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Paper Citation Record · LEDGER

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification

As of 22 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2411.18456.

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pith.paper-citation-record.v1
2411.18456 v1

Coverage vector

measured 69 of 69 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:15:25.330100Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

69 of 69 outbound references displayed

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External citation measurements

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Outbound references

Observation 1d1481a9-1b43-4973-a158-df8e80aab391 · outbound

This paper cites A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications,

Reference 1

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Observation 7ca9078d-1e6d-4b1b-aef8-7d8b24298e45 · outbound

This paper cites Ethical Challenges Posed by Big Data,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Ethical Challenges Posed by Big Data,

Reference 2

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Observation 0036795b-ddd1-4273-9c93-3d87a317770f · outbound

This paper cites Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff,

Reference 3

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Observation 502a6d0c-5c65-4c82-8293-d1a2306dda4c · outbound

This paper cites Handling limited datasets with neural networks in medical appli- cations: A small-data approach,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Handling limited datasets with neural networks in medical appli- cations: A small-data approach,

Reference 4

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Observation 513cd5a7-221a-4baf-9b72-1ca6aa4ebefd · outbound

This paper cites Commentary: The Problem of Class Imbalance in Biomedical Data,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Commentary: The Problem of Class Imbalance in Biomedical Data,

Reference 5

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Observation bc448be0-3c62-4c44-8310-f99895b6e028 · outbound

This paper cites Data Augmentation techniques in time series domain: A survey and taxonomy.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Data Augmentation techniques in time series domain: A survey and taxonomy

Reference 6

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Observation b027d1fb-1e9b-471f-8b2e-64d5d5fbac29 · outbound

This paper cites On the Challenges and Opportunities in Generative AI,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification On the Challenges and Opportunities in Generative AI,

Reference 7

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Observation 59dab44c-cdfa-4225-aa6d-a4412d885ce0 · outbound

This paper cites Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models,

Reference 8

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Observation fc57ef72-3c24-49ae-b1ed-de04e7b853b9 · outbound

This paper cites A review on generative adversarial networks: Algorithms, the- ory, and applications,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification A review on generative adversarial networks: Algorithms, the- ory, and applications,

Reference 9

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Source-reported events for the cited work

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Observation 6f333de1-33fa-465b-9a7a-b48bcafe931a · outbound

This paper cites Autoregressive models: What are they good for?,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Autoregressive models: What are they good for?,

Reference 10

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Observation 86c25106-2841-48c3-bdbd-525d7c5df8ef · outbound

This paper cites Normalizing flows for probabilistic modeling and inference,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Normalizing flows for probabilistic modeling and inference,

Reference 11

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Observation 792196f0-5aeb-423e-9f17-83337839cae2 · outbound

This paper cites Diffusion models: A comprehensive survey of methods and applica- tions,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Diffusion models: A comprehensive survey of methods and applica- tions,

Reference 12

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Observation 4a8c98a1-1d61-46b8-90ed-ecaf4ab2e710 · outbound

This paper cites Auto-encoding variational bayes,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Auto-encoding variational bayes,

Reference 13

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Observation 1db30cf3-dc84-4437-a4f3-0126872d64fd · outbound

This paper cites Generative adversarial networks,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Generative adversarial networks,

Reference 14

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Observation b7c421a3-52ed-43c1-8ad1-4966e31cefbe · outbound

This paper cites Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state,

Reference 15

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Observation 25884f00-dded-4b61-8959-345c5df97306 · outbound

This paper cites Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction,

Reference 16

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Observation c95c5e77-b8cf-4f55-851c-8d328b883409 · outbound

This paper cites Time Series Prediction in Industry 4.0: A Com- prehensive Review and Prospects for Future Advancements,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Time Series Prediction in Industry 4.0: A Com- prehensive Review and Prospects for Future Advancements,

Reference 17

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Observation 9629d3c4-2603-493b-8c3d-ba6524717c6f · outbound

This paper cites Trend analysis of climate time series: A review of methods,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Trend analysis of climate time series: A review of methods,

Reference 18

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Observation b51624c7-959b-4342-aeb2-016bd9d7403a · outbound

This paper cites Characterizing parking systems from sensor data through a data-driven approach,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Characterizing parking systems from sensor data through a data-driven approach,

