Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography | Paper Citation Record

Observation 46fa1a43-17f9-4953-9da1-7657e3a112d5 · outbound

This paper cites Deep learning for healthcare applications based on physiological signals: A review,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Deep learning for healthcare applications based on physiological signals: A review,

Reference 1

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Observation 18868781-093f-4806-b8ec-b558adfd1a18 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography DINOv2: Learning Robust Visual Features without Supervision

Reference 2

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This paper cites A simple framework for contrastive learning of visual representations,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography A simple framework for contrastive learning of visual representations,

Reference 3

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Observation a6511697-6fc0-4d6b-b5cf-1531d21c6df1 · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised learning,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Bootstrap your own latent: A new approach to self-supervised learning,

Reference 4

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This paper cites Context autoencoder for self- supervised representation learning,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Context autoencoder for self- supervised representation learning,

Reference 5

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Observation 0a0924e9-a3ac-41aa-b62a-eb777c4d39a1 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Representation Learning with Contrastive Predictive Coding

Reference 6

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Observation 48cd722e-a3ec-4074-85c9-f49e6ec5580c · outbound

This paper cites Masked autoencoders for point-cloud self-supervised learning,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Masked autoencoders for point-cloud self-supervised learning,

Reference 7

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Observation 438fe992-383e-471f-bcfa-07f7059241ff · outbound

This paper cites Self-supervised speech representation learning: A review,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Self-supervised speech representation learning: A review,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.336248Z

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Observation 578f4927-2afc-45bb-9f51-db6541a51f14 · outbound

This paper cites Which augmentation should I use? An empirical investigation of augmentations for self-supervised phonocardiogram representation learning,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Which augmentation should I use? An empirical investigation of augmentations for self-supervised phonocardiogram representation learning,

Reference 9

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Observation b179acb5-7329-471c-bc8f-4df078249edc · outbound

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

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network,

Reference 10

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Observation 371f590c-e5c5-4e06-bc4f-f034f5d27090 · outbound

This paper cites AF classification from a short single-lead ECG recording: The PhysioNet/Computing in Cardiology Challenge 2017,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography AF classification from a short single-lead ECG recording: The PhysioNet/Computing in Cardiology Challenge 2017,

Reference 11

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Observation 83570c57-c3df-47a8-b4e8-e6970958f45c · outbound

This paper cites Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals,

Reference 12

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Observation 46eff3fa-7a17-4fb2-8d07-50635f868206 · outbound

This paper cites ECG segmentation using a deep learning model,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography ECG segmentation using a deep learning model,

Reference 13

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Observation b517dce6-e924-44dc-93d8-a4fb150d5324 · outbound

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

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Self-supervised representation learning from 12-lead ECG data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.257887Z

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Observation 7a61cec1-c813-44c9-87b1-f8d03c974a31 · outbound

This paper cites 3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography 3KG: Contrastive learning of 12-lead electrocardiograms using physiologically-inspired augmentations,

Reference 15

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Observation 8ece18d7-f90b-46c5-935b-7880747238db · outbound

This paper cites sCL-ST: Su- pervised contrastive learning with semantic transformations for multiple- lead ECG arrhythmia classification,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography sCL-ST: Su- pervised contrastive learning with semantic transformations for multiple- lead ECG arrhythmia classification,

Reference 16

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Observation fa7ae092-25f3-46a5-80db-876e2080e065 · outbound

This paper cites CLECG: A novel contrastive learning framework for electrocardiogram arrhythmia classification,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography CLECG: A novel contrastive learning framework for electrocardiogram arrhythmia classification,

Reference 17

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Observation 3e5e9976-0070-4f0f-ada1-3d6704fdf213 · outbound

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

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography CLOCS: Contrastive learning of cardiac signals across space, time and patients,

Reference 18

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This paper cites Contrast everything: A hierarchical contrastive framework for medical time-series,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Contrast everything: A hierarchical contrastive framework for medical time-series,

