Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer Article Swipe
Qinyu Chen
,
Congyi Sun
,
Chang Gao
,
Shih‐Chii Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.09424
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2402.09424
Epilepsy is a common disease of the nervous system. Timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. This paper presents a neuromorphic Spiking Convolutional Transformer, named Spiking Conformer, to detect and predict epileptic seizure segments from scalped long-term electroencephalogram (EEG) recordings. We report evaluation results from the Spiking Conformer model using the Boston Children's Hospital-MIT (CHB-MIT) EEG dataset. By leveraging spike-based addition operations, the Spiking Conformer significantly reduces the classification computational cost compared to the non-spiking model. Additionally, we introduce an approximate spiking neuron layer to further reduce spike-triggered neuron updates by nearly 38% without sacrificing accuracy. Using raw EEG data as input, the proposed Spiking Conformer achieved an average sensitivity rate of 94.9% and a specificity rate of 99.3% for the seizure detection task, and 96.8%, 89.5% for the seizure prediction task, and needs >10x fewer operations compared to the non-spiking equivalent model.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2402.09424
- https://arxiv.org/pdf/2402.09424
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391912465
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4391912465Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2402.09424Digital Object Identifier
- Title
-
Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional TransformerWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-21Full publication date if available
- Authors
-
Qinyu Chen, Congyi Sun, Chang Gao, Shih‐Chii LiuList of authors in order
- Landing page
-
https://arxiv.org/abs/2402.09424Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2402.09424Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2402.09424Direct OA link when available
- Concepts
-
Epilepsy, Transformer, Computer science, Artificial intelligence, Pattern recognition (psychology), Neuroscience, Electrical engineering, Psychology, Engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4391912465 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2402.09424 |
| ids.doi | https://doi.org/10.48550/arxiv.2402.09424 |
| ids.openalex | https://openalex.org/W4391912465 |
| fwci | 0.0 |
| type | preprint |
| title | Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10429 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | EEG and Brain-Computer Interfaces |
| topics[1].id | https://openalex.org/T10502 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9945999979972839 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Advanced Memory and Neural Computing |
| topics[2].id | https://openalex.org/T10241 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9839000105857849 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | Functional Brain Connectivity Studies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2778186239 |
| concepts[0].level | 2 |
| concepts[0].score | 0.5998067855834961 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q41571 |
| concepts[0].display_name | Epilepsy |
| concepts[1].id | https://openalex.org/C66322947 |
| concepts[1].level | 3 |
| concepts[1].score | 0.5053227543830872 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q11658 |
| concepts[1].display_name | Transformer |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.4998202323913574 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.3778928518295288 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C153180895 |
| concepts[4].level | 2 |
| concepts[4].score | 0.34315985441207886 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[4].display_name | Pattern recognition (psychology) |
| concepts[5].id | https://openalex.org/C169760540 |
| concepts[5].level | 1 |
| concepts[5].score | 0.13897794485092163 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[5].display_name | Neuroscience |
| concepts[6].id | https://openalex.org/C119599485 |
| concepts[6].level | 1 |
| concepts[6].score | 0.13341552019119263 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[6].display_name | Electrical engineering |
| concepts[7].id | https://openalex.org/C15744967 |
| concepts[7].level | 0 |
| concepts[7].score | 0.12166526913642883 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[7].display_name | Psychology |
| concepts[8].id | https://openalex.org/C127413603 |
