Paper Citation Record · LEDGER

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings

As of 17 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2411.17711.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17711 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:58:15.664823Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:59:17.762781Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-13T20:28:14.213828Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fe27b50a-7d25-4d20-bc17-ed897b40e833 · outbound

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

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Large-scale Training of Foundation Models for Wearable Biosignals

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.442317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3451353-b441-4274-93fe-587dc821888b · outbound

This paper cites Deep learning for ecg arrhythmia detection and classification: an overview of progress for period 2017–2023,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Deep learning for ecg arrhythmia detection and classification: an overview of progress for period 2017–2023,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.422429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bf775842-d80e-4877-b4ad-6f6e82e987b4 · outbound

This paper cites Validation of an automated artificial intelligence system for 12-lead ecg interpretation,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Validation of an automated artificial intelligence system for 12-lead ecg interpretation,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.408332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a91df9fd-3fc6-48b9-8020-fbd3e04109fc · outbound

This paper cites Attention-based convolutional denoising autoencoder for two-lead ecg denoising and arrhythmia classification,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Attention-based convolutional denoising autoencoder for two-lead ecg denoising and arrhythmia classification,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.393691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d0540d84-a0cb-4b03-9873-f4faa75712f4 · outbound

This paper cites Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.378827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6e896244-3794-4ac4-aa4b-7d4c3eb87281 · outbound

This paper cites Diagnostic and prognostic utility of ecg for left ventricular hypertrophy defined by mri in relationship to ethnicity: The multi-ethnic study of atherosclerosis (mesa),.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Diagnostic and prognostic utility of ecg for left ventricular hypertrophy defined by mri in relationship to ethnicity: The multi-ethnic study of atherosclerosis (mesa),

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.364460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dc0ea9fb-1da0-4233-a88b-e3cef7abe6a7 · outbound

This paper cites Combining 1d cnn and lstm for automated myocardial infarction detection from ecg signals,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Combining 1d cnn and lstm for automated myocardial infarction detection from ecg signals,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.349662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b3d5bd5b-e22c-40f0-abed-02274e9101fa · outbound

This paper cites Left atrial overload detec- tion in ecg using frequency domain features with convolutional neural network,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Left atrial overload detec- tion in ecg using frequency domain features with convolutional neural network,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.334611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation af0308d4-b7f9-4229-8a02-99c0ae37f1e2 · outbound

This paper cites An investigation of the contextual distribution of false pos- itives in a deep learning-based atrial fibrillation detection algorithm,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings An investigation of the contextual distribution of false pos- itives in a deep learning-based atrial fibrillation detection algorithm,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.318638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d1ca61a9-da8d-4220-baf0-24b1ad77b2af · outbound

This paper cites Ecg-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Ecg-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.302446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 83c766b7-6060-431e-92a1-9c6e2ffab38f · outbound

This paper cites Ecg-transcovnet: A hybrid transformer model for accurate arrhythmia detection using electrocardiogram signals,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Ecg-transcovnet: A hybrid transformer model for accurate arrhythmia detection using electrocardiogram signals,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.286865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b746ef58-2200-41c3-8f3d-8da4329ae62a · outbound

This paper cites Msgformer: A multi-scale grid transformer network for 12-lead ecg arrhythmia detec- tion,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Msgformer: A multi-scale grid transformer network for 12-lead ecg arrhythmia detec- tion,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.272038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d1da6b1-3514-4998-932e-999de5a8fe1a · outbound

This paper cites Apneanet: A hybrid 1dcnn-lstm architecture for detection of obstructive sleep apnea using digitized ecg signals,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Apneanet: A hybrid 1dcnn-lstm architecture for detection of obstructive sleep apnea using digitized ecg signals,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.257180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f2c453e5-c8b2-47a0-a89d-bc4d6748c1ce · outbound

This paper cites 1d cnn framework on ecg signals,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings 1d cnn framework on ecg signals,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.241802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5dea3027-518e-4f20-a41c-e6a4c73ec974 · outbound

This paper cites Automatic diagnosis of the 12-lead ecg using a deep neural network,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Automatic diagnosis of the 12-lead ecg using a deep neural network,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.225516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7ba943bd-ba2e-45bc-a962-c9a207ce2378 · outbound

