:

Predicting heart disease from electrocardiogram signal images using a hybrid deep learning framework.

Journal: Scientific Reports

Article Title: Hybrid deep learning framework for heart disease prediction using ECG signal images

doi: 10.1038/s41598-025-10062-6

Figure Lengend Snippet: Predicting heart disease from electrocardiogram signal images using a hybrid deep learning framework.

Article Snippet: This information is taken from the ECG Heartbeat classification Kaggle dataset .

Techniques:

Review

Structured Review

Images

Similar Products

Image Search Results