| concepts[8].level | 0 |
| concepts[8].score | 0.10216805338859558 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[8].display_name | Engineering |
| concepts[9].id | https://openalex.org/C165801399 |
| concepts[9].level | 2 |
| concepts[9].score | 0.09309068322181702 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q25428 |
| concepts[9].display_name | Voltage |
| keywords[0].id | https://openalex.org/keywords/epilepsy |
| keywords[0].score | 0.5998067855834961 |
| keywords[0].display_name | Epilepsy |
| keywords[1].id | https://openalex.org/keywords/transformer |
| keywords[1].score | 0.5053227543830872 |
| keywords[1].display_name | Transformer |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.4998202323913574 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.3778928518295288 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/pattern-recognition |
| keywords[4].score | 0.34315985441207886 |
| keywords[4].display_name | Pattern recognition (psychology) |
| keywords[5].id | https://openalex.org/keywords/neuroscience |
| keywords[5].score | 0.13897794485092163 |
| keywords[5].display_name | Neuroscience |
| keywords[6].id | https://openalex.org/keywords/electrical-engineering |
| keywords[6].score | 0.13341552019119263 |
| keywords[6].display_name | Electrical engineering |
| keywords[7].id | https://openalex.org/keywords/psychology |
| keywords[7].score | 0.12166526913642883 |
| keywords[7].display_name | Psychology |
| keywords[8].id | https://openalex.org/keywords/engineering |
| keywords[8].score | 0.10216805338859558 |
| keywords[8].display_name | Engineering |
| keywords[9].id | https://openalex.org/keywords/voltage |
| keywords[9].score | 0.09309068322181702 |
| keywords[9].display_name | Voltage |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2402.09424 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2402.09424 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2402.09424 |
| locations[1].id | doi:10.48550/arxiv.2402.09424 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article-journal |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2402.09424 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5041745327 |
| authorships[0].author.orcid | https://orcid.org/0009-0005-9480-6164 |
| authorships[0].author.display_name | Qinyu Chen |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chen, Qinyu |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5079049469 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5826-9040 |
| authorships[1].author.display_name | Congyi Sun |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sun, Congyi |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5037066563 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-9037-1371 |
| authorships[2].author.display_name | Chang Gao |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gao, Chang |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5053821067 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-7557-045X |
| authorships[3].author.display_name | Shih‐Chii Liu |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Liu, Shih-Chii |
| authorships[3].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2402.09424 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Epilepsy Seizure Detection and Prediction using an Approximate Spiking Convolutional Transformer |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10429 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | EEG and Brain-Computer Interfaces |
| related_works | https://openalex.org/W2748952813, https://openalex.org/W2395385109, https://openalex.org/W2773633178, https://openalex.org/W2802335767, https://openalex.org/W2080101436, https://openalex.org/W4388090985, https://openalex.org/W2356350882, https://openalex.org/W2390279801, https://openalex.org/W2033914206, https://openalex.org/W2042327336 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2402.09424 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2402.09424 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2402.09424 |
| primary_location.id | pmh:oai:arXiv.org:2402.09424 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2402.09424 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2402.09424 |
| publication_date | 2024-01-21 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 2, 35, 130 |
| abstract_inverted_index.By | 73 |
| abstract_inverted_index.We | 56 |
| abstract_inverted_index.an | 95, 123 |
| abstract_inverted_index.as | 116 |
| abstract_inverted_index.by | 106 |
| abstract_inverted_index.is | 1 |
| abstract_inverted_index.of | 5, 11, 22, 30, 127, 133 |
