Intracerebrally recorded high frequency oscillations: Simple visual assessment versus automated detection

Exploring Ripple Waves in the Human Brain

Ripples are brief (<150 ms) high-frequency oscillatory neural activities in the brain with a range of 140 to 200 Hz in rodents and 80 to 140 Hz in humans. Ripples are regarded as playing an essential role in several aspects of memory function, mainly in the hippocampus. This type of ripple generally occurs with sharp waves and is called a sharp-wave ripple (SPW-R). Extensive research of SPW-Rs in the rodent brain while actively awake has also linked the function of these SPW-Rs to navigation and decision making. Although many studies with rodents unveiled SPW-R function, research in humans on this subject is still sparse. Therefore, unveiling SPW-R function in the human hippocampus is warranted. A certain type of ripples may also be a biomarker of epilepsy. This type of ripple is called a pathological ripple (p-ripple). p-ripples have a wider range of frequency (80-500 Hz) than SPW-Rs, and the range of frequency is especially higher in brain regions that are intrinsically linked to epilepsy onset. Brain regions producing ripples are too small for scalp electrode recording, and intracranial recording is typically needed to detect ripples. In addition, SPW-Rs in the human hippocampus have been recorded from patients with epilepsy who may have p-ripples. Differentiating SPW-Rs and p-ripples is often not easy. We need to develop more sophisticated methods to record SPW-Rs to differentiate them from p-ripples. This paper reviews the general features and roles of ripple waves.

Recent advances in the noninvasive detection of high-frequency oscillations in the human brain

In recent decades, a significant body of evidence based on invasive clinical research has showed that high-frequency oscillations (HFOs) are a promising biomarker for localization of the seizure onset zone (SOZ), and therefore, have the potential to improve postsurgical outcomes in patients with epilepsy. Emerging clinical literature has demonstrated that HFOs can be recorded noninvasively using methods such as scalp electroencephalography (EEG) and magnetoencephalography (MEG). Not only are HFOs considered to be a useful biomarker of the SOZ, they also have the potential to gauge disease severity, monitor treatment, and evaluate prognostic outcomes. In this article, we review recent clinical research on noninvasively detected HFOs in the human brain, with a focus on epilepsy. Noninvasively detected scalp HFOs have been investigated in various types of epilepsy. HFOs have also been studied noninvasively in other pathologic brain disorders, such as migraine and autism. Herein, we discuss the challenges reported in noninvasive HFO studies, including the scarcity of MEG and high-density EEG equipment in clinical settings, low signal-to-noise ratio, lack of clinically approved automated detection methods, and the difficulty in differentiating between physiologic and pathologic HFOs. Additional studies on noninvasive recording methods for HFOs are needed, especially prospective multicenter studies. Further research is fundamental, and extensive work is needed before HFOs can routinely be assessed in clinical settings; however, the future appears promising.

Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.

Localizing epileptogenic regions using high-frequency oscillations and machine learning

Pathological high frequency oscillations (HFOs) are putative neurophysiological biomarkers of epileptogenic brain tissue. Utilizing HFOs for epilepsy surgery planning offers the promise of improved seizure outcomes for patients with medically refractory epilepsy. This review discusses possible machine learning strategies that can be applied to HFO biomarkers to better identify epileptogenic regions. We discuss the role of HFO rate, and utilizing features such as explicit HFO properties (spectral content, duration, and power) and phase-amplitude coupling for distinguishing pathological HFO (pHFO) events from physiological HFO events. In addition, the review highlights the importance of neuroanatomical localization in machine learning strategies.

Automated Detection of High-Frequency Oscillations in Epilepsy Based on a Convolutional Neural Network

Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future.

Simultaneously recorded intracranial and scalp high frequency oscillations help identify patients with poor postsurgical seizure outcome

OBJECTIVE

High frequency oscillations (HFO) between 80-500 Hz are markers of epileptic areas in intracranial and maybe also scalp EEG. We investigate simultaneous recordings of scalp and intracranial EEG and hypothesize that scalp HFOs provide important additional clinical information in the presurgical setting.

METHODS

Spikes and HFOs were visually identified in all intracranial scalp EEG channels. Analysis of correlation of event location between intracranial and scalp EEG as well as relationship between events and the SOZ and zone of surgical removal was performed.

