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Paper Citation Record · LEDGER

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study

As of 13 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2412.16207.

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

pith.paper-citation-record.v1
2412.16207 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:26:31.633269Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

17 of 17 outbound references displayed

  • verified exact0
  • verified fuzzy9
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d7867629-1fd3-4781-a237-42de85e77d63 · outbound

This paper cites Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Reference 1

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no resolver link, observed 2026-08-11T13:26:31.574084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.574084Z digest=sha256:e743b486dc7f8fabbed6f101f1d23b0ef2038ef562dbd100d111fd7ecc6e0c72

Observation 3dab1816-3331-48c7-87be-b3ec5e554fab · outbound

This paper cites Synsiggan: Generative adversarial networks for synthetic biomedical signal generation,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Synsiggan: Generative adversarial networks for synthetic biomedical signal generation,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.828224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:26:31.578753Z digest=sha256:6778bff8125621dd97ce25e7118036a3d63c4307f4caa463451479ace33ace79

Observation 8bc4470e-eae7-4de3-8cae-3e8c46ddac71 · outbound

This paper cites CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study CorGAN: Correlation-Capturing Convolutional Generative Adversarial Networks for Generating Synthetic Healthcare Records

Reference 3

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unresolved
no resolver link, observed 2026-08-11T13:26:31.582200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.582200Z digest=sha256:fee35453c9dd9a337edaf5a2836b68333dc2443c681bb8d468a75af891ae3c7d

Observation 939f1f8c-2b75-49f8-b7b8-356cc8f97c16 · outbound

This paper cites Differentially private synthetic medical data generation using convolutional gans,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Differentially private synthetic medical data generation using convolutional gans,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.817056Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation d31b014c-697c-4a34-b978-24e747eff6f2 · outbound

This paper cites Dp-ctgan: Differentially pri- vate medical data generation using ctgans,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Dp-ctgan: Differentially pri- vate medical data generation using ctgans,

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.805008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:26:31.590305Z digest=sha256:18c39712ce28978241e1f3250d5094665458680a79eb13011ad05c2cc87dabb1

Observation 78c482ad-c210-4b9d-88bc-830bfbcd504b · outbound

This paper cites Data augmentation and the improvement of the performance of convolutional neural networks for heart sound classification,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Data augmentation and the improvement of the performance of convolutional neural networks for heart sound classification,

Reference 6

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.793783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:26:31.594091Z digest=sha256:5895f3dfef0361bd44446e1e9e89def59eb1cfad40921efff115aeb96ab7150e

Observation c3571804-71ec-4ce4-ac19-35a70a84b943 · outbound

This paper cites WaveNet: A Generative Model for Raw Audio.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study WaveNet: A Generative Model for Raw Audio

Reference 7

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unresolved
no resolver link, observed 2026-08-11T13:26:31.598140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.598140Z digest=sha256:caffb79ed069c9ad1787a6d452ef979d486977d1481fa1045097c3caaac8bb70

Observation bb0f608a-d41f-4e71-9082-593bd768bc50 · outbound

This paper cites Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions

Reference 8

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unresolved
no resolver link, observed 2026-08-11T13:26:31.602124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.602124Z digest=sha256:3954070de1e38f1bd5d754042414b9ebbdd7378447066bf827644711a4cddfe2

Observation f7d16037-fdc4-4217-9c3f-81d3fb8dfbe3 · outbound

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

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study DiffWave: A Versatile Diffusion Model for Audio Synthesis

Reference 9

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unresolved
no resolver link, observed 2026-08-11T13:26:31.605947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.605947Z digest=sha256:5e9e111f17e68be4d4d99317939030ee424a8cd137758f49f2cbd8fac434cf64

Observation 56a7704a-2d8f-407e-bd14-f3e43b7ec4e2 · outbound

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

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals,

Reference 10

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unresolved
no resolver link, observed 2026-08-11T13:26:31.609623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation c05e5db7-3307-4290-aaa1-211aed795063 · outbound

This paper cites The circor digiscope dataset: From murmur detection to murmur classification,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study The circor digiscope dataset: From murmur detection to murmur classification,

Reference 11

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.782508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:26:31.613095Z digest=sha256:9f6e0f082ee19719ba0c0b8877867cbb55febe2c3099bd46cb608b77b865383a

Observation 6064fd9c-1d9f-4e55-ba4f-87f899e792d4 · outbound

This paper cites Heart murmur detection from phonocardiogram recordings: The george b. moody physionet challenge 2022,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Heart murmur detection from phonocardiogram recordings: The george b. moody physionet challenge 2022,

Reference 12

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.771166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 71a07908-09f9-4f56-b7fa-798de352edf7 · outbound

This paper cites Analysis of pcg signals using quality assessment and homomorphic filters for localization and classification of heart sounds,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Analysis of pcg signals using quality assessment and homomorphic filters for localization and classification of heart sounds,

Reference 13

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.760510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 025b48bb-510b-4859-b118-78936c79042c · outbound

This paper cites an unresolved cited work.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Unresolved cited work

Reference 14

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unresolved
no resolver link, observed 2026-08-11T13:26:31.623190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.623190Z digest=sha256:5e5d4b77832452e57a33bba53349ab8cf57e95dd9d6b1962ec5d61ef00b68aea

Observation ae129ebc-680a-422b-925c-3ad71ff04756 · outbound

This paper cites SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python,

Reference 15

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unresolved
no resolver link, observed 2026-08-11T13:26:31.626494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T13:26:31.626494Z digest=sha256:a018e22fca59e1c486a9bd53b1b7847a72deed4c036cb4af2c538d3ebb26313d

Observation 915402c7-2a4e-401c-ac62-f3003da71ec2 · outbound

This paper cites Transfusion: Generating long, high fidelity time series using diffusion models with transformers,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Transfusion: Generating long, high fidelity time series using diffusion models with transformers,

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.737023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-11T13:26:31.629915Z digest=sha256:973e110363f2e211aff5007bd814005a010c75e5cc69fa4d26a1ab256767c7de

Observation edb8ab57-6474-4440-8f65-604a98e685bb · outbound

This paper cites Time-series generative ad- versarial networks,.

Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study Time-series generative ad- versarial networks,

Reference 17

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verified fuzzy
raw_fallback, observed 2026-08-11T13:26:31.724731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Pith citing papers

No inbound Pith citation observations are available.