Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe
Background: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges...
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Frontiers Media S.A.
2020-10-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fneur.2020.563577/full |
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| author | Aljoscha Thomschewski Aljoscha Thomschewski Aljoscha Thomschewski Nathalie Gerner Patrick B. Langthaler Patrick B. Langthaler Eugen Trinka Arne C. Bathke Arne C. Bathke Jürgen Fell Yvonne Höller |
| author_facet | Aljoscha Thomschewski Aljoscha Thomschewski Aljoscha Thomschewski Nathalie Gerner Patrick B. Langthaler Patrick B. Langthaler Eugen Trinka Arne C. Bathke Arne C. Bathke Jürgen Fell Yvonne Höller |
| author_sort | Aljoscha Thomschewski |
| collection | DOAJ |
| description | Background: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges for HFO research. First, detection, when performed manually, is time consuming and prone to rater biases, but when performed automatically, it is biased by artifacts mimicking HFOs. Second, distinguishing physiologic from pathologic HFOs in patients with epilepsy is problematic. Here we automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe.Methods: Ten patients with unclear seizure origin and bilaterally implanted macroelectrodes took part in a visual memory consolidation task. In addition to iEEG, scalp EEG, electrooculography (EOG), and facial electromyography (EMG) were recorded. iEEG channels contralateral to the suspected epileptogenic zone were inspected visually for HFOs. Furthermore, HFOs were marked automatically using an RMS detector and a Stockwell classifier. We compared the two detection approaches and assessed a possible link between task performance and HFO occurrence during encoding and retrieval trials.Results: HFO occurrence rates were significantly lower when events were marked manually. The automatic detection algorithm was greatly biased by filter-artifacts. Surprisingly, EOG artifacts as seen on scalp electrodes appeared to be linked to many HFOs in the iEEG. Occurrence rates could not be associated to memory performance, and we were not able to detect strictly defined “clear” ripples.Conclusion: Filtered graphoelements in the EEG are known to mimic HFOs and thus constitute a problem. So far, in invasive EEG recordings mostly technical artifacts and filtered epileptiform discharges have been considered as sources for these “false” HFOs. The data at hand suggests that even ocular artifacts might bias automatic detection in invasive recordings. Strict guidelines and standards for HFO detection are necessary in order to identify artifact-derived HFOs, especially in conditions when cognitive tasks might produce a high amount of artifacts. |
| first_indexed | 2024-12-12T08:41:09Z |
| format | Article |
| id | doaj.art-01c423e67f5d49679a78da5d18a53065 |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-12-12T08:41:09Z |
| publishDate | 2020-10-01 |
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| series | Frontiers in Neurology |
| spelling | doaj.art-01c423e67f5d49679a78da5d18a530652022-12-22T00:30:46ZengFrontiers Media S.A.Frontiers in Neurology1664-22952020-10-011110.3389/fneur.2020.563577563577Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal LobeAljoscha Thomschewski0Aljoscha Thomschewski1Aljoscha Thomschewski2Nathalie Gerner3Patrick B. Langthaler4Patrick B. Langthaler5Eugen Trinka6Arne C. Bathke7Arne C. Bathke8Jürgen Fell9Yvonne Höller10Department of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, AustriaDepartment of Mathematics, Paris-Lodron University of Salzburg, Salzburg, AustriaDepartment of Psychology, Paris-Lodron University of Salzburg, Salzburg, AustriaDepartment of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, AustriaDepartment of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, AustriaDepartment of Mathematics, Paris-Lodron University of Salzburg, Salzburg, AustriaDepartment of Neurology, Christian-Doppler Medical Center, Paracelsus Medical University, Salzburg, AustriaDepartment of Mathematics, Paris-Lodron University of Salzburg, Salzburg, AustriaIntelligent Data Analytics Lab Salzburg, Paris-Lodron University of Salzburg, Salzburg, AustriaDepartment of Epileptology, University Hospital Bonn, Bonn, GermanyFaculty of Psychology, University of Akureyri, Akureyri, IcelandBackground: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges for HFO research. First, detection, when performed manually, is time consuming and prone to rater biases, but when performed automatically, it is biased by artifacts mimicking HFOs. Second, distinguishing physiologic from pathologic HFOs in patients with epilepsy is problematic. Here we automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe.Methods: Ten patients with unclear seizure origin and bilaterally implanted macroelectrodes took part in a visual memory consolidation task. In addition to iEEG, scalp EEG, electrooculography (EOG), and facial electromyography (EMG) were recorded. iEEG channels contralateral to the suspected epileptogenic zone were inspected visually for HFOs. Furthermore, HFOs were marked automatically using an RMS detector and a Stockwell classifier. We compared the two detection approaches and assessed a possible link between task performance and HFO occurrence during encoding and retrieval trials.Results: HFO occurrence rates were significantly lower when events were marked manually. The automatic detection algorithm was greatly biased by filter-artifacts. Surprisingly, EOG artifacts as seen on scalp electrodes appeared to be linked to many HFOs in the iEEG. Occurrence rates could not be associated to memory performance, and we were not able to detect strictly defined “clear” ripples.Conclusion: Filtered graphoelements in the EEG are known to mimic HFOs and thus constitute a problem. So far, in invasive EEG recordings mostly technical artifacts and filtered epileptiform discharges have been considered as sources for these “false” HFOs. The data at hand suggests that even ocular artifacts might bias automatic detection in invasive recordings. Strict guidelines and standards for HFO detection are necessary in order to identify artifact-derived HFOs, especially in conditions when cognitive tasks might produce a high amount of artifacts.https://www.frontiersin.org/article/10.3389/fneur.2020.563577/fullhigh-frequency oscillationsvisual memoryinvasive EEGelectroencephalographyepilepsy |
| spellingShingle | Aljoscha Thomschewski Aljoscha Thomschewski Aljoscha Thomschewski Nathalie Gerner Patrick B. Langthaler Patrick B. Langthaler Eugen Trinka Arne C. Bathke Arne C. Bathke Jürgen Fell Yvonne Höller Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe Frontiers in Neurology high-frequency oscillations visual memory invasive EEG electroencephalography epilepsy |
| title | Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe |
| title_full | Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe |
| title_fullStr | Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe |
| title_full_unstemmed | Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe |
| title_short | Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe |
| title_sort | automatic vs manual detection of high frequency oscillations in intracranial recordings from the human temporal lobe |
| topic | high-frequency oscillations visual memory invasive EEG electroencephalography epilepsy |
| url | https://www.frontiersin.org/article/10.3389/fneur.2020.563577/full |
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