This paper cites An end-to-end atrial fibrillation detec- tion by a novel residual-based temporal attention convolutional neural network with exponential nonlinearity loss,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings An end-to-end atrial fibrillation detec- tion by a novel residual-based temporal attention convolutional neural network with exponential nonlinearity loss,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.210062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fd341f85-b4ca-444d-8c91-6b080e9d9239 · outbound

This paper cites Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and transformer architecture,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Automatic segmentation of atrial fibrillation and flutter in single-lead electrocardiograms by self-supervised learning and transformer architecture,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.194456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d830553a-7c8a-40f7-8b71-9fffcf338b08 · outbound

This paper cites Improving ballistocardiogram-based continuous heart rate variability monitoring: A self-supervised learning approach,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Improving ballistocardiogram-based continuous heart rate variability monitoring: A self-supervised learning approach,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.177762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2e1887dd-89f2-4428-80d7-c47268d6fa37 · outbound

This paper cites Learning with incomplete labels of multisource datasets for ecg classification,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Learning with incomplete labels of multisource datasets for ecg classification,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.162529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e01cd41d-8c95-4dfe-a839-bc97f7c0e061 · outbound

This paper cites Clocs: Contrastive learning of cardiac signals across space, time, and patients,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Clocs: Contrastive learning of cardiac signals across space, time, and patients,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.529202Z

Source-reported events for the cited work

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Observation 5605391a-99e2-4f57-9d53-a884ca052d08 · outbound

This paper cites Adversarial spatiotemporal contrastive learning for electrocardiogram signals,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Adversarial spatiotemporal contrastive learning for electrocardiogram signals,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.136527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dd2be735-32e9-42c6-8038-ff1782efa3f2 · outbound

This paper cites Self-supervised representation learning from 12-lead ecg data,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Self-supervised representation learning from 12-lead ecg data,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.119504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b8ec6f0b-f1a9-4a1f-8e6a-c72528a3e3d9 · outbound

This paper cites Maefe: Masked autoencoders family of electrocardiogram for self- supervised pretraining and transfer learning,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Maefe: Masked autoencoders family of electrocardiogram for self- supervised pretraining and transfer learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.104121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation be709204-fa0b-474e-bf0c-fb71669bad35 · outbound

This paper cites Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.545818Z

Source-reported events for the cited work

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Observation df4c7bce-8014-4047-9acb-fde827338efb · outbound

This paper cites An effective data enhancement method for classification of ecg arrhythmia,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings An effective data enhancement method for classification of ecg arrhythmia,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.088715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c437ae38-b78a-4a9e-963a-d346020e7b73 · outbound

This paper cites Electrocardio panorama: Synthesizing new ecg views with self-supervision,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Electrocardio panorama: Synthesizing new ecg views with self-supervision,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.072972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 96394f9e-650f-4d64-bde9-d56499449452 · outbound

This paper cites The discrete wavelet transform: wedding the a trous and mallat algorithms,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings The discrete wavelet transform: wedding the a trous and mallat algorithms,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.057401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fb25ee22-dfeb-47b8-9fd6-92a0044ed74e · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.563517Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 596292db-0789-4f49-b2bd-ab21f6683f36 · outbound

This paper cites BEiT: BERT Pre-Training of Image Transformers.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings BEiT: BERT Pre-Training of Image Transformers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.567968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0c0551b-0145-4917-ae87-e1f1c8a17db1 · outbound

This paper cites Masked au- toencoders are scalable vision learners,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Masked au- toencoders are scalable vision learners,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.572640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 00bb1f6b-c582-47b0-a830-6b02ef6644c4 · outbound

This paper cites An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.022285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d64b013b-2441-4c2a-9bba-99e82eec1720 · outbound

This paper cites St petersburg incart 12-lead arrhythmia database,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings St petersburg incart 12-lead arrhythmia database,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:16.007282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8915823c-02a1-47e4-8128-8fd5ce851a31 · outbound

This paper cites Nutzung der ekg- signaldatenbank cardiodat der ptb ¨uber das internet,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Nutzung der ekg- signaldatenbank cardiodat der ptb ¨uber das internet,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.586643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb3bb1c2-be1f-4aff-8413-cc5da9a613af · outbound