Reference 19

Resolution
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Observation b6c2c995-fb0c-448f-af67-c88a27a3b5b0 · outbound

This paper cites Electrocardiogram generation with a bidirectional LSTM- CNN generative adversarial network,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Electrocardiogram generation with a bidirectional LSTM- CNN generative adversarial network,

Reference 20

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Observation de0bc9a4-9a8c-4c30-9213-df786565bf51 · outbound

This paper cites Quick and Easy Time Series Generation with Established Image-based GANs.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Quick and Easy Time Series Generation with Established Image-based GANs

Reference 21

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Observation e05dc6c9-fc0d-4016-85e6-548098bf25a2 · outbound

This paper cites Synthesis of Realistic ECG using Generative Adversarial Networks.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Synthesis of Realistic ECG using Generative Adversarial Networks

Reference 22

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Observation c16226e8-6045-41a1-8d27-40f8757347db · outbound

This paper cites Pgans: Personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Pgans: Personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification,

Reference 23

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Observation 49dde406-a988-4606-bcd0-4f81dab0d716 · outbound

This paper cites ECG Arrhythmias Detection Using Auxiliary Classifier Gen- erative Adversarial Network and Residual Network,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification ECG Arrhythmias Detection Using Auxiliary Classifier Gen- erative Adversarial Network and Residual Network,

Reference 24

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Observation 2a684db2-3d85-4f84-8521-2961044230c9 · outbound

This paper cites Investigating Deep Convolution Conditional GANs for Electrocardio- gram Generation,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Investigating Deep Convolution Conditional GANs for Electrocardio- gram Generation,

Reference 25

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Observation 007401f5-65ea-4198-a79f-81fa1ecad8b6 · outbound

This paper cites ECG signal generation based on conditional generative models,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification ECG signal generation based on conditional generative models,

Reference 26

Resolution
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Source-reported events for the cited work

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Observation 546e35fb-4fed-4178-a891-6284457d4293 · outbound

This paper cites Synthetic ECG Signal Generation Using Proba- bilistic Diffusion Models,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Synthetic ECG Signal Generation Using Proba- bilistic Diffusion Models,

Reference 27

Resolution
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Source-reported events for the cited work

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Observation 5b4543e7-a896-4609-b155-14e9b4ec1c4e · outbound

This paper cites Synthesis of standard 12-lead electrocardiograms using two- dimensional generative adversarial networks,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Synthesis of standard 12-lead electrocardiograms using two- dimensional generative adversarial networks,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 81bf7c0e-1ed1-49d1-9b30-f3dd7512a5db · outbound

This paper cites PTB-XL, a large publicly available electrocardiography dataset,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification PTB-XL, a large publicly available electrocardiography dataset,

Reference 29

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 03f88967-058e-4749-94db-5989a73b8947 · outbound

This paper cites Chinese Cardiovascular Disease Database (CCDD) and Its Management Tool,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Chinese Cardiovascular Disease Database (CCDD) and Its Management Tool,

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 1ad539da-a262-4cbd-864d-f361eb4597ed · outbound

This paper cites A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,

Reference 31

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a82ae4ee-4397-46db-a297-6efbbf9ae8ba · outbound

This paper cites Deepfake electrocardiograms using generative adver- sarial networks are the beginning of the end for privacy issues in medicine,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Deepfake electrocardiograms using generative adver- sarial networks are the beginning of the end for privacy issues in medicine,

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation a4b41448-d6da-4070-bc3c-642b817ab0b6 · outbound

This paper cites Multivariate Generative Adversarial Networks and Their Loss Functions for Synthesis of Multichannel ECGs,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Multivariate Generative Adversarial Networks and Their Loss Functions for Synthesis of Multichannel ECGs,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.851593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.215230Z digest=sha256:ce9a9f763d9d39889774ad84cccefdd554e204fb8b2ca514753c1e66244679af

Observation ca490dc5-7add-46bd-b127-ba6667bb18fe · outbound

This paper cites TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification TTS-CGAN: A Transformer Time-Series Conditional GAN for Biosignal Data Augmentation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.222059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.222059Z digest=sha256:5d5bd46514d0a39ba30812a80c776e47d0639084cde2252995068b6475e22f1d

