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Paper Citation Record · LEDGER

UniECG: Understanding and Generating ECG in One Unified Model

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 3 inbound Pith citation observations for arXiv:2509.18588.

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

pith.paper-citation-record.v1
2509.18588 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:45:10.106466Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T15:45:10.044452Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:38:19.226060Z

Reference resolution

22 of 22 outbound references displayed

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External citation measurements

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Outbound references

Observation d11457bb-9206-41ee-9dc0-ad81c4e044ff · outbound

This paper cites UniECG: Understanding and Generating ECG in One Unified Model.

UniECG: Understanding and Generating ECG in One Unified Model UniECG: Understanding and Generating ECG in One Unified Model

Reference 1

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source=pdf_text observed=2026-08-04T15:45:10.044452Z digest=sha256:352d51c427d31b4bdd6ea74ff10a9729e13cdfbcfb6016940c0482a092289e5e

Observation 3dd25672-bf74-496d-9a0f-c469d06158b9 · outbound

This paper cites Generate an ECG of.

UniECG: Understanding and Generating ECG in One Unified Model Generate an ECG of

Reference 2

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source=pdf_text observed=2026-08-04T15:45:10.048746Z digest=sha256:5e5d14fc033d28c972741d418c514734e6796f0574325dd00fe05f677388702d

Observation 295a5c01-887e-4756-803e-44bb5b20eeb5 · outbound

This paper cites Datasets We primarily employ the ECG-Grounding and MIMIC-IV- ECG datasets for training and validation in UniECG under- standing and ECG generation tasks.

UniECG: Understanding and Generating ECG in One Unified Model Datasets We primarily employ the ECG-Grounding and MIMIC-IV- ECG datasets for training and validation in UniECG under- standing and ECG generation tasks

Reference 3

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source=pdf_text observed=2026-08-04T15:45:10.051727Z digest=sha256:56d4ad8f01f089e4b1901ffb64fa6bf7c18e04fc4148c40951dc82f35e7f8b6d

Observation 2ea1bae5-c26c-4aca-955c-557fe6a53d41 · outbound

This paper cites an unresolved cited work.

UniECG: Understanding and Generating ECG in One Unified Model Unresolved cited work

Reference 4

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source=pdf_text observed=2026-08-04T15:45:10.054896Z digest=sha256:4dc92eb71671be36fdc45b69c0fd59f64a7d76be06309788d2ca94586932f6fd

Observation f865a8c3-04f6-4002-a378-42184ec8c91a · outbound

This paper cites Opportunities and challenges of deep learning methods for electrocardiogram data: A system- atic review,.

UniECG: Understanding and Generating ECG in One Unified Model Opportunities and challenges of deep learning methods for electrocardiogram data: A system- atic review,

Reference 5

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source=pdf_text observed=2026-08-04T15:45:10.058241Z digest=sha256:3bdcd102ed8798b57314069da041b56556e2d0c61168e830b19d0a6b30df6014

Observation 5b864629-2421-4b1d-905f-f1c0dafd4da3 · outbound

This paper cites Deep learning in ECG diagnosis: A review,.

UniECG: Understanding and Generating ECG in One Unified Model Deep learning in ECG diagnosis: A review,

Reference 6

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source=pdf_text observed=2026-08-04T15:45:10.061035Z digest=sha256:a23a36d425abd0dca4860ecd7a51c6be709d106cb85b6109c82218e8c181c1a3

Observation 9faef4dd-30ae-4acb-a1ad-f58d0cd3799f · outbound

This paper cites Screening for cardiac contrac- tile dysfunction using an artificial intelligence–enabled electrocardiogram,.

UniECG: Understanding and Generating ECG in One Unified Model Screening for cardiac contrac- tile dysfunction using an artificial intelligence–enabled electrocardiogram,

Reference 7

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source=pdf_text observed=2026-08-04T15:45:10.063786Z digest=sha256:f8ad8a8710e909097842f078724da3ba15cc509f534304bd3eab0ec54e4fc94d

Observation 48f28100-67c0-4ae0-8ab5-b869b9da2062 · outbound

This paper cites EN- CASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks,.

UniECG: Understanding and Generating ECG in One Unified Model EN- CASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks,

Reference 8

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source=pdf_text observed=2026-08-04T15:45:10.066414Z digest=sha256:3527b8d534fb1774404a551e3390b5c1d7d0c5185e5240d5214fde818a9dbc18

Observation 7081a83c-aa9c-43c2-82bf-206171c464e5 · outbound

This paper cites MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals,.

UniECG: Understanding and Generating ECG in One Unified Model MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals,

Reference 9

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source=pdf_text observed=2026-08-04T15:45:10.068950Z digest=sha256:3b3ceaae6c4efda1545cf210478158e0f1c01c7de16855415c03b820fc346be2

Observation 20e38cff-19cf-4137-ad0d-a3ee28b9d9a3 · outbound

This paper cites Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model.

UniECG: Understanding and Generating ECG in One Unified Model Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model

Reference 10

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source=pdf_text observed=2026-08-04T15:45:10.072023Z digest=sha256:44f074f1f01b767edf1655dea06161e7548523185214ba1b15c16e4ca39bb577

Observation 9edf05cc-f230-4ce6-851d-d1512a555f4c · outbound

This paper cites Synthetic ECG signals gener- ation: A scoping review,.

