Analysis Of High-frequency Oscillations Research Articles

Analysis of high frequency oscillations based on MMC-HVDC system

Modular multilevel converter is widely used in new energy grid-connection and power grid asynchronous interconnection by virtue of its advantages of flexible and easy to expand structure and no commutation failure problem. In recent years, high-frequency oscillations have been observed in several flexible DC transmission projects at home and abroad, while the internal harmonic dynamic characteristics and control system of the MMC system are complex and the mechanism of high-frequency oscillations has not been sufficiently analysed at present. In this paper, the detailed DQ impedance model and the equivalent circuit impedance model on the AC side are firstly derived based on the state-space model of the MMC system, and the impedance sweeping method with superimposed small perturbations is proposed. Meanwhile, the dynamic characteristics of the detailed DQ impedance model are compared with those of the electromagnetic transient model. The amplitude-frequency characteristics of the system impedance under different system delays are further compared, and the mechanism of the high-frequency oscillation of the MMC-HVDC system is revealed.

Time-Frequency Analysis of Somatosensory Evoked High-Frequency (600 Hz) Oscillations as an Early Indicator of Arousal Recovery after Hypoxic-Ischemic Brain Injury.

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery phase, where the injury is reversible, has not been tested. We hypothesize that time-frequency (TF) analysis of HFOs can determine arousal recovery in the acute recovery phase. To test our hypothesis, eleven adult male Wistar rats were subjected to asphyxial CA (five with 3-min mild and six with 7-min moderate to severe CA) and SSEPs were recorded for 60 min post-resuscitation. Arousal level was quantified by the neurological deficit scale (NDS) at 4 h. Our results demonstrated that continuous wavelet transform (CWT) of SSEPs localizes HFOs in the TF domain under baseline conditions. The energy dispersed immediately after injury and gradually recovered. We proposed a novel TF-domain measure of HFO: the total power in the normal time-frequency space (NTFS) of HFO. We found that the NTFS power significantly separated the favorable and unfavorable outcome groups. We conclude that the NTFS power of HFOs provides earlier and objective determination of arousal recovery after CA.

"Generalized-to-focal" epilepsy: stereotactic EEG and high-frequency oscillation patterns

We aimed to clarify the pathophysiology of epilepsy involving seizures with apparently generalized onset, progressing to focal ictal rhythm through stereotactic EEG (SEEG) implantation, recording, stimulation and high-frequency oscillation (HFO) analysis. We identified two patients with seizures with bilateral electrographic onset evolving to focal ictal rhythm, who underwent SEEG implantation. Patients had pre-surgical epilepsy work-up, including prolonged video scalp EEG, brain MRI, PET, ictal/interictal SPECT, MEG, and EEG-fMRI prior to SEEG implantation. Both patients had childhood-onset seizures involving behavioural arrest and left versive head and eye deviation, evolving to bilateral tonic-clonic convulsions. Seizures were electrographically preceded by diffuse, bilateral 3-Hz activity resembling absence seizures. Both had suspected focal lesions based on neuroimaging, including 3T MRI and voxel-based post-processing in one patient. Electrode stimulation did not elicit any habitual electroclinical seizures. HFO analysis showed bilateral focal regions with high fast-ripple rates. “Generalized-to-focal” seizures may occur due to a diffuse, bilateral epileptic network, however, both patients showed ictal evolution from a generalized pattern to a single dominant focus which may explain why the focal aspect of their seizures had a consistent clinical semiology. Patients such as these may have a unique form of generalized epilepsy, but focal/multifocal cerebral abnormalities are also a possibility.

Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom.

In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach.We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80–250 Hz) and in the fast ripple (250–500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient’s HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.

High frequency oscillation analysis of intracranial 14 and 6 Hz-positive spikes and interictal epileptiform discharges in a patient with occipital lobe epilepsy

High frequency oscillation analysis of intracranial 14 and 6 Hz-positive spikes and interictal epileptiform discharges in a patient with occipital lobe epilepsy

Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation.

Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)—a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ.

Effects of a stable concentration of propofol on interictal high-frequency oscillations in drug-resistant epilepsy.