Observation 5ab7b65b-2f36-4400-a4fc-442c154bf128 · outbound

This paper cites Diffusion-based conditional ECG generation with structured state space models,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Diffusion-based conditional ECG generation with structured state space models,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.833980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.225740Z digest=sha256:31ca4ea2b5a414e4687f3719bcdfa844551fea1d7f1dfe118fb9fb4400b72e02

Observation 46266f9e-771a-41a7-94c3-8e1ebfe92da9 · outbound

This paper cites ECG Synthesis via Diffusion-Based State Space Augmented Trans- former,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification ECG Synthesis via Diffusion-Based State Space Augmented Trans- former,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.824789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.228837Z digest=sha256:a126874669ab53b0686d64195606901113f0a36464ddf672ec18f40a01e3c1c5

Observation 459d8dc4-1351-4f8c-822b-040d41fac275 · outbound

This paper cites Automatic classification of heartbeats using ECG mor- phology and heartbeat interval features,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Automatic classification of heartbeats using ECG mor- phology and heartbeat interval features,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.814587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.232019Z digest=sha256:11bc8d2d4173448baaf2a9d414932a05d3ab353dc7ed1f3a3eb9622b703f275e

Observation 5aec5c62-4547-45d1-9762-f0cf2a272535 · outbound

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

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Support vector machine-based expert system for reliable heartbeat recognition,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.805472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.235777Z digest=sha256:85162fb9bcf78489f6c1f64e413c9ffad8aadd3c5061282a18de4dcad5f78691

Observation 56c25a17-1356-4139-9326-bec20f944918 · outbound

This paper cites ECG arrhythmia classification based on optimum-path forest,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification ECG arrhythmia classification based on optimum-path forest,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.795349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.238853Z digest=sha256:7043e0802dbd0fd4f1e14257d5e5f4916b36c9c2bfbb3cf738dbc5c0bfac11ed

Observation d9978e31-5f8c-44a6-ac57-42191ca7c6f3 · outbound

This paper cites Electrocardiogram Classifica- tion Using Reservoir Computing With Logistic Regression,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Electrocardiogram Classifica- tion Using Reservoir Computing With Logistic Regression,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.785741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.241722Z digest=sha256:9a75af88db7764963a1f57ca011694a08d0b2127b2ef52d12fd2bf73627ea639

Observation 7a66719f-9b4a-4eb7-a07f-e8f692a33bfa · outbound

This paper cites Patient-Specific ECG Classification Based on Recurrent Neural Networks and Clustering Technique,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Patient-Specific ECG Classification Based on Recurrent Neural Networks and Clustering Technique,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.775379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.244733Z digest=sha256:d80bf077f2b9636cd46d27a33716cc5adcc8b14b5f57df5f63efa1e70f2f451b

Observation c7135eef-0e43-4462-be91-f341df4fcb57 · outbound

This paper cites Automated detection of atrial fibrillation using long short-term memory network with RR interval signals,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Automated detection of atrial fibrillation using long short-term memory network with RR interval signals,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.766272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.247742Z digest=sha256:0f46aac9f1d039094e68bdd7d8efb890b21d135df0c4f16ea0d6619e29797531

Observation 01cda072-b43e-4546-85d9-2f4fd593bff8 · outbound

This paper cites Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.757039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.251038Z digest=sha256:c48abb1c3376e016b5f15b9448139be563477bc63e297a0a2b4fda0e113c2cb6

Observation 739d76d8-37dd-468c-bbec-d343388e0402 · outbound

This paper cites A deep learning approach for ECG-based heartbeat classification for arrhythmia detection,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification A deep learning approach for ECG-based heartbeat classification for arrhythmia detection,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.747644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.254063Z digest=sha256:83f3cbe4c82107e46f1b59cca28e7f1bf1d6a9475256cb406081f655cf319f47

Observation e076c1bf-d70f-40d3-b097-c68d3c5b118a · outbound

This paper cites Automatic QRS complex detection using two-level convolutional neural network,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Automatic QRS complex detection using two-level convolutional neural network,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.737763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.257110Z digest=sha256:4aa7fcda2393eba4629f6b819c0f3de5f384744933a0bbd844b2b72a81a771f8