This paper cites Ptb-xl, a large publicly available electro- cardiography dataset,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Ptb-xl, a large publicly available electro- cardiography dataset,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.590851Z

Source-reported events for the cited work

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Observation 64203166-2ded-4dfb-b41c-60f8c960d59e · outbound

This paper cites Dens-ecg: A deep learning approach for ecg signal delineation,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Dens-ecg: A deep learning approach for ecg signal delineation,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.972074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c893c5a8-16c8-49e1-b0ab-9a7c11dc848b · outbound

This paper cites Self-supervised EEG Representation Learning for Automatic Sleep Staging.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Self-supervised EEG Representation Learning for Automatic Sleep Staging

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.600034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 974c22e5-29b5-48f0-8ccc-ab643a31af33 · outbound

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

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings ECG-FM: An Open Electrocardiogram Foundation Model

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.604998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b6d50ea-034b-4a16-bfb8-90ec6611a992 · outbound

This paper cites Bag of tricks for image classification with convolutional neural networks,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Bag of tricks for image classification with convolutional neural networks,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.610154Z

Source-reported events for the cited work

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Observation 1065796e-397c-4d42-88eb-0a7057e98822 · outbound

This paper cites Transformer convolutional neural networks for automated artifact detection in scalp eeg,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Transformer convolutional neural networks for automated artifact detection in scalp eeg,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.946441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 75703fd1-7de1-413e-a029-59e9d655915b · outbound

This paper cites Ecg-based biometrics using recurrent neural networks,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Ecg-based biometrics using recurrent neural networks,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.929910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 76ae2f92-f36c-45b9-b4d0-517e9e4bc8e5 · outbound

This paper cites Motor imagery eeg classification algorithm based on cnn-lstm feature fusion network,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Motor imagery eeg classification algorithm based on cnn-lstm feature fusion network,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.913554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 40f58c04-b83e-453d-b9b3-b0a8421ad174 · outbound

This paper cites Deep learning for ecg analysis: Benchmarks and insights from ptb-xl,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Deep learning for ecg analysis: Benchmarks and insights from ptb-xl,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.896945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a013ba88-3425-4582-849f-053add74eb11 · outbound

This paper cites Transformer-based Spatial-Temporal Feature Learning for EEG Decoding.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Transformer-based Spatial-Temporal Feature Learning for EEG Decoding

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.632490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35506428-090b-4f15-bb46-a5806037428a · outbound

This paper cites Conditional Generative Adversarial Nets.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Conditional Generative Adversarial Nets

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.637625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77cd36fe-e1f0-414c-9341-d0a5082adc82 · outbound

This paper cites Banach wasserstein gan,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Banach wasserstein gan,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.881298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0f63f221-0318-4ba1-99fc-866cf6477300 · outbound

This paper cites Automated and interpretable patient ecg profiles for disease detection, tracking, and discovery,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Automated and interpretable patient ecg profiles for disease detection, tracking, and discovery,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.866302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e0dcd8a6-c4d4-4073-bcca-455cfa0dbc35 · outbound

This paper cites Biot: Biosignal transformer for cross-data learning in the wild,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Biot: Biosignal transformer for cross-data learning in the wild,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.850675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c5f1d77d-4e85-46fd-8b85-b4d12a3e4dd4 · outbound

This paper cites Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.655566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4569e971-422b-4b37-9a2c-64f7717da9b2 · outbound

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

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings CREMA: A Contrastive Regularized Masked Autoencoder for Robust ECG Diagnostics across Clinical Domains

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T18:58:15.660223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dad13ec2-bfce-4a5c-8a63-123b848e2711 · outbound

This paper cites Cardiogpt: An ecg interpretation generation model,.

AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings Cardiogpt: An ecg interpretation generation model,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:58:15.835428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

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

FoundationalECGNet: A Lightweight Foundational Model for ECG-based Multitask Cardiac Analysis cites this paper.

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 a46961ef-e208-4832-90e9-4ec755bc2770 · inbound

ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook cites this paper.

ECG Foundation Models and Medical LLMs for Agentic Cardiovascular Intelligence at the Edge: A Review and Outlook AnyECG: Foundational Models for Multitask Cardiac Analysis in Real-World Settings

Reference 156

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:28:14.215217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.