The aim of this study was to clarify the effect of a stable concentration of propofol on interictal high-frequency oscillations (HFOs), which may contribute to identifying the epileptogenic zone intraoperatively for resection surgery. Nine patients with drug-resistant focal epilepsy who underwent invasive pre-surgical evaluation with chronic subdural electrodes were recruited. Five-minute electrocorticograms during wakefulness, slow-wave sleep, and under a stable brain concentration of propofol were recorded with the same electrodes. In each patient, 1-10 pairs of electrodes were selected for both electrodes with EEG changes within 5 seconds from the ictal onset (ictal pattern for 5 seconds [IP5]) and those outside the area of IP5 with no interictal epileptiform discharges (non-epileptiform [nEPI]). The numbers of ripples (80-250Hz) and fast ripples (>250Hz) were measured semi-automatically using an established algorithm. Statistical testing was performed with a mixed effect model. Thirty-seven pairs of electrodes from nine patients were analysed for IP5 and 29 pairs from seven patients were analysed for nEPI. The numbers of HFOs differed between the areas (IP5 and nEPI) and among the conditions (wakefulness, slow-wave sleep, propofol anaesthesia) (all p <0.01). The HFO occurrence rates were significantly higher for IP5 than those for nEPI in all conditions (for both ripples and fast ripples in all conditions; p <0.01). The occurrence rates of HFOs for IP5 were significantly higher than those for nEPI under propofol anaesthesia. These are fundamental findings for intraoperative HFO analysis, however, the following limitations should be considered: physiological HFOs could not be completely differentiated from pathological HFOs; in order to apply an HFO detector, an appropriate cut-off threshold is needed; an artefact of the impulse response filter appears as an HFO; and the series was comprised of a small number of heterogeneous patients.

Integrated Automatic Detection, Classification and Imaging of High Frequency Oscillations With Stereoelectroencephalography.

ObjectiveDuring presurgical evaluation for focal epilepsy patients, the evidence supporting the use of high frequency oscillations (HFOs) for delineating the epileptogenic zone (EZ) increased over the past decade. This study aims to develop and validate an integrated automatic detection, classification and imaging pipeline of HFOs with stereoelectroencephalography (SEEG) to narrow the gap between HFOs quantitative analysis and clinical application.MethodsThe proposed pipeline includes stages of channel inclusion, candidate HFOs detection and automatic labeling with four trained convolutional neural network (CNN) classifiers and HFOs sorting based on occurrence rate and imaging. We first evaluated the initial detector using an open simulated dataset. After that, we validated our full algorithm in a 20-patient cohort against three assumptions based on previous studies. Classified HFOs results were compared with seizure onset zone (SOZ) channels for their concordance. The receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were calculated representing the prediction ability of the labeled HFOs outputs for SOZ.ResultsThe initial detector demonstrated satisfactory performance on the simulated dataset. The four CNN classifiers converged quickly during training, and the accuracies on the validation dataset were above 95%. The localization value of HFOs was significantly improved by HFOs classification. The AUC values of the 20 testing patients increased after HFO classification, indicating a satisfactory prediction value of the proposed algorithm for EZ identification.ConclusionOur detector can provide robust HFOs analysis results revealing EZ at the individual level, which may ultimately push forward the transitioning of HFOs analysis into a meaningful part of the presurgical evaluation and surgical planning.

Assessing Epileptogenicity Using Phase-Locked High Frequency Oscillations: A Systematic Comparison of Methods.

High frequency oscillations (HFOs) are traditional biomarkers to identify the epileptogenic tissue during presurgical evaluation in pharmacoresistant epileptic patients. Recently, the resection of brain tissue exhibiting coupling between the amplitude of HFOs and the phase of low frequencies demonstrated a more favorable surgical outcome. Here we compare the predictive value of ictal HFOs and four methods for quantifying the ictal phase-amplitude coupling, namely mean vector length, phase-locked high gamma, phase locking value, and modulation index (MI). We analyzed 32 seizures from 16 patients to identify the channels that exhibit HFOs and phase-locked HFOs during seizures. We compared the resection ratio, defined as the percentage of channels exhibiting coupling located in the resected tissue, with the postsurgical outcome. We found that the MI is the only method to show a significant difference between the resection ratios of patients with good and poor outcomes. We further show that the whole seizure, not only the onset, is critical to assess epileptogenicity using the phase-locked HFOs. We postulate that the superiority of MI stems from its capacity to assess coupling of discrete HFO events and its independence from the HFO power. These results confirm that quantitative analysis of HFOs can boost presurgical evaluation and indicate the paramount importance of algorithm selection for clinical applications.