| abstract_inverted_index.to | 43, 88, 100, 154 |
| abstract_inverted_index.we | 93 |
| abstract_inverted_index.38% | 108 |
| abstract_inverted_index.EEG | 71, 114 |
| abstract_inverted_index.and | 13, 24, 28, 45, 129, 140, 148 |
| abstract_inverted_index.can | 16 |
| abstract_inverted_index.for | 135, 143 |
| abstract_inverted_index.raw | 113 |
| abstract_inverted_index.the | 6, 19, 26, 61, 66, 78, 83, 89, 118, 136, 144, 155 |
| abstract_inverted_index.This | 32 |
| abstract_inverted_index.cost | 86 |
| abstract_inverted_index.data | 115 |
| abstract_inverted_index.from | 50, 60 |
| abstract_inverted_index.life | 27 |
| abstract_inverted_index.rate | 126, 132 |
| abstract_inverted_index.(EEG) | 54 |
| abstract_inverted_index.89.5% | 142 |
| abstract_inverted_index.94.9% | 128 |
| abstract_inverted_index.99.3% | 134 |
| abstract_inverted_index.Using | 112 |
| abstract_inverted_index.fewer | 151 |
| abstract_inverted_index.layer | 99 |
| abstract_inverted_index.model | 64 |
| abstract_inverted_index.named | 40 |
| abstract_inverted_index.needs | 149 |
| abstract_inverted_index.paper | 33 |
| abstract_inverted_index.task, | 139, 147 |
| abstract_inverted_index.using | 65 |
| abstract_inverted_index.96.8%, | 141 |
| abstract_inverted_index.Boston | 67 |
| abstract_inverted_index.Timely | 9 |
| abstract_inverted_index.common | 3 |
| abstract_inverted_index.detect | 44 |
| abstract_inverted_index.health | 29 |
| abstract_inverted_index.injury | 21 |
| abstract_inverted_index.input, | 117 |
| abstract_inverted_index.model. | 91, 158 |
| abstract_inverted_index.nearly | 107 |
| abstract_inverted_index.neuron | 98, 104 |
| abstract_inverted_index.reduce | 18, 102 |
| abstract_inverted_index.report | 57 |
| abstract_inverted_index.>10x | 150 |
| abstract_inverted_index.Spiking | 37, 41, 62, 79, 120 |
| abstract_inverted_index.average | 124 |
| abstract_inverted_index.disease | 4 |
| abstract_inverted_index.further | 101 |
| abstract_inverted_index.nervous | 7 |
| abstract_inverted_index.predict | 46 |
| abstract_inverted_index.protect | 25 |
| abstract_inverted_index.reduces | 82 |
| abstract_inverted_index.results | 59 |
| abstract_inverted_index.scalped | 51 |
| abstract_inverted_index.seizure | 48, 137, 145 |
| abstract_inverted_index.spiking | 97 |
| abstract_inverted_index.system. | 8 |
| abstract_inverted_index.updates | 105 |
| abstract_inverted_index.without | 109 |
| abstract_inverted_index.Epilepsy | 0 |
| abstract_inverted_index.achieved | 122 |
| abstract_inverted_index.addition | 76 |
| abstract_inverted_index.compared | 87, 153 |
| abstract_inverted_index.dataset. | 72 |
| abstract_inverted_index.patients | 23 |
| abstract_inverted_index.presents | 34 |
| abstract_inverted_index.proposed | 119 |
| abstract_inverted_index.segments | 49 |
| abstract_inverted_index.seizures | 12 |
| abstract_inverted_index.(CHB-MIT) | 70 |
| abstract_inverted_index.Conformer | 63, 80, 121 |
| abstract_inverted_index.accuracy. | 111 |
| abstract_inverted_index.detection | 138 |
| abstract_inverted_index.epileptic | 47 |
| abstract_inverted_index.introduce | 94 |
| abstract_inverted_index.long-term | 52 |
| abstract_inverted_index.patients. | 31 |
| abstract_inverted_index.treatment | 15 |
| abstract_inverted_index.Children's | 68 |
| abstract_inverted_index.Conformer, | 42 |
| abstract_inverted_index.accidental | 20 |
| abstract_inverted_index.equivalent | 157 |
| abstract_inverted_index.evaluation | 58 |
| abstract_inverted_index.leveraging | 74 |
| abstract_inverted_index.operations | 152 |
| abstract_inverted_index.prediction | 10, 146 |
| abstract_inverted_index.approximate | 96 |
| abstract_inverted_index.non-spiking | 90, 156 |
| abstract_inverted_index.operations, | 77 |
| abstract_inverted_index.recordings. | 55 |
| abstract_inverted_index.sacrificing | 110 |
| abstract_inverted_index.sensitivity | 125 |
| abstract_inverted_index.specificity | 131 |
| abstract_inverted_index.spike-based | 75 |
| abstract_inverted_index.Hospital-MIT | 69 |
| abstract_inverted_index.Transformer, | 39 |
| abstract_inverted_index.intervention | 14 |
| abstract_inverted_index.neuromorphic | 36 |
| abstract_inverted_index.Additionally, | 92 |
| abstract_inverted_index.Convolutional | 38 |
| abstract_inverted_index.computational | 85 |
| abstract_inverted_index.significantly | 17, 81 |
| abstract_inverted_index.classification | 84 |
| abstract_inverted_index.spike-triggered | 103 |
| abstract_inverted_index.electroencephalogram | 53 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.02786576 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |