FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis | Paper Citation Record

Observation fccc1d09-3058-433f-a5c0-3c6674f6cabe · outbound

This paper cites Large-scale Training of Foundation Mod- els for Wearable Biosignals.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Large-scale Training of Foundation Mod- els for Wearable Biosignals

Reference 1

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This paper cites A novel multi-class imbalanced EEG signals classifi- cation based on the adaptive synthetic sampling (ADASYN) approach.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A novel multi-class imbalanced EEG signals classifi- cation based on the adaptive synthetic sampling (ADASYN) approach

Reference 2

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Observation 3c1af263-e50f-44f5-abfb-cd2dc6007971 · outbound

This paper cites Early diagnosis of cardiovascular diseases in the era of artificial intelligence: An in-depth review.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Early diagnosis of cardiovascular diseases in the era of artificial intelligence: An in-depth review

Reference 3

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This paper cites Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023

Reference 4

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Observation bbc24a23-d506-4e2a-8f14-be86b8d0f287 · outbound

This paper cites Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Interpretable Deep Learning Models for Arrhythmia Classification Based on ECG Signals Using PTB-X Dataset

Reference 5

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Observation 58049e35-4e85-44e9-8703-248c446304c6 · outbound

This paper cites A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG).

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A comprehensive review of AI-Based detection of Arrhythmia using Electrocardiogram (ECG)

Reference 6

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Observation 9faf8e7c-9df6-4a21-ba05-eb754e1c6188 · outbound

This paper cites An efficient ECG signals denoising technique based on the combination of particle swarm optimisation and wavelet transform.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis An efficient ECG signals denoising technique based on the combination of particle swarm optimisation and wavelet transform

Reference 7

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Observation def25861-21aa-46d4-994a-215101e6a9c9 · outbound

This paper cites Implementation of ECG signal processing and analysis techniques in digital signal processor based system.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Implementation of ECG signal processing and analysis techniques in digital signal processor based system

Reference 8

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Observation 243fc0e1-8d76-4cfd-ae9f-59815a6fd0da · outbound

This paper cites ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine- tuning.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine- tuning

Reference 9

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Observation 18fa4403-30f2-426e-a1d6-e7d2523f16fe · outbound

This paper cites A literature review: ECG-based models for arrhyth- mia diagnosis using artificial intelligence techniques.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A literature review: ECG-based models for arrhyth- mia diagnosis using artificial intelligence techniques

Reference 10

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Observation 658addcc-44a4-4114-ba1d-3bb288c0b881 · outbound

This paper cites Toward automated feature extraction for deep learning classification of electrocardiogram signals.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Toward automated feature extraction for deep learning classification of electrocardiogram signals

Reference 11

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Observation 8c6bde3d-f4bb-4d67-9070-7a63771ffc1d · outbound

This paper cites Basic Paediatric ECG Interpretation Principles.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Basic Paediatric ECG Interpretation Principles

Reference 12

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Observation 38342b26-c3b3-4e82-afe7-cf3ece0cc114 · outbound

This paper cites AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017

Reference 13

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Observation 96bb700c-7f7c-4170-a843-83da5f89bd98 · outbound

This paper cites Basic electrocardiography: normal and ab- normal ECG patterns.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Basic electrocardiography: normal and ab- normal ECG patterns

Reference 14

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This paper cites Fusion of edge detection and graph neural networks to classifying electrocardiogram signals.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Fusion of edge detection and graph neural networks to classifying electrocardiogram signals

Reference 15

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Observation 4c0c350e-a124-4b4e-a951-8a835d949fc3 · outbound

This paper cites MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations

Reference 16

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Observation 3cef9e43-57e9-4198-a845-b0ad89c996b0 · outbound

This paper cites Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Using domain adaptation for classification of healthy and disease conditions from mobile-captured images of standard 12-lead electrocardiograms

Reference 17

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Observation 8728f9b5-9d69-4e16-bbf4-66cb39bab57c · outbound

