Universal automated high frequency oscillation detector for real-time, long term EEG

Gliske, S.; Irwin, Zachary T.; Davis, Kathryn A.; Sahaya, Kinshuk; Chestek, Cynthia A.; Stacey, William C.

doi:10.1016/j.clinph.2015.07.016

Cited by 81 publications

(142 citation statements)

References 47 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…This is somewhat expected because the RMS detector was initially designed for use with microwire data, and it has been shown that the characteristics of high frequency activity depend on electrode size (Worrell et al 2008). However, the RMS detector has been successfully implemented in various recording schemes, including microwire electrodes (Staba et al 2002;Staba et al 2004) and macro-and microelectrodes Gliske et al 2016). Importantly, the results of our tests demonstrate that good performance is dependent on proper optimization of all parameters, not just the threshold.…”

Section: Discussionmentioning

confidence: 86%

“…The RMS detector is one of the most widely used automatic HFO detectors in published studies (Gardner et al 2007;Blanco et al 2010;Zelmann et al 2012;Gliske et al 2016). …”

Section: Rms Detectormentioning

confidence: 99%

“…There are several means to reduce the FDR, e.g. applying post-processing steps (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016) or using human validation (Staba et al 2002;Gardner et al 2007;Crépon et al 2010). Here we propose another approach, in which α is optimized based on FDR instead of FPR.…”

Section: Parameter Optimization To Reduce the False Detection Ratementioning

confidence: 99%

“…More recently, several algorithms for automated artifact rejection have been developed (Burnos et al 2014;Cho et al 2014;Amiri et al 2016;Gliske et al 2016). These algorithms are independent from the initial detection, which is accomplished through either human visual analysis (Amiri et al 2016) or an existing detector (Burnos et al 2014;Cho et al 2014;Gliske et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms are independent from the initial detection, which is accomplished through either human visual analysis (Amiri et al 2016) or an existing detector (Burnos et al 2014;Cho et al 2014;Gliske et al 2016). These methods reduce the number of false detections, but again increase the complexity and number of parameters, making implementation more challenging.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Charupanit

Lopour

2017

Brain Topogr

View full text Add to dashboard Cite

Keywords:HFO Epilepsy Ripple Electrocorticogram Depth electrode Highlights  New automatic HFO detection algorithm requires optimization of only one tunable parameter that is related to the number of allowable false positive events.  The detector uses an iterative estimate of the background amplitude distribution to choose the threshold.  Cross-validation tests indicate that sensitivity is superior to a commonly used automated detector based on RMS amplitude. AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptic tissue, but detection of these electrographic events remains a challenge. Automatic detectors show encouraging results, but they typically require optimization of multiple parameters, which is a barrier to good performance and broad applicability. We therefore propose a new automatic HFO detection algorithm, focusing on simplicity and ease of implementation. It requires tuning of only an amplitude threshold, which can be determined by an iterative process or directly calculated from statistics of the rectified filtered data (i.e. mean plus standard deviation). The iterative approach uses an estimate of the amplitude probability distribution of the background activity to calculate the optimum threshold for identification of transient high amplitude events. We tested both the iterative and non-iterative approaches using a dataset of visually marked HFOs, and we compared the performance to a commonly used detector based on the root-mean-square. When the threshold was optimized for individual channels via ROC curve, all three methods were comparable. The iterative detector achieved a sensitivity of 99.6%, false positive rate (FPR) of 1.1%, and false detection rate (FDR) of 37.3%. However, in an eight-fold cross-validation test, the iterative method had better sensitivity than the other two methods (80.0% compared to 64.4% and 65.8%), with FPR and FDR of 1.3%, and 49.4%, respectively. The simplicity of this algorithm, with only a single parameter, will enable consistent application of automatic detection across research centers and recording modalities, and it may therefore be a powerful tool for the assessment and localization of epileptic activity.2

show abstract

Section: Discussionmentioning

confidence: 86%

“…The RMS detector is one of the most widely used automatic HFO detectors in published studies (Gardner et al 2007;Blanco et al 2010;Zelmann et al 2012;Gliske et al 2016). …”

Section: Rms Detectormentioning

confidence: 99%

Section: Parameter Optimization To Reduce the False Detection Ratementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Charupanit

Lopour

2017

Brain Topogr

View full text Add to dashboard Cite

show abstract

Detection of anomalous high‐frequency events in human intracranial EEG

et al. 2020

View full text Add to dashboard Cite

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. AbstractObjective: High-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high-frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape. Method: We propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared. Result: In the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non-SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitudebased method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125). Significance: ADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non-SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome. |

show abstract

Epileptic high-frequency oscillations: detection and classification

Chou

Chen

et al. 2019

Multidim Syst Sign Process

View full text Add to dashboard Cite

Universal automated high frequency oscillation detector for real-time, long term EEG

Cited by 81 publications

References 47 publications

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

A Simple Statistical Method for the Automatic Detection of Ripples in Human Intracranial EEG

Detection of anomalous high‐frequency events in human intracranial EEG

Epileptic high-frequency oscillations: detection and classification

Contact Info

Product

Resources

About