RESULTS

24 patients could be included, 23 showed spikes and 19 HFOs on scalp recordings. In 15/19 patients highest scalp HFO rate was located over the implantation side, with 13 patients having the highest scalp and intracranial HFO rate over the same region. 17 patients underwent surgery, 7 became seizure free. Patients with poor post-operative outcome showed significantly more regions with HFO than those with seizure free outcome.

CONCLUSIONS

Scalp HFOs are mostly located over the SOZ. Widespread scalp HFOs are indicative of a larger epileptic network and associated with poor postsurgical outcome.

SIGNIFICANCE

Analysis of scalp HFO add clinically important information about the extent of epileptic areas during presurgical simultaneous scalp and intracranial EEG recordings.

High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.

Generalizability of High Frequency Oscillation Evaluations in the Ripple Band

Objective: We examined the interrater reliability and generalizability of high-frequency oscillation (HFO) visual evaluations in the ripple (80–250 Hz) band, and established a framework for the transition of HFO analysis to routine clinical care. We were interested in the interrater reliability or epoch generalizability to describe how similar the evaluations were between reviewers, and in the reviewer generalizability to represent the consistency of the internal threshold each individual reviewer.

Methods: We studied 41 adult epilepsy patients (mean age: 35.6 years) who underwent intracranial electroencephalography. A morphology detector was designed and used to detect candidate HFO events, lower-threshold events, and distractor events. These events were subsequently presented to six expert reviewers, who visually evaluated events for the presence of HFOs. Generalizability theory was used to characterize the epoch generalizability (interrater reliability) and reviewer generalizability (internal threshold consistency) of visual evaluations, as well as to project the numbers of epochs, reviewers, and datasets required to achieve strong generalizability (threshold of 0.8).

Results: The reviewer generalizability was almost perfect (0.983), indicating there were sufficient evaluations to determine the internal threshold of each reviewer. However, the interrater reliability for 6 reviewers (0.588) and pairwise interrater reliability (0.322) were both poor, indicating that the agreement of 6 reviewers is insufficient to reliably establish the presence or absence of individual HFOs. Strong interrater reliability (≥0.8) was projected as requiring a minimum of 17 reviewers, while strong reviewer generalizability could be achieved with <30 epoch evaluations per reviewer.

Significance: This study reaffirms the poor reliability of using small numbers of reviewers to identify HFOs, and projects the number of reviewers required to overcome this limitation. It also provides a set of tools which may be used for training reviewers, tracking changes to interrater reliability, and for constructing a benchmark set of epochs that can serve as a generalizable gold standard, against which other HFO detection algorithms may be compared. This study represents an important step toward the reconciliation of important but discordant findings from HFO studies undertaken with different sets of HFOs, and ultimately toward transitioning HFO analysis into a meaningful part of the clinical epilepsy workup.

Presurgical language mapping using event-related high-gamma activity: The Detroit procedure

A number of investigators have reported that event-related augmentation of high-gamma activity at 70-110 Hz on electrocorticography (ECoG) can localize functionally-important brain regions in children and adults who undergo epilepsy surgery. The advantages of ECoG-based language mapping over the gold-standard stimulation include: (i) lack of stimulation-induced seizures, (ii) better sensitivity of localization of language areas in young children, and (iii) shorter patient participant time. Despite its potential utility, ECoG-based language mapping is far less commonly practiced than stimulation mapping. Here, we have provided video presentations to explain, point-by-point, our own hardware setting and time-frequency analysis procedures. We also have provided standardized auditory stimuli, in multiple languages, ready to be used for ECoG-based language mapping. Finally, we discussed the technical aspects of ECoG-based mapping, including its pitfalls, to facilitate appropriate interpretation of the data.

Multi-channel algorithms for epileptic high-frequency oscillation detection

Short-lasting rhythmic activity in intracranial electroencephalogram (iEEG) in the frequency range of 80 to 500 Hz is regarded to be a promising biomarker of epileptogenicity. This activity is referred to as high-frequency oscillation (HFO), and its detection from iEEG is considered the first step to several applications. In this study, several multi-channel algorithms for HFO detection are proposed. With the proposed multi-channel statistics and threshold determination scheme, the algorithms allow HFO detection to be performed without breaking the iEEG channel structure and the detection threshold to be determined automatically. Experimental simulation results illustrate the advantage of the proposed algorithms over existing single-channel-based approaches.

