Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography

Zhou, Yufeng; You, Jing; Zhu, Fengjun; Bragin, Anatol; Engel, Jerome; Li, Lin

doi:10.1109/embc46164.2021.9630651

Cited by 2 publications

(10 citation statements)

References 20 publications

(36 reference statements)

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“…Hotspots of ST reflected a thin and long candle‐shaped contour, while HFOs typically represent more of a squeezed or orb shape around the center, with AESH not indicating any obvious hotspot. Here, we introduced a similar approach to quantify the predominant characteristic of sharp transients 33 . The first property introduced was the average power

\overset{Z}{true}

, and the power of the

k^{th}

time window is denoting as:

{\overset{true¯}{Z}}_{k} = \frac{1}{TF} \sum_{l} \sum_{m = t_{k}}^{t_{k + 1} - 1} Z_{lm}

, it was set to

\frac{1}{TF} \sum_{l} \sum_{m} Z_{lm}

, which was identical to our previous study 33 .…”

Section: Methodsmentioning

confidence: 99%

“…Set the starting points boundary <0 and set the ending points boundary >0 (Figure 2). All other parameters in this section follow the settings in the previous study 33 …”

Section: Methodsmentioning

confidence: 99%

“…All other parameters in this section follow the settings in the previous study. 33 Module 3: HFO classification All unlabeled data were processed according to the pipeline to form a refined dataset. The POE of each individual HFO was implemented to compute the corresponding weighted frequency F, which is defined as:…”

Section: ) Power Thresholding Layermentioning

confidence: 99%

“…Here, we introduced a similar approach to quantify the predominant characteristic of sharp transients. 33 The first property introduced was the average power Z , and the power of the k th time window is denoting as:…”

Section: ) Power Thresholding Layermentioning

confidence: 99%

“…Z lm, which was identical to our previous study. 33 The other was the power ratio HPR, and the ratio of the k th window is as follows:…”

Section: ) Power Thresholding Layermentioning

confidence: 99%

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An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections

et al. 2022

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Objective: Aiming to improve the feasibility and reliability of using highfrequency oscillations (HFOs) for translational studies of epilepsy, we present a pipeline with features specifically designed to reject false positives for HFOs to improve the automatic HFO detector. Methods:We presented an integrated, multi-layered procedure capable of automatically rejecting HFOs from a variety of common false positives, such as motion, background signals, and sharp transients. This method utilizes a timefrequency contour approach that embeds three different layers including peak constraints, power thresholds, and morphological identification to discard false positives. Four experts were involved in rating detected HFO events that were randomly selected from different posttraumatic epilepsy (PTE) animals for a comprehensive evaluation.

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\overset{Z}{true}

, and the power of the

k^{th}

time window is denoting as:

{\overset{true¯}{Z}}_{k} = \frac{1}{TF} \sum_{l} \sum_{m = t_{k}}^{t_{k + 1} - 1} Z_{lm}

, it was set to

\frac{1}{TF} \sum_{l} \sum_{m} Z_{lm}

, which was identical to our previous study 33 .…”

Section: Methodsmentioning

confidence: 99%

“…Set the starting points boundary <0 and set the ending points boundary >0 (Figure 2). All other parameters in this section follow the settings in the previous study 33 …”

Section: Methodsmentioning

confidence: 99%

Section: ) Power Thresholding Layermentioning

confidence: 99%

Section: ) Power Thresholding Layermentioning

confidence: 99%

“…Z lm, which was identical to our previous study. 33 The other was the power ratio HPR, and the ratio of the k th window is as follows:…”

Section: ) Power Thresholding Layermentioning

confidence: 99%

See 3 more Smart Citations

An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections

et al. 2022

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Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy

Zhu

Wang

et al. 2023

Front. Neurol.

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ObjectiveTo test the feasibility and reliability of intracranial electrophysiological recordings in an acute status epilepticus model on laboratory swine.MethodIntrahippocampal injection of kainic acid (KA) was performed on 17 male Bama pigs (Sus scrofa domestica) weighing between 25 and 35 kg. Two stereoelectroencephalography (SEEG) electrodes with a total of 16 channels were implanted bilaterally along the sensorimotor cortex to the hippocampus. Brain electrical activity was recorded 2 h daily for 9–28 days. Three KA dosages were tested to evaluate the quantities capable of evoking status epilepticus. Local field potentials (LFPs) were recorded and compared before and after the KA injection. We quantified the epileptic patterns, including the interictal spikes, seizures, and high-frequency oscillations (HFOs), up to 4 weeks after the KA injection. Test–retest reliability using intraclass correlation coefficients (ICCs) were performed on interictal HFO rates to evaluate the recording stability of this model.ResultsThe KA dosage test suggested that a 10 μl (1.0 μg/μl) intrahippocampal injection could successfully evoke status epilepticus lasting from 4 to 12 h. At this dosage, eight pigs (50% of total) had prolonged epileptic events (tonic-chronic seizures + interictal spikes n = 5, interictal spikes alone n = 3) in the later 4 weeks of the video-SEEG recording period. Four pigs (25% of total) had no epileptic activities, and another four (25%) had lost the cap or did not complete the experiments. Animals that showed epileptiform events were grouped as E + (n = 8) and the four animals showing no signs of epileptic events were grouped as E– (n = 4). A total of 46 electrophysiological seizures were captured in the 4-week post-KA period from 4 E + animals, with the earliest onset on day 9. The seizure durations ranged from 12 to 45 s. A significant increase of hippocampal HFOs rate (num/min) was observed in the E+ group during the post-KA period (weeks 1, 2,4, p < 0.05) compared to the baseline. But the E-showed no change or a decrease (in week 2, p = 0.43) compared to their baseline rate. The between-group comparison showed much higher HFO rates in E + vs. E – (F = 35, p < 0.01). The high ICC value [ICC (1, k) = 0.81, p < 0.05] quantified from the HFO rate suggested that this model had a stable measurement of HFOs during the four-week post-KA periods.SignificanceThis study measured intracranial electrophysiological activity in a swine model of KA-induced mesial temporal lobe epilepsy (mTLE). Using the clinical SEEG electrode, we distinguished abnormal EEG patterns in the swine brain. The high test–retest reliability of HFO rates in the post-KA period suggests the utility of this model for studying mechanisms of epileptogenesis. The use of swine may provide satisfactory translational value for clinical epilepsy research.

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Automatic Electrophysiological Noise Reduction and Epileptic Seizure Detection for Stereoelectroencephalography

Cited by 2 publications

References 20 publications

An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections

An approach for reliably identifying high‐frequency oscillations and reducing false‐positive detections

Intracranial electrophysiological recordings on a swine model of mesial temporal lobe epilepsy

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