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Paper Citation Record · LEDGER

UniECG: Understanding and Generating ECG in One Unified Model

As of 9 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 2026-08-09T06:31:02.800959+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=sha256:1c6e25f6afb88b3b792610c6a99ed6ab57329b6267e392ca4b96421ac8242b7c

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=sha256:879197ff769173727b5a827cd5cbecfdf871ae132cec23d594909fff6294ed8e

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:d6d214fbb9e696ffefb55a2837752e93c60f96dc5f58b8461c7f135903b52665

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=sha256:4ff802d1720da688c1d05510d029a764a3cbcf5f3a16e6c68bc5bb00ee6247f0

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:b9f618121d0c98d1949b6c404674a845eba43a8ea5e08dddec14ce8a78babd2e

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:b6ab35cbb91c1534c5a285d297809ef3cb6cea0abc6abace57371c1574fab13a

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:bd6356d4197dea57cce68ded6c0c3544c4208639930e8bc3b055d7b27a9e2242

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:af17bb2468f11caf9b494411bdb6b484a377232c3c87319462f4229a4eb3e6a7

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:a72a662906ecc73915b1654734a6b657be8d9b82d0304e7f7a4ffb8df565aca4

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=sha256:90006dec91469eac3727cc1ec80e12448464aac56156b8d05414ddfed602a0ce

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=sha256:80fb30586fd46aced596c2766ca7f02c6905f41eb9c2ac9a12bb09a940a642ad

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:d6f1c1702cc5e13505bec49cfaafa550c0e6e045925ddbb575f5c372f41b84cd

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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source=pdf_text observed=2026-08-04T15:45:10.083614Z digest=sha256:f2e9daef7677e35b2a0efb602f4caae4b698cc9ef872db597cb3eed142266234

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=sha256:6db7b3d44f542e172ff350bd52eebfb24d958199a2fedf1352279a1732d83c42

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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source=pdf_text observed=2026-08-04T15:45:10.089551Z digest=sha256:e96f14b4db349295ec26837a5cfb3fc45c88cd19de0940d03da187f8a661a26e

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=sha256:4400ca8ba3d5b66744ce433eb53be7d50580cf7ca9ab50eb37b1d27e6ec4cdd5

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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source=pdf_text observed=2026-08-04T15:45:10.095832Z digest=sha256:a298f58e04bce8a3cddc4153ed78744520055317f8b84d1fc7e1f7c2e6ad9fb3

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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source=pdf_text observed=2026-08-04T15:45:10.098497Z digest=sha256:bc2acb57da73a91d5c8d14677a1c7883d7090c876d32edfd2d697e84e6b9373f

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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source=pdf_text observed=2026-08-04T15:45:10.101284Z digest=sha256:ae5e673822767c76d5daaf5ab94f3c46a13b996a89cc8a7302a360fe1ee8fe03

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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source=pdf_text observed=2026-08-04T15:45:10.103944Z digest=sha256:916a7946e0dc02870181eff106c879624411d6f8e5d255e7d48d9dd569276766

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=sha256:126bac4d6c130774d777c4cb0abecb5a7d59a07e3180ed928656ba19e2b044a8

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:c44cb413fcdb4f37e2d17ce6cd97b49e671cca2037d2f0aaba9f4e708a380067