High-frequency oscillations: The state of clinical research

Modern electroencephalographic (EEG) technology contributed to the appreciation that the EEG signal outside the classical Berger frequency band contains important information. In epilepsy, research of the past decade focused particularly on interictal high-frequency oscillations (HFOs) > 80 Hz. The first large application of HFOs was in the context of epilepsy surgery. This is now followed by other applications such as assessment of epilepsy severity and monitoring of antiepileptic therapy. This article reviews the evidence on the clinical use of HFOs in epilepsy with an emphasis on the latest developments. It highlights the growing literature on the association between HFOs and postsurgical seizure outcome. A recent meta-analysis confirmed a higher resection ratio for HFOs in seizure-free versus non-seizure-free patients. Residual HFOs in the postoperative electrocorticogram were shown to predict epilepsy surgery outcome better than preoperative HFO rates. The review further discusses the different attempts to separate physiological from epileptic HFOs, as this might increase the specificity of HFOs. As an example, analysis of sleep microstructure demonstrated a different coupling between HFOs inside and outside the epileptogenic zone. Moreover, there is increasing evidence that HFOs are useful to measure disease activity and assess treatment response using noninvasive EEG and magnetoencephalography. This approach is particularly promising in children, because they show high scalp HFO rates. HFO rates in West syndrome decrease after adrenocorticotropic hormone treatment. Presence of HFOs at the time of rolandic spikes correlates with seizure frequency. The time-consuming visual assessment of HFOs, which prevented their clinical application in the past, is now overcome by validated computer-assisted algorithms. HFO research has considerably advanced over the past decade, and use of noninvasive methods will make HFOs accessible to large numbers of patients. Prospective multicenter trials are awaited to gather information over long recording periods in large patient samples.

Identification of brain regions of interest for epilepsy surgery planning using support vector machines

In patients with intractable epilepsy, surgical resection is a promising treatment; however, post surgical seizure freedom is contingent upon accurate identification of the seizure onset zone (SOZ). Identification of the SOZ in extratemporal epilepsy requires invasive intracranial EEG (iEEG) recordings as well as resource intensive and subjective analysis by epileptologists. Expert inspection yields inconsistent localization of the SOZ which leads to comparatively poor post surgical outcomes for patients. This study employs recordings from 6 patients undergoing resection surgery in order to develop an automated and scalable system for identifying regions of interest (ROIs). Leveraging machine learning techniques and features used for seizure detection, a classification system was trained and tested on patients with Engel class I to class IV outcomes, demonstrating superior performance in the class I patients. Further, classification using features based upon both high frequency and low frequency oscillations was best able to identify channels suited for resection. This study demonstrates a novel approach to ROI identification and provides a path for developing tools to improve outcomes in epilepsy surgery.

RIPPLELAB: A Comprehensive Application for the Detection, Analysis and Classification of High Frequency Oscillations in Electroencephalographic Signals

High Frequency Oscillations (HFOs) in the brain have been associated with different physiological and pathological processes. In epilepsy, HFOs might reflect a mechanism of epileptic phenomena, serving as a biomarker of epileptogenesis and epileptogenicity. Despite the valuable information provided by HFOs, their correct identification is a challenging task. A comprehensive application, RIPPLELAB, was developed to facilitate the analysis of HFOs. RIPPLELAB provides a wide range of tools for HFOs manual and automatic detection and visual validation; all of them are accessible from an intuitive graphical user interface. Four methods for automated detection-as well as several options for visualization and validation of detected events-were implemented and integrated in the application. Analysis of multiple files and channels is possible, and new options can be added by users. All features and capabilities implemented in RIPPLELAB for automatic detection were tested through the analysis of simulated signals and intracranial EEG recordings from epileptic patients (n = 16; 3,471 analyzed hours). Visual validation was also tested, and detected events were classified into different categories. Unlike other available software packages for EEG analysis, RIPPLELAB uniquely provides the appropriate graphical and algorithmic environment for HFOs detection (visual and automatic) and validation, in such a way that the power of elaborated detection methods are available to a wide range of users (experts and non-experts) through the use of this application. We believe that this open-source tool will facilitate and promote the collaboration between clinical and research centers working on the HFOs field. The tool is available under public license and is accessible through a dedicated web site.