Reference 19

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This paper cites Lead-agnostic self-supervised learning for local and global representations of electrocardiogram,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Lead-agnostic self-supervised learning for local and global representations of electrocardiogram,

Reference 20

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This paper cites Multi-channel masked autoen- coder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Multi-channel masked autoen- coder and comprehensive evaluations for reconstructing 12-lead ECG from arbitrary single-lead ECG,

Reference 21

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Observation 2b721905-3d5a-4c22-9c82-205d030ca766 · outbound

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

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram

Reference 22

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This paper cites Learn- ing representations for multi-lead electrocardiograms from morphol- ogy–rhythm contrast,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Learn- ing representations for multi-lead electrocardiograms from morphol- ogy–rhythm contrast,

Reference 23

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This paper cites Self-supervised inter–intra period-aware ECG representation learning for detecting atrial fibrillation,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Self-supervised inter–intra period-aware ECG representation learning for detecting atrial fibrillation,

Reference 24

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Observation 58ba29f0-8456-4e1a-aec1-f01d4c209fc3 · outbound

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Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Unresolved cited work

Reference 25

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This paper cites Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network,

Reference 26

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Observation 8e237e44-b4bb-4655-928a-0ba414fafbc2 · outbound

This paper cites Surawicz and T.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Surawicz and T

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.089491Z

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This paper cites Support vector machine- based expert system for reliable heartbeat recognition,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Support vector machine- based expert system for reliable heartbeat recognition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.075928Z

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Observation 94eb1a68-a28e-4cfd-a9fb-56270ac08413 · outbound

This paper cites Automated patient-specific clas- sification of premature ventricular contractions,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Automated patient-specific clas- sification of premature ventricular contractions,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.061169Z

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This paper cites ECG feature extraction and classification using wavelet transform and support vector machines,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography ECG feature extraction and classification using wavelet transform and support vector machines,

Reference 30

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Observation e0f40be0-fe39-4703-89a5-c49c1b27bfcd · outbound

This paper cites Patient-specific ECG classification by deeper CNN from generic to dedicated,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Patient-specific ECG classification by deeper CNN from generic to dedicated,

Reference 31

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This paper cites A novel imbalanced-dataset mitigation and ECG classification model based on combined 1D CBAM autoen- coder and lightweight CNN,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography A novel imbalanced-dataset mitigation and ECG classification model based on combined 1D CBAM autoen- coder and lightweight CNN,

Reference 32

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Observation ad8f7ad4-ceb3-41ea-a702-ccdc20deba95 · outbound

This paper cites A deep learning model for the classification of atrial fibrillation in critically ill patients,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography A deep learning model for the classification of atrial fibrillation in critically ill patients,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:47.001163Z

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Observation 88f5d924-4418-462e-9355-9dcf4bdf6dc6 · outbound

This paper cites Classification of ECG arrhythmia using recurrent neural networks,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Classification of ECG arrhythmia using recurrent neural networks,

Reference 34

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Observation 72339b50-3dc2-48c8-a2e6-10afca09e436 · outbound

This paper cites Deep learning-based classification of ECG signals using RNN and LSTM mechanism,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Deep learning-based classification of ECG signals using RNN and LSTM mechanism,

Reference 35

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Observation b41eeeb4-f866-42d1-aef0-610c6d242244 · outbound

This paper cites Generative adversarial network with transformer generator for boosting ECG classification,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Generative adversarial network with transformer generator for boosting ECG classification,

Reference 36

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Observation 421cff4b-6876-46d7-93ab-0d362da51a45 · outbound

This paper cites ECGTransForm: Empowering adap- tive ECG arrhythmia classification framework with bidirectional trans- former,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography ECGTransForm: Empowering adap- tive ECG arrhythmia classification framework with bidirectional trans- former,

Reference 37

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Observation 761b7113-5ad7-472b-9dd1-f714d2ca2016 · outbound

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

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography ECG-FM: An Open Electrocardiogram Foundation Model

Reference 38

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Observation 414ffc12-40cb-4329-9945-524260b7fc4b · outbound

