Applicability of Hyperdimensional Computing to Seizure Detection
Hyperdimensional (HD) computing is a form of brain-inspired computing which can be applied to numerous classification problems. In past research, it has been shown that seizures can be detected from electroencephalograms (EEG) with high accuracy using local binary pattern (LBP) encoding. This paper...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of Circuits and Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/9744111/ |
| _version_ | 1828070751513608192 |
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| author | Lulu Ge Keshab K. Parhi |
| author_facet | Lulu Ge Keshab K. Parhi |
| author_sort | Lulu Ge |
| collection | DOAJ |
| description | Hyperdimensional (HD) computing is a form of brain-inspired computing which can be applied to numerous classification problems. In past research, it has been shown that seizures can be detected from electroencephalograms (EEG) with high accuracy using local binary pattern (LBP) encoding. This paper explores applicability of binary HD computing to seizure detection from intra-cranial EEG (iEEG) data from the Kaggle seizure detection contest based on using both LBP and power spectral density (PSD) features. In the PSD method, three novel approaches to HD classification are presented for both selected features and all features. These are referred as <italic>single classifier long hypervector</italic>, <italic>multiple classifiers</italic>, and <italic>single classifier short hypervector</italic>. To visualize the quality of classification of test data, a <italic>hypervector distance</italic> plot is introduced that plots the Hamming distance of the query hpervectors from one class hypervector <italic>vs.</italic> that from the other. Simulation results show that: <italic>1)</italic>. LBP method offers an average 80.9% test accuracy, 71.9% sensitivity, 81.4% specificity and 76.6% test AUC whereas the PSD method can achieve an average of 91.0% test accuracy, 81.8% sensitivity, 92.0% specificity and 86.9% test AUC. <italic>2)</italic>. The average seizure detection latency is 2.5s for LBP method and is 4.5s for the PSD methods. This average latency, less than 5s, is a relevant parameter for fast drug delivery, indicating that both LBP and PSD methods are able to detect the seizures in a timely manner. The performance using selected PSD features is better than that using all features. <italic>3)</italic>. It is shown that the dimensionality of the hypervector can be reduced to 1, 000 bits for LBP and PSD methods from 10, 000. Futhermore, for some approaches of selected features, the dimensionality of the hypervector can be reduced to 100 bits. |
| first_indexed | 2024-04-11T00:43:52Z |
| format | Article |
| id | doaj.art-b9ca5c40b09f4dcba157528c5708efd3 |
| institution | Directory Open Access Journal |
| issn | 2644-1225 |
| language | English |
| last_indexed | 2024-04-11T00:43:52Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Circuits and Systems |
| spelling | doaj.art-b9ca5c40b09f4dcba157528c5708efd32023-01-06T00:00:36ZengIEEEIEEE Open Journal of Circuits and Systems2644-12252022-01-013597110.1109/OJCAS.2022.31630759744111Applicability of Hyperdimensional Computing to Seizure DetectionLulu Ge0https://orcid.org/0000-0002-0043-6512Keshab K. Parhi1https://orcid.org/0000-0001-6543-2793Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USADepartment of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USAHyperdimensional (HD) computing is a form of brain-inspired computing which can be applied to numerous classification problems. In past research, it has been shown that seizures can be detected from electroencephalograms (EEG) with high accuracy using local binary pattern (LBP) encoding. This paper explores applicability of binary HD computing to seizure detection from intra-cranial EEG (iEEG) data from the Kaggle seizure detection contest based on using both LBP and power spectral density (PSD) features. In the PSD method, three novel approaches to HD classification are presented for both selected features and all features. These are referred as <italic>single classifier long hypervector</italic>, <italic>multiple classifiers</italic>, and <italic>single classifier short hypervector</italic>. To visualize the quality of classification of test data, a <italic>hypervector distance</italic> plot is introduced that plots the Hamming distance of the query hpervectors from one class hypervector <italic>vs.</italic> that from the other. Simulation results show that: <italic>1)</italic>. LBP method offers an average 80.9% test accuracy, 71.9% sensitivity, 81.4% specificity and 76.6% test AUC whereas the PSD method can achieve an average of 91.0% test accuracy, 81.8% sensitivity, 92.0% specificity and 86.9% test AUC. <italic>2)</italic>. The average seizure detection latency is 2.5s for LBP method and is 4.5s for the PSD methods. This average latency, less than 5s, is a relevant parameter for fast drug delivery, indicating that both LBP and PSD methods are able to detect the seizures in a timely manner. The performance using selected PSD features is better than that using all features. <italic>3)</italic>. It is shown that the dimensionality of the hypervector can be reduced to 1, 000 bits for LBP and PSD methods from 10, 000. Futhermore, for some approaches of selected features, the dimensionality of the hypervector can be reduced to 100 bits.https://ieeexplore.ieee.org/document/9744111/Hyperdimensional (HD) computingclassificationseizure detectionlocal binary pattern (LBP)power spectral density (PSD)Fisher score |
| spellingShingle | Lulu Ge Keshab K. Parhi Applicability of Hyperdimensional Computing to Seizure Detection IEEE Open Journal of Circuits and Systems Hyperdimensional (HD) computing classification seizure detection local binary pattern (LBP) power spectral density (PSD) Fisher score |
| title | Applicability of Hyperdimensional Computing to Seizure Detection |
| title_full | Applicability of Hyperdimensional Computing to Seizure Detection |
| title_fullStr | Applicability of Hyperdimensional Computing to Seizure Detection |
| title_full_unstemmed | Applicability of Hyperdimensional Computing to Seizure Detection |
| title_short | Applicability of Hyperdimensional Computing to Seizure Detection |
| title_sort | applicability of hyperdimensional computing to seizure detection |
| topic | Hyperdimensional (HD) computing classification seizure detection local binary pattern (LBP) power spectral density (PSD) Fisher score |
| url | https://ieeexplore.ieee.org/document/9744111/ |
| work_keys_str_mv | AT luluge applicabilityofhyperdimensionalcomputingtoseizuredetection AT keshabkparhi applicabilityofhyperdimensionalcomputingtoseizuredetection |