UniECG: Understanding and Generating ECG in One Unified Model Synthetic ECG signals gener- ation: A scoping review,

Reference 11

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source=pdf_text observed=2026-08-04T15:45:10.074867Z digest=sha256:81e464b3be9ed2bdf05a27b068942da9d9cdbc8e881cf589c6e14df0961e81f5

Observation b4cb4b2e-25f3-4991-92ed-3673b1e22574 · outbound

This paper cites Region-Disentangled Diffusion Model for High- Fidelity PPG-to-ECG Translation,.

UniECG: Understanding and Generating ECG in One Unified Model Region-Disentangled Diffusion Model for High- Fidelity PPG-to-ECG Translation,

Reference 12

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Observation 3b5c96b3-bb7d-4b09-90a9-b4813f18c66c · outbound

This paper cites ECGTwin: Person- alized ECG Generation Using Controllable Diffusion Model,.

UniECG: Understanding and Generating ECG in One Unified Model ECGTwin: Person- alized ECG Generation Using Controllable Diffusion Model,

Reference 13

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source=pdf_text observed=2026-08-04T15:45:10.080674Z digest=sha256:2955564c3496a52f157c12a9101150e9ad45bdc8f18eb3a2973aca4f36655e4f

Observation 95f9211b-0acd-4469-8a91-7ef9305b2405 · outbound

This paper cites Generating Images with Multimodal Language Models.

UniECG: Understanding and Generating ECG in One Unified Model Generating Images with Multimodal Language Models

Reference 14

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Observation 94b9f1ac-59f5-48b0-99f9-c221d39aee55 · outbound

This paper cites Making LLaMA SEE and Draw with SEED Tokenizer.

UniECG: Understanding and Generating ECG in One Unified Model Making LLaMA SEE and Draw with SEED Tokenizer

Reference 15

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source=pdf_text observed=2026-08-04T15:45:10.086435Z digest=sha256:d7209f0d84b646287dc7db2af84fd44cd707f686364a09e6f3665e9a3f7dc8ed

Observation 4300a3c9-91e1-43ea-9e5e-8882418a9869 · outbound

This paper cites WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation.

UniECG: Understanding and Generating ECG in One Unified Model WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation

Reference 16

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Observation 0d6eb9b2-40d7-4581-8211-ff769973cd55 · outbound

This paper cites BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset.

UniECG: Understanding and Generating ECG in One Unified Model BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset

Reference 17

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source=pdf_text observed=2026-08-04T15:45:10.092799Z digest=sha256:0a96e48fd3d92105b1c24fe75c01dfa1032f694b413d43c12521dd59ee9a699c

Observation 57fd0049-7678-4875-b7fe-06907cf61f79 · outbound

This paper cites Teach Multimodal LLMs to Comprehend Electrocardiographic Images.

UniECG: Understanding and Generating ECG in One Unified Model Teach Multimodal LLMs to Comprehend Electrocardiographic Images

Reference 18

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Observation 81aa09d4-c2e9-4f89-b299-15297a8e8a4c · outbound

This paper cites ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis.

UniECG: Understanding and Generating ECG in One Unified Model ECG-Chat: A Large ECG-Language Model for Cardiac Disease Diagnosis

Reference 19

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Observation 58522139-7d3d-4fd7-9a3b-7fa79048dd11 · outbound

This paper cites GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images,.

UniECG: Understanding and Generating ECG in One Unified Model GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images,

Reference 20

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Observation 93a31899-580c-44c3-afe9-5f8b22f7d3ff · outbound

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

UniECG: Understanding and Generating ECG in One Unified Model DiffuSETS: 12-Lead ECG generation conditioned on clinical text reports and patient-specific information,

Reference 21

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Observation b4769b3d-641d-4ba0-a33c-0cb2e6028f35 · outbound

This paper cites MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset,.

UniECG: Understanding and Generating ECG in One Unified Model MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset,

Reference 22

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Pith citing papers

Observation d11457bb-9206-41ee-9dc0-ad81c4e044ff · inbound

UniECG: Understanding and Generating ECG in One Unified Model cites this paper.

UniECG: Understanding and Generating ECG in One Unified Model UniECG: Understanding and Generating ECG in One Unified Model

Reference 1

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Observation 68f0a91c-0dee-40ed-a57b-c99ac2814977 · inbound

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? cites this paper.

R1-SyntheticVL: Is Synthetic Data from Generative Models Ready for Multimodal Large Language Model? UniECG: Understanding and Generating ECG in One Unified Model

Reference 9

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source=pdf_text observed=2026-08-03T05:06:44.804933Z digest=sha256:2246fbc8bcc462e510a19419db1624a175fc0535dbe437ea79ae1b0d0b7bfb6e

Observation 84c1ed4d-aa56-4c6c-a2a8-0b460bd67220 · inbound

Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification cites this paper.

Reasoning Before Diagnosis: Physician-Inspired Structured Thinking for ECG Classification UniECG: Understanding and Generating ECG in One Unified Model

Reference 11

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source=pdf_text observed=2026-05-20T13:36:24.853780Z digest=sha256:57e19b66aa98a5da9b86b0f2130fd2ef67fe3d91c04748c42cfb0bc5dbe1d398