Observation f99875b1-f1d9-4f3f-ab43-9c6845692209 · outbound

This paper cites Automated arrhythmia classification based on a com- bination network of CNN and LSTM,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Automated arrhythmia classification based on a com- bination network of CNN and LSTM,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.728922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.260126Z digest=sha256:260a1dc0cf6ae7fe960164ae863debe4e1b2b9d647f8f7235b27c305952711ff

Observation e838b178-f51c-4871-862d-e1f065892389 · outbound

This paper cites DeepArrNet: An Efficient Deep CNN Architecture for Au- tomatic Arrhythmia Detection and Classification From Denoised ECG Beats,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification DeepArrNet: An Efficient Deep CNN Architecture for Au- tomatic Arrhythmia Detection and Classification From Denoised ECG Beats,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.719527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.264306Z digest=sha256:b5527cbfffde4ce1e328a1bc3fe90e547bdec65fd2267d5e06785e898fcf8d6a

Observation 9723d54a-11e1-4bc8-bfec-0426b0ac48db · outbound

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

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.267463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.267463Z digest=sha256:4ce69b8fabeb059eea220f1ea9918a742401e827c9c592c1043d8f15c81ec766

Observation 8d0b60a7-d4c7-4cdd-b95e-df8a622446e6 · outbound

This paper cites The impact of the MIT-BIH Arrhythmia Database,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification The impact of the MIT-BIH Arrhythmia Database,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.710211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.270667Z digest=sha256:ae85332a84418b7f274289ae0a1cf422c7797331dd48a7f87b93cabe8c2ca990

Observation d8b0991a-a737-4c4a-8b20-485bbc716910 · outbound

This paper cites An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification An Open Access Database for Evaluating the Algorithms of Electrocardiogram Rhythm and Morphology Abnormality Detection,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.700557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.273610Z digest=sha256:7c514074f1ae43ca5f4953dc75921a8e34c2964f731ea78c85c12d7d9fed0f26

Observation 601099ce-8e15-4c79-a91f-4ce4ed4d71bc · outbound

This paper cites Deep Learning- Based ECG Arrhythmia Classification: A Systematic Review,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Deep Learning- Based ECG Arrhythmia Classification: A Systematic Review,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.691093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.277211Z digest=sha256:e96043bd7af5155cff2e67ed2f91fa5fd55be0f5f306ee986dc746e4c147f070

Observation 11bf6d7e-3f06-4048-8af8-c671b295b71f · outbound

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

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Deep learning for ECG Arrhythmia detection and classification: an overview of progress for period 2017–2023,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.671405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.283768Z digest=sha256:a97c5efd861a52c0241f63e1bd1c9d405b082466f76fab66ae68fba684b0a402

Observation 7730a633-ac80-4c68-83d1-166e0dbe33be · outbound

This paper cites A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.660850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.286922Z digest=sha256:de48bfd12bb54748c0e9450a06d49b24b40f831a1ab266ef379da30b7c67e454

Observation 300f0284-42ca-4ba0-96a9-5e59fbc3cecc · outbound

This paper cites an unresolved cited work.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Unresolved cited work

Reference 54

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:15:25.681314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.280405Z digest=sha256:356794bc2e5667ff6d120bfbb11fb51968500e873b7f9f2fffedf053922257e7

Observation 91d45994-1f57-4a86-805f-c6edce685fd1 · outbound

This paper cites Diffusion-TS: Interpretable Diffusion for General Time Series Generation.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Diffusion-TS: Interpretable Diffusion for General Time Series Generation

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.293913Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.293913Z digest=sha256:7bda5f0820595509b1f36766b6a8f2bf836cb2542bfc5f2bab2fccce7d24cea9

Observation a4fc29da-c707-459d-899b-926b3bb1263b · outbound

This paper cites U-net: Convolutional networks for biomedical image segmen- tation,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification U-net: Convolutional networks for biomedical image segmen- tation,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.651063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.297390Z digest=sha256:25e81a4137e66fdb96ee15b575e0167fbde02791e079238808ac21673ea56684

Observation 46a7afba-be16-4027-b691-e0933782f472 · outbound

This paper cites DiffWave: A Versatile Diffusion Model for Audio Synthesis.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.289734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.289734Z digest=sha256:13635f1116e0e6631ac1b9243099720c12194820c401f7880c1f299abcf9e002

