Paper Citation Record · LEDGER

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 1 inbound Pith citation observation for arXiv:2501.10342.

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

pith.paper-citation-record.v1
2501.10342 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T19:16:41.807266Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:48:11.845671Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-04T08:19:44.625594Z

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 4d7818f5-840e-4775-9799-266b670504ad · outbound

This paper cites Accessed: September 9, 2024.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Accessed: September 9, 2024

Reference 1

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Observation 59c634db-0175-414c-920a-c9d6836fd63f · outbound

This paper cites Accessed: September 9, 2024.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Accessed: September 9, 2024

Reference 2

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Observation 112b3189-57f0-4ec1-a138-a653e52b2c0c · outbound

This paper cites Journal of Clinical Neurology (Seoul, Korea) 17(3), 393 (2021).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Journal of Clinical Neurology (Seoul, Korea) 17(3), 393 (2021)

Reference 3

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Observation cc26a13f-d194-4ae5-808f-7e8e2f2d84ef · outbound

This paper cites In: Artificial Intelligence in Medicine: 17th Con- ference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings 17, pp.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism In: Artificial Intelligence in Medicine: 17th Con- ference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings 17, pp

Reference 4

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Observation 5e8d1437-56d7-435d-8b35-d2870d42648d · outbound

This paper cites Neurocomputing 414, 90–100 (2020).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Neurocomputing 414, 90–100 (2020)

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.267865Z

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Observation 16263f67-848e-49e7-9752-13341e88108e · outbound

This paper cites Advances in neural information processing systems 32 (2019) 17.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Advances in neural information processing systems 32 (2019) 17

Reference 6

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Observation 1f9b6470-e3a9-4e69-aae3-8761e98d78a0 · outbound

This paper cites Archives of Computational Methods in Engineering 31(4), 2345–2384 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Archives of Computational Methods in Engineering 31(4), 2345–2384 (2024)

Reference 7

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Observation 87730722-b1c3-4960-8d96-38b233216f26 · outbound

This paper cites Neural Computing and Applications, 1–26 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Neural Computing and Applications, 1–26 (2024)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.218616Z

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Observation 36ea2475-3aa4-45c0-85e0-88432c73b86a · outbound

This paper cites Multimedia Tools and Applications, 1–23 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Multimedia Tools and Applications, 1–23 (2024)

Reference 10

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Observation e9a29748-bf1e-4f8f-8da5-92aeb6b70094 · outbound

This paper cites an unresolved cited work.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Unresolved cited work

Reference 11

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Observation 636edb1c-753f-4217-95f3-584935a8e504 · outbound

This paper cites Waves in Random and Complex Media, 1–27 (2023).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Waves in Random and Complex Media, 1–27 (2023)

Reference 12

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Observation eafa895e-ecf0-4858-8b0a-ebdf056621b1 · outbound

This paper cites Expert Systems with Applications 219, 119527 (2023).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Expert Systems with Applications 219, 119527 (2023)

Reference 13

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Observation 5478f2d9-92c0-4843-aa5a-7945f5dcc04c · outbound

This paper cites Applied Soft Computing 133, 109924 (2023).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Applied Soft Computing 133, 109924 (2023)

Reference 14

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Observation 30afd968-a5aa-464f-a0d6-0c78d530dd26 · outbound

This paper cites Frontiers in Human Neuroscience 18, 1319574 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Frontiers in Human Neuroscience 18, 1319574 (2024)

Reference 15

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Observation e6a471a8-248d-4dc9-8436-052f98e0ddfd · outbound

This paper cites IEEE Transactions on signal processing 44(9), 2163–2171 (1996).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism IEEE Transactions on signal processing 44(9), 2163–2171 (1996)

Reference 16

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Observation c50c5597-632a-41f9-a4d7-83a803254cac · outbound

This paper cites Signal Processing 214, 109258 (2024) 18.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Signal Processing 214, 109258 (2024) 18

Reference 17

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Observation d4379b7a-2839-467f-9bbd-e78c10684a1f · outbound

This paper cites Computers, Materials and Continua (2020).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Computers, Materials and Continua (2020)

Reference 18

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Observation 0a6c3a68-6cfb-4c28-b95e-ab52fd4e7241 · outbound

This paper cites circulation 101(23), 215–220 (2000).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism circulation 101(23), 215–220 (2000)

Reference 19

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Observation a481e2df-07fb-405d-87e2-a5b9ec3175e6 · outbound

This paper cites Biomedical signal processing and control 72, 103342 (2022).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Biomedical signal processing and control 72, 103342 (2022)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.096747Z

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Observation ab42bb18-9301-416c-a5ff-e336c5fdef6c · outbound