This paper cites Components of a new research resource for complex physiologic signals.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Components of a new research resource for complex physiologic signals

Reference 18

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Observation d71b63d7-9a84-480b-b40b-208a6675a97d · outbound

This paper cites Artifact removal from ECG signals using online recursive independent component analysis.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Artifact removal from ECG signals using online recursive independent component analysis

Reference 19

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Observation 20b52e93-dcae-4949-8e61-d06dc054f8e1 · outbound

This paper cites A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A comprehensive review on efficient artificial intelligence models for classification of abnormal cardiac rhythms using electrocardiograms

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Observation fc45b811-35ed-40c5-be2e-783f853b79b9 · outbound

This paper cites Foundation Models in Electrocardiogram: A Review.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Foundation Models in Electrocardiogram: A Review

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Observation badfad5c-d2ed-4fc7-86d8-550f6dd8e8c0 · outbound

This paper cites Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

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Observation 98986e0e-292b-4c8a-a9f5-1eeaaec27159 · outbound

This paper cites Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Reference 23

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Observation 3efb37c0-4a85-40eb-9b5a-14f8f2c3a8d5 · outbound

This paper cites A transformer-based deep neural network for arrhythmia detection using continuous ECG signals.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A transformer-based deep neural network for arrhythmia detection using continuous ECG signals

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This paper cites Using AUC and accuracy in evaluating learning algorithms.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Using AUC and accuracy in evaluating learning algorithms

Reference 25

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This paper cites The Applications of Deep Learning in ECG Classification for Disease Diagnosis: A Systematic Review and Meta- Data Analysis.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis The Applications of Deep Learning in ECG Classification for Disease Diagnosis: A Systematic Review and Meta- Data Analysis

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This paper cites A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform

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Observation 218dea8a-978f-4e8b-8b38-26d4f2fde477 · outbound

This paper cites Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Comparative study of ECG signal denoising by wavelet thresholding in empirical and variational mode decomposition domains

Reference 28

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This paper cites DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information

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This paper cites Clinical tests: sensitivity and specificity.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Clinical tests: sensitivity and specificity

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This paper cites ECG Signal Denoising Method Based on Disentangled Autoencoder.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis ECG Signal Denoising Method Based on Disentangled Autoencoder

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This paper cites Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis

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This paper cites Multi-dimensional signal processing for non-linear structural dynamics.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Multi-dimensional signal processing for non-linear structural dynamics

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This paper cites Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution

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This paper cites Current and future use of artificial intelligence in electrocardiography.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Current and future use of artificial intelligence in electrocardiography

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This paper cites ECG arrhythmias classification based on deep learning methods and transfer learning technique.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis ECG arrhythmias classification based on deep learning methods and transfer learning technique

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This paper cites ECG-FM: An Open Electrocardiogram Founda- tion Model.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis ECG-FM: An Open Electrocardiogram Founda- tion Model

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This paper cites Deep Generative Models: The winning key for large and easily accessible ECG datasets?.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Deep Generative Models: The winning key for large and easily accessible ECG datasets?

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This paper cites Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engi- neering. J Big Data. 2024; 11: 87.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Data oversampling and imbalanced datasets: an investigation of performance for machine learning and feature engi- neering. J Big Data. 2024; 11: 87

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This paper cites Transfer learning in ECG diagnosis: Is it effective?.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Transfer learning in ECG diagnosis: Is it effective?