Observation ab4b2490-cdfa-4eb3-b8af-3b9694ee5a63 · outbound

This paper cites Neural discrete representation learning,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Neural discrete representation learning,

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.630528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.303707Z digest=sha256:429a50049006a70cd51cf2c4e53eefb89a5c27719ee29dc38fe7c660aedd793c

Observation 8d853d98-288e-48ec-8875-964d0529a6f6 · outbound

This paper cites Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.306737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.306737Z digest=sha256:96834716452ee1ae19dd20226f546ef951198d12984ec65d8e89cac6596092a1

Observation 40c5ffd6-eb05-4d94-a570-d6bbbe2d1d27 · outbound

This paper cites Vector quantized time series generation with a bidirectional prior model,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Vector quantized time series generation with a bidirectional prior model,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.640411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.300514Z digest=sha256:5687b7e729c007dc715392be5989f9c39dd12e16a459754c4080dcf088792f3f

Observation 0df2591b-691c-4cd5-ab19-38e8086f8598 · outbound

This paper cites GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilib- rium,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilib- rium,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.611665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.314150Z digest=sha256:69da8e064df2aebc7a0798048f401aa6244e7337f68c078d73f36c0578137e43

Observation deb5982e-a5ae-463e-b36b-1bd23f8c707c · outbound

This paper cites MMD GAN: Towards Deeper Understanding of Moment Matching Network,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification MMD GAN: Towards Deeper Understanding of Moment Matching Network,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.601272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.317217Z digest=sha256:6a1fa0d1dfa1085c4240fc7ff9b283e2b56067cb90103bdf2fb7d308ea0fd9d3

Observation af3ae170-91df-46a1-b7ff-9224f1a8b223 · outbound

This paper cites Optuna: A next-generation hyperparameter op- timization framework,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Optuna: A next-generation hyperparameter op- timization framework,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.621101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.311053Z digest=sha256:6cc346b035411cfb8b8c9740274edbaa321dd47f74d94659164fcc69f509e618

Observation 2a2b5b1f-7522-49b1-b369-25d516089577 · outbound

This paper cites Umap: Uniform manifold approximation and pro- jection,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Umap: Uniform manifold approximation and pro- jection,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.585973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.323454Z digest=sha256:262e9ff6b814c2df25f55e8b43c477a84e24ddbd72f233c05253a840206e8f56

Observation 6ff6b744-7687-4159-82e7-71272d39d51b · outbound

This paper cites Understanding how dimension reduction tools work: An empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Understanding how dimension reduction tools work: An empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization,

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.326857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.326857Z digest=sha256:cfc2687e6c6e6c6942765b5e1579047cd23e2e047ee229dc81cde0586fadc36c

Observation 70c18a6f-13a1-4b84-93d6-8f1d82b01904 · outbound

This paper cites Visualizing data using t-sne,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Visualizing data using t-sne,

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.320256Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.320256Z digest=sha256:b4ff4fc99fec99e0bfecfa88707dc7710ec60a1676cb3b28adaa90710417e571

Observation e47bc722-d857-4cf0-84c7-2df2f1d79255 · outbound

This paper cites Monitoring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews,.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Monitoring ai-modified content at scale: A case study on the impact of chatgpt on ai conference peer reviews,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T11:15:25.570007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.330100Z digest=sha256:589be2fc1a86c36ef833dbbdc5183e2ba444defac49f867dc7a2fee6fcdc05db

Observation 09939c96-7909-40e9-be0e-b1b836e33f2b · outbound

This paper cites an unresolved cited work.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Unresolved cited work

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-12T11:15:25.218929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:15:25.218929Z digest=sha256:617a210e864404d1f57b34cf78f9fce88a0de7cbfe05f4b886c4a7fa39160ef0

Observation 0f8c8c28-4284-47ee-951c-a52d42111930 · outbound

This paper cites an unresolved cited work.

Synthetic ECG Generation for Data Augmentation and Transfer Learning in Arrhythmia Classification Unresolved cited work

Reference 2023

Resolution
unresolved
raw_fallback, observed 2026-08-12T11:15:25.978415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-12T11:15:25.164205Z digest=sha256:62725e167ad033c95f4c48aeaf0c60408e9a263ad936372d1016c9b4a22f744b

Pith citing papers

No inbound Pith citation observations are available.