This paper cites DinoSR: Self- distillation and online clustering for self-supervised speech representa- tion learning,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography DinoSR: Self- distillation and online clustering for self-supervised speech representa- tion learning,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.926975Z

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Observation 3cd8dd80-be24-46c0-a9b2-c104a42c7e50 · outbound

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

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Adversarial spatiotemporal contrastive learning for electrocardiogram signals,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.912075Z

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Observation ad0a6fe4-de3a-4357-b40a-ba2eccde9541 · outbound

This paper cites Boosting contrastive self-supervised learning with false-negative can- cellation,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Boosting contrastive self-supervised learning with false-negative can- cellation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.897909Z

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Observation 8ba960d9-bef8-473d-a465-ee63d680d0c3 · outbound

This paper cites MaeFE: Masked autoencoders family of electrocardiogram for self-supervised pre-training and transfer learning,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography MaeFE: Masked autoencoders family of electrocardiogram for self-supervised pre-training and transfer learning,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.882611Z

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Observation 0975a4c9-a973-4885-8d74-921c9b1f4e12 · outbound

This paper cites Masked Transformer for Electrocardiogram Classification.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Masked Transformer for Electrocardiogram Classification

Reference 43

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Observation 4b2afd83-58bb-44b2-a7bc-af684c98a132 · outbound

This paper cites REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography REBAR: Retrieval-Based Reconstruction for Time-series Contrastive Learning

Reference 44

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Observation d0cd86f7-c0f2-40b9-a5d5-35bf02a28d4c · outbound

This paper cites Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners

Reference 45

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Observation 137f9f64-f62f-4360-af53-86d212375b7d · outbound

This paper cites NeuroKit2: A python toolbox for neurophysiological signal processing,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography NeuroKit2: A python toolbox for neurophysiological signal processing,

Reference 46

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Observation 408e5744-78cf-4e3d-bc15-dc5fce459074 · outbound

This paper cites A real-time QRS detection algorithm,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography A real-time QRS detection algorithm,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.867343Z

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Observation b3f898ce-78a2-4c97-8ba0-8d481e8ea208 · outbound

This paper cites wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-04T21:20:46.474596Z

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Observation 27c4b378-2999-4073-b84a-b92fb0cb75d8 · outbound

This paper cites PhysioBank, Phys- ioToolkit and PhysioNet: Components of a new research resource for complex physiologic signals,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography PhysioBank, Phys- ioToolkit and PhysioNet: Components of a new research resource for complex physiologic signals,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.852528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3df8350a-3656-4c48-8650-7656e4d1160c · outbound

This paper cites MIMIC-IV-ECG: Diagnostic electrocardiogram matched subset (version 1.0),.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography MIMIC-IV-ECG: Diagnostic electrocardiogram matched subset (version 1.0),

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.836295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 407f0a4d-b7e4-4e6b-a2ac-e5687f94af10 · outbound

This paper cites Will two do? Varying dimensions in electrocardiography: The PhysioNet/Computing in Cardi- ology Challenge 2021,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Will two do? Varying dimensions in electrocardiography: The PhysioNet/Computing in Cardi- ology Challenge 2021,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.818799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f7e3679c-0b25-4841-a7ae-fd54f1917e0d · outbound

This paper cites Classification of 12- lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography Classification of 12- lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.801481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d9b00b61-05de-4417-bc32-56043a9c9a44 · outbound

This paper cites fairseq: A fast, extensible toolkit for sequence modeling,.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography fairseq: A fast, extensible toolkit for sequence modeling,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.783275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1e1205c1-165a-453e-bfa1-2ef4513f330a · outbound

This paper cites fairseq-signals: Self-supervised learning framework for biosig- nals (ECG, PPG),.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography fairseq-signals: Self-supervised learning framework for biosig- nals (ECG, PPG),

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.766153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6edd39e0-37c1-4331-8bf6-08d5db0d3f7b · outbound

This paper cites What makes for good views for contrastive learning?.

Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography What makes for good views for contrastive learning?

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T21:20:46.748124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Reference resolution

Outbound references

Pith citing papers