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Paper Citation Record · LEDGER

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining

As of 15 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2506.23259.

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

pith.paper-citation-record.v1
2506.23259 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:50:25.628652Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

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

13 of 13 outbound references displayed

  • verified exact2
  • verified fuzzy2
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 90c4b3e3-85b0-4bc8-8919-6da5bf908c8e · outbound

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

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining Deep learning for ecg arrhythmia detection and classification: an overview of progress for period 2017--2023

Reference 1

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T21:50:26.873187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T21:50:24.456873Z digest=sha256:8e31fe4e88ffaba5a7c38372e6b84da7424a0dbb8ff4232f781348f15537f1ed

Observation f84ab8c6-7fd1-41f5-bc84-41c8772a3a9c · outbound

This paper cites Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:24.547365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:24.547365Z digest=sha256:88dd850b6c39d41b701a83e8c55221f352e85836a0edf566339c8b3c036081c5

Observation 34f82be7-22a3-4e4e-a891-7ba124ae9380 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:24.628087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:24.628087Z digest=sha256:469c4c0910dcca6f8de2702753205c4c572d70561622d16ad45f5f4ef6605cb6

Observation 151f4a2f-4a60-43be-81e9-813d32cae46d · outbound

This paper cites J., Brammer, J.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining J., Brammer, J

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:24.762588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:24.762588Z digest=sha256:a12ea88f4bc7f99a8ceb43cee8118072647d2a4e9bdec2dbcacb5c9ddf6535f3

Observation 3fa560dd-7ddd-4020-a7f0-ab3e349de0f2 · outbound

This paper cites E., Clifford, G.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining E., Clifford, G

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:50:27.164561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T21:50:24.845435Z digest=sha256:454127d8fc41e2f5dc84a1f6a8d785cd2f790c74367b3baa63b224fb484d522a

Observation f2710c43-ef4f-47e2-87e3-474d63a7c6ec · outbound

This paper cites In silico evaluation of cell therapy in acute versus chronic infarction.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining In silico evaluation of cell therapy in acute versus chronic infarction

Reference 6

Resolution
verified exact
doi, observed 2026-08-06T21:50:26.103719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T21:50:24.990732Z digest=sha256:ba89e5763888488b136f73fe864a029e6515046ebdc29aceca05a1f4fbbd7015

Observation f853e7ae-4e19-49d1-92d0-1ed230f37039 · outbound

This paper cites Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies

Reference 7

Resolution
verified exact
doi, observed 2026-08-06T21:50:25.886453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T21:50:25.086463Z digest=sha256:2b2f2fa44920ebe98472f4a1585185a0e075bee084056b9bf03ca8162f6efba8

Observation 6b89e743-45cc-4616-b215-4563a055c174 · outbound

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

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining Deep learning for ecg analysis: Benchmarks and insights from ptb-xl

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:25.158496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:25.158496Z digest=sha256:851d50e852650ad6db6f4cf4a5557f629704214e237562b7c5a3452aae9f7b03

Observation aebeeee5-9012-4cb8-a85d-531c4fe73fe3 · outbound

This paper cites A multi-lead group network for myocardial infarction detection and localization based on clinical knowledge-driven and dynamic-static feature fusion.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining A multi-lead group network for myocardial infarction detection and localization based on clinical knowledge-driven and dynamic-static feature fusion

Reference 9

Resolution
metadata mismatch
raw_fallback, observed 2026-08-06T21:50:26.448429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T21:50:25.267994Z digest=sha256:5a236c13a709821aafae916c329f6d48bc5dcb13237e2dc7af15958f866757d3

Observation 224c42e1-8100-4596-9296-0eb20a1159f8 · outbound

This paper cites N., Kaiser, ., and Polosukhin, I.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining N., Kaiser, ., and Polosukhin, I

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:25.360825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:25.360825Z digest=sha256:51de8d036dfd608aa4fb7136057ff0667037b558f75962778e3a3a82187fc311

Observation 63ab0f4a-3659-4a0d-8744-5909fbd75f88 · outbound

This paper cites I., Samek, W., and Schaeffter, T.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining I., Samek, W., and Schaeffter, T

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:50:27.008031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T21:50:25.458975Z digest=sha256:66c36b3684a7f19561b3b9a9d00ac12bda910d7721058b67518930bf054d4ff3

Observation d41fa7ac-47d9-475d-ab90-65c85093e519 · outbound

This paper cites Masked Transformer for Electrocardiogram Classification.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining Masked Transformer for Electrocardiogram Classification

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:25.542613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:25.542613Z digest=sha256:1067bdab3ee3f03a219031a5ad7108200d240b8c6abcf770e8aec4ae5fa5cb9e

Observation fa68ff0d-c8d8-4944-84b6-acd1dfa4360a · outbound

This paper cites write newline.

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining write newline

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T21:50:25.628652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:50:25.628652Z digest=sha256:2eb7d6936f246b2cc61dcaca1894bb4ab0fe381d46078f69edd5861f1691b1c2

Pith citing papers

No inbound Pith citation observations are available.