High-frequency oscillations in epilepsy and surgical outcome. A meta-analysis

High frequency oscillations (HFOs) are estimated as a potential marker for epileptogenicity. Current research strives for valid evidence that these HFOs could aid the delineation of the to-be resected area in patients with refractory epilepsy and improve surgical outcomes. In the present meta-analysis, we evaluated the relation between resection of regions from which HFOs can be detected and outcome after epilepsy surgery. We conducted a systematic review of all studies that related the resection of HFO-generating areas to postsurgical outcome. We related the outcome (seizure freedom) to resection ratio, that is, the ratio between the number of channels on which HFOs were detected and, among these, the number of channels that were inside the resected area. We compared the resection ratio between seizure free and not seizure free patients. In total, 11 studies were included. In 10 studies, ripples (80-200 Hz) were analyzed, and in 7 studies, fast ripples (>200 Hz) were studied. We found comparable differences (dif) and largely overlapping confidence intervals (CI) in resection ratios between outcome groups for ripples (dif = 0.18; CI: 0.10-0.27) and fast ripples (dif = 0.17; CI: 0.01-0.33). Subgroup analysis showed that automated detection (dif = 0.22; CI: 0.03-0.41) was comparable to visual detection (dif = 0.17; CI: 0.08-0.27). Considering frequency of HFOs (dif = 0.24; CI: 0.09-0.38) was related more strongly to outcome than considering each electrode that was showing HFOs (dif = 0.15; CI = 0.03-0.27). The effect sizes found in the meta-analysis are small but significant. Automated detection and application of a detection threshold in order to detect channels with a frequent occurrence of HFOs is important to yield a marker that could be useful in presurgical evaluation. In order to compare studies with different methodological approaches, detailed and standardized reporting is warranted.

Temporal and spatial characteristics of high frequency oscillations as a new biomarker in epilepsy

OBJECTIVE

Interictal high frequency oscillations (HFOs) are a promising candidate as a biomarker in epilepsy as well as for defining the seizure-onset zone as for the prediction of the surgical outcome after epilepsy surgery. The purpose of the study is to investigate properties of HFOs in long-term recordings with respect to the sleep-wake cycle and anatomic regions to verify previous results based on observations from short intervals and patients mainly with temporal lobe epilepsy to the analysis of hours of recordings and focal epilepsies with extratemporal origin.

METHODS

Automatic HFO detection using a radial basis function neural network detector was performed in long-term recordings of 15 presurgical patients investigated with subdural strip, grid, and depth contacts. Periods with visual marked sleep stages based on parallel scalp recordings from two consecutive nights were compared to awake intervals. Statistical analysis was based on the Kruskal-Wallis test, Mann-Whitney U-test and Spearman's rank correlations.

RESULTS

HFO rates in seizure-onset contacts differed from other brain regions independent of the sleep-wake cycle. For temporal contacts, the HFO rate increased significantly with sleep stage. In addition, contacts covering the parietal lobe, including rolandic cortex, showed a significant increase of HFO rates during sleep. However, no significant HFO rate changes depending on the sleep-wake cycle were found for frontal contacts.

SIGNIFICANCE

The rate of interictal HFOs predicted the SOZ with statistical significance at the group level, but properties other than the HFO rate may need to be considered to improve the diagnostic utility of HFOs. This study gives evidence that the modulation of HFO rates by states of the sleep-wake cycle has particular characteristics within different neocortical regions and in mesiotemporal structures, and contributes to the establishment of HFOs as a biomarker in epilepsy.

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations

OBJECTIVE

We investigate the relevance of high frequency oscillations (HFO) for biomarkers of epileptogenic tissue and indicators of preictal state before complex partial seizures in humans.

METHODS

We introduce a novel automated HFO detection method based on the amplitude and features of the HFO events. We examined intracranial recordings from 33 patients and compared HFO rates and characteristics between channels within and outside the seizure onset zone (SOZ). We analyzed changes of HFO activity from interictal to preictal and to ictal periods.

RESULTS

The average HFO rate is higher for SOZ channels compared to non-SOZ channels during all periods. Amplitudes and durations of HFO are higher for events within the SOZ in all periods compared to non-SOZ events, while their frequency is lower. All analyzed HFO features increase for the ictal period.

CONCLUSIONS

HFO may occur in all channels but their rate is significantly higher within SOZ and HFO characteristics differ from HFO outside the SOZ, but the effect size of difference is small.

SIGNIFICANCE

The present results show that based on accumulated dataset it is possible to distinguish HFO features different for SOZ and non-SOZ channels, and to show changes in HFO characteristics during the transition from interictal to preictal and to ictal periods.