This paper cites Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology 16, 1–6 (2008).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Institute for knowledge discovery (laboratory of brain-computer interfaces), Graz University of Technology 16, 1–6 (2008)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.084369Z

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Observation 5f6f94cc-5d5a-4c86-a164-8a50246dec42 · outbound

This paper cites Signal, Image and Video Processing 18(2), 1577–1588 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Signal, Image and Video Processing 18(2), 1577–1588 (2024)

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.071820Z

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Observation 1f56c834-907b-4f4a-bf32-a00a59a9ef49 · outbound

This paper cites Physical Review E 64(6), 061907 (2001).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Physical Review E 64(6), 061907 (2001)

Reference 24

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 69222aa7-fddd-4627-9177-d1bd751f1bab · outbound

This paper cites In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp

Reference 25

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Observation ca308b52-9eb9-4a11-9d92-bbed9bf1351f · outbound

This paper cites Computational and Mathematical Methods in Medicine 2022(1), 7751263 (2022).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Computational and Mathematical Methods in Medicine 2022(1), 7751263 (2022)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.027548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e2f3792f-846f-4901-b0f6-7470be221260 · outbound

This paper cites In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism In: 2020 International Conference on Communication and Signal Processing (ICCSP), pp

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.015510Z

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Observation 79d9949c-68ed-4247-8190-944287c11093 · outbound

This paper cites Biology 11(8), 1220 (2022) 19.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Biology 11(8), 1220 (2022) 19

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.004821Z

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Observation 201444a7-e291-425c-9dba-d4f250001c9b · outbound

This paper cites Knowledge-Based Systems 265, 110372 (2023).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Knowledge-Based Systems 265, 110372 (2023)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.994936Z

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Observation e06f795c-48f4-4cdc-9d7c-61fcbbc36661 · outbound

This paper cites Pattern Recognition Letters 128, 544–550 (2019).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Pattern Recognition Letters 128, 544–550 (2019)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.984857Z

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Observation 8567131a-bc6e-451e-a4d1-516c5c923df2 · outbound

This paper cites an unresolved cited work.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-10T19:16:41.973007Z

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Observation aa94cd35-9532-4d64-8244-ecb85071b15d · outbound

This paper cites Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation

Reference 33

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Observation c9b8592d-3981-45ae-a975-cc93838dd09a · outbound

This paper cites Epilepsia 64(6), 1466–1468 (2023).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Epilepsia 64(6), 1466–1468 (2023)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.961516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 68cab3c6-4db4-49b5-98a4-620cdf2a7df0 · outbound

This paper cites BioData Mining 16(1), 4 (2023).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism BioData Mining 16(1), 4 (2023)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.950009Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8bd8ea2b-4007-412c-ae24-dd59982dd62a · outbound

This paper cites Journal of Engineering and Applied Science 71(1), 21 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Journal of Engineering and Applied Science 71(1), 21 (2024)

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.937800Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c87a6674-265b-41ac-8c50-04b9be56a173 · outbound

This paper cites Neural Computing and Applications 36(6), 2835–2852 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Neural Computing and Applications 36(6), 2835–2852 (2024)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.924823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8bd2707-dec9-400c-a4c6-a93e95bc0418 · outbound

This paper cites Brain Informatics 11(1), 21 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Brain Informatics 11(1), 21 (2024)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.913538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a5c23ac1-0800-447e-9bae-b8f46a998889 · outbound

This paper cites Computers In Biology And Medicine 148, 105931 (2022).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Computers In Biology And Medicine 148, 105931 (2022)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.902819Z

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Observation a44e645b-62f5-4883-ba4d-25ff02e03e14 · outbound

This paper cites Informatics in Medicine Unlocked21, 100444 (2020).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Informatics in Medicine Unlocked21, 100444 (2020)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.891327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5fc647d9-87a7-4bdf-854c-96bf926eff4b · outbound

This paper cites Engineering Proceedings 59(1), 166 (2024).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Engineering Proceedings 59(1), 166 (2024)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.879398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f42fe33b-a4fc-43e2-b008-d4ab7be26637 · outbound

This paper cites Journal of Investigations on Engineering and Technology 4(2), 47–60 (2021).

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Journal of Investigations on Engineering and Technology 4(2), 47–60 (2021)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:42.060881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3c80df52-66d0-4916-92f4-811300d8fc70 · outbound

This paper cites In: 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism In: 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.867202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c44ff836-baf9-49c7-ac12-c4f4b2130cb9 · outbound

This paper cites Multimedia Tools and Applications 83(8), 22119–22151 (2024) 21.

Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism Multimedia Tools and Applications 83(8), 22119–22151 (2024) 21

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T19:16:41.853852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation a33829a6-9fde-47a2-b494-a885c98fbc67 · inbound

From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks cites this paper.

From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism

Reference 110

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:19:44.626903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.