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This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

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This paper cites Robustness of Deep Learning models in electro- cardiogram noise detection and classification.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Robustness of Deep Learning models in electro- cardiogram noise detection and classification

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FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Unresolved cited work

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This paper cites A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A Novel Deep Learning based Gated Recurrent Unit with Extreme Learning Machine for Electrocardiogram (ECG) Signal Recognition

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This paper cites Risk factors of deaths related to cardiovascular diseases in World Health Organization (WHO) member countries.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Risk factors of deaths related to cardiovascular diseases in World Health Organization (WHO) member countries

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This paper cites A Systematic Review of ECG Arrhythmia Clas- sification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A Systematic Review of ECG Arrhythmia Clas- sification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility

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This paper cites Unveiling person- ality traits through Bangla speech using Morlet wavelet transformation and BiG.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Unveiling person- ality traits through Bangla speech using Morlet wavelet transformation and BiG

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This paper cites CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains

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This paper cites Investigating advantages and disadvantages of the analysis of a geometrical surface structure with the use of Fourier and wavelet transform.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Investigating advantages and disadvantages of the analysis of a geometrical surface structure with the use of Fourier and wavelet transform

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Observation 55282aec-1e0a-4f68-93ce-9c2a85e7cf2e · outbound

This paper cites Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

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This paper cites Deep learning for ECG analysis: Benchmarks and insights from PTB-XL.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Deep learning for ECG analysis: Benchmarks and insights from PTB-XL

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This paper cites Class-driven graph attention network for multi-label time series classification in mobile health digital twins.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Class-driven graph attention network for multi-label time series classification in mobile health digital twins

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This paper cites Foundation model of ECG diagnosis: Diag- nostics and explanations of any form and rhythm on ECG.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Foundation model of ECG diagnosis: Diag- nostics and explanations of any form and rhythm on ECG

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This paper cites Drug-induced arrhythmias: a scientific state- ment from the American Heart Association.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Drug-induced arrhythmias: a scientific state- ment from the American Heart Association

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Observation e76be5f9-aff1-42bc-8b51-6e31d0676465 · outbound

This paper cites Generalized Daubechies wavelet families.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Generalized Daubechies wavelet families

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.738751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6479499d-78b4-40e5-86d8-0cc91f210e3c · outbound

This paper cites Interactive ECG annotation: An artificial intelli- gence method for smart ECG manipulation.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Interactive ECG annotation: An artificial intelli- gence method for smart ECG manipulation

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.748448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0cac0ee3-c177-436f-8021-47cbae30d9ae · outbound

This paper cites AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.762781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cfc6688-1f6c-4273-9429-6bc2f9fdc525 · outbound

This paper cites Transfer learning for ECG classification.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Transfer learning for ECG classification

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.780531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74e4ebf4-8669-4dac-81c8-0e1cb7a2bb2c · outbound

This paper cites Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diag- nosis and management.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Deep learning and electrocardiography: systematic review of current techniques in cardiovascular disease diag- nosis and management

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.800268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5fede134-5a66-4cf6-9996-c06745d36e73 · outbound

This paper cites Denoising ECG signal using Daubechies and Symlet wavelet transform techniques.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Denoising ECG signal using Daubechies and Symlet wavelet transform techniques

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.812122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ad2e09-091c-4e54-8323-6b9e2a2f4abd · outbound

This paper cites an unresolved cited work.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Unresolved cited work

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.819684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47dae142-76fd-4084-aff4-0441d90d282d · outbound

This paper cites Brant: Foundation model for intracranial neu- ral signal.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Brant: Foundation model for intracranial neu- ral signal

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.834156Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50303c56-6e9a-430b-9183-7b5c20fb2860 · outbound

This paper cites Opportunities and challenges of noise interference suppression algorithms for dynamic ECG signals in wearable devices: A review.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Opportunities and challenges of noise interference suppression algorithms for dynamic ECG signals in wearable devices: A review

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.844084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a65d1ddd-90f6-4854-8965-55cc769832f4 · outbound

This paper cites Lumbar spine localisation method based on feature fusion.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Lumbar spine localisation method based on feature fusion

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.855920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a346d43-68d3-475a-9bc8-037e47420094 · outbound

This paper cites Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis Transforming ECG Diagnosis:An In-depth Review of Transformer-based DeepLearning Models in Cardiovascular Disease Detection

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.864556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dee1a82-4c67-4a84-b0af-764fa59e225e · outbound

This paper cites A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing coun- tries.

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing coun- tries

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T19:59:17.879436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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