Electrical stimulation for cortical mapping reduces the density of high frequency oscillations

BACKGROUND

High frequency oscillations (HFOs, 80-500 Hz) are EEG biomarkers for epileptogenic areas. HFOs are also indicators of disease activity as HFO rates increase after reduction of antiepileptic medication. Electrical stimulation (ES) can be used for diagnostic purposes as well as therapy in patients with refractory epilepsy. This study investigates the occurrence and changes of HFOs during ES in patients with refractory epilepsy.

OBJECTIVE

Analysis of the effects of ES using intracranial ES on the occurrence of epileptic HFOs.

METHODS

Patients underwent ES for diagnostic purposes. Ripples (80-200 Hz) and fast ripples (200-500 Hz) were visually marked in a baseline EEG segment prior to ES, after each period of ES as well as after the end of ES. In patients in whom ES triggered a seizure a pre- and postictal segment was marked. Rates of HFOs were compared for the different time periods using a Spearman's correlation and Wilcoxon rank sum test (p<0.05).

RESULTS

12 patients with 911 EEG channels were analyzed. Ripple (r=-0.42, p<0.001) as well as fast ripple (r=-0.21, p<0.001) rates decreased significantly over the course of stimulation. This phenomenon was not focal over the seizure onset or neighboring contacts but even observed over distant contacts.

CONCLUSIONS

ES resulted in a gradual decrease of HFO-Rates over time. The decrease of HFOs was not limited to SOZ areas. If HFOs are considered as markers of disease activity the reduction in HFO-rates as a result of intracranial ES has to be interpreted as a reduction of disease activity.

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

Recent studies have revealed the importance of high-frequency brain signals (>70 Hz). One challenge of high-frequency signal analysis is that the size of time-frequency representation of high-frequency brain signals could be larger than 1 terabytes (TB), which is beyond the upper limits of a typical computer workstation's memory (<196 GB). The aim of the present study is to develop a new method to provide greater sensitivity in detecting high-frequency magnetoencephalography (MEG) signals in a single automated and versatile interface, rather than the more traditional, time-intensive visual inspection methods, which may take up to several days. To address the aim, we developed a new method, accumulated source imaging, defined as the volumetric summation of source activity over a period of time. This method analyzes signals in both low- (1~70 Hz) and high-frequency (70~200 Hz) ranges at source levels. To extract meaningful information from MEG signals at sensor space, the signals were decomposed to channel-cross-channel matrix (CxC) representing the spatiotemporal patterns of every possible sensor-pair. A new algorithm was developed and tested by calculating the optimal CxC and source location-orientation weights for volumetric source imaging, thereby minimizing multi-source interference and reducing computational cost. The new method was implemented in C/C++ and tested with MEG data recorded from clinical epilepsy patients. The results of experimental data demonstrated that accumulated source imaging could effectively summarize and visualize MEG recordings within 12.7 h by using approximately 10 GB of computer memory. In contrast to the conventional method of visually identifying multi-frequency epileptic activities that traditionally took 2–3 days and used 1–2 TB storage, the new approach can quantify epileptic abnormalities in both low- and high-frequency ranges at source levels, using much less time and computer memory.

Peri-ictal broadband electrocorticographic activities between 1 and 700 Hz and seizure onset zones in 18 patients

OBJECTIVE

We investigated the relationship between locations of broadband peri-ictal electrocorticographic activities determined by a semi-automatic detection method and seizure onset zones in medically intractable epilepsy patients.

METHODS

We included 18 patients. Peri-ictal periods (-15 to +5s from the ictal onset) were divided into 4 periods of 5s duration each in bandwidth from 1 to 700 Hz divided into 11 bins. Thereafter, we calculated the mean overlapping percentage of the maximum amplitude activity electrodes with the seizure onset zone in the total number of seizures in each patient. Significance was considered at an adjusted p-value of 0.05.

RESULTS

By the maximum amplitude method with the Bonferroni correction, only high-frequency activities (>60 Hz) during -5 to 0 s from the ictal onset were significantly related to seizure onset zones. In post hoc analyses, bands in 60-139 Hz and 4-7 Hz were significantly related to seizure onset zones in the Bonferroni correction. However, after the less conservative Benjamini-Yekutieli correction and with the epileptogenicity index, other bands and periods after -10s from the ictal onset were also related with seizure onset zones.

SIGNIFICANCE

Detailed bands, timings and analytic methods of peri-ictal activities with high relationships to seizure onset zones were identified.