MEEGIPS-A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations.

High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. Localization of the seizure onset zone, and, more specifically, of the to-be resected area in patients with refractory epilepsy seems to be supported by the detection of HFOs. The visual identification of HFOs is very time consuming with approximately 8 h for 10 min and 20 channels. Therefore, automated detection of HFOs is highly warranted. So far, no software for visual marking or automated detection of HFOs meets the needs of everyday clinical practice and research. In the context of the currently available tools and for the purpose of related local HFO study activities we aimed at converging the advantages of clinical and experimental systems by designing and developing a comprehensive and extensible software framework for HFO analysis that, on the one hand, focuses on the requirements of clinical application and, on the other hand, facilitates the integration of experimental code and algorithms. The development project included the definition of use cases, specification of requirements, software design, implementation, and integration. The work comprised the engineering of component-specific requirements, component design, as well as component- and integration-tests. A functional and tested software package is the deliverable of this activity. The project MEEGIPS, a Modular EEG Investigation and Processing System for visual and automated detection of HFOs, introduces a highly user friendly software that includes five of the most prominent automated detection algorithms. Future evaluation of these, as well as implementation of further algorithms is facilitated by the modular software architecture.

Transitory impairment of the somatosensory postsynaptic fibres after electrode implantation in the thalamus

To investigate whether the electrode implantation in the thalamus for deep brain stimulation can damage the somatosensory pathway. A Parkinsonian patient underwent electrode implantation in the ventralis intermediate (VIM) nucleus of the thalamus. The somatosensory evoked potentials (SEPs) were recorded to median nerve stimulation in the operatory room, immediately after surgery, and in the laboratory, 5 days after surgery. Both low-frequency SEPs and high-frequency oscillations (HFOs) were recorded from the scalp and the VIM electrode contacts. Immediately after VIM targeting, the low-frequency SEP amplitudes obtained from both the scalp and the VIM electrode contacts were decreased, but they recovered after 5 days. The HFOs underlying the VIM low-frequency SEPs showed the same behaviour. In particular, the HFO modifications due to electrode implantation mainly involved the higher-frequency HFOs (1500 Hz), rather than the lower-frequency HFOs (800 Hz). Our results suggest that the somatosensory fibers are transitorily damaged by electrode implantation in the thalamus. The HFO analysis allowed us to determine that it was the higher-frequency HFOs to be particularly impaired. Since the higher-frequency HFOs have a postynaptic origin, we suggest that electrode implantation in the thalamus can lead to a reversible damage of the neurons of the ventral posterolateral (VPL) nucleus.

Kurtosis-Based Detection of Intracranial High-Frequency Oscillations for the Identification of the Seizure Onset Zone

Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time-frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80-250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.

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.

S173. Automatic detection of high frequency oscillations (HFOs) to locate epileptogenic zone

Introduction Surgical resection of epileptogenic zone is the first choice for the treatment of refractory epilepsy, and accurate localization of epileptogenic zone is the key to determine the outcome of the operation. In recent years, There are many research show that high-frequency oscillations (HFOs) is a promising biomarker for the epileptogenic zone. Visual HFOs evaluation is subjectivity and time-consuming. It seriously limits the application of the HFOs in clinic. Therefore, it is very important to develop high frequency oscillation automatic detection program to precisely locate the epileptogenic zone for improving the curative effect of epilepsy surgery. Methods We retrospectively studied EEG data from 20 patients at Xuanwu hospital, Capital Medical University in who underwent epilepsy surgery (from January 2017 to March 2017). Inclusion criterion: 1. Patients with complete medical records and underwent surgery resection. 2. Implanted subdural grids, depth electrode or stereotactic deep electrode (for SEEG). 3. Sampling rate > 2000 Hz. 4. For more than half a year’s follow-up. Exclusion criterion: 1. EEG data with obvious artifacts. 2. Patients with bilateral lesions. 3. Patients with occipital lobe epilepsy. Inclusion criterion of channels: HFOs rate > 1/min (the number of highest HFOs channels is less than 10/min, then all channels are analyzed). All patients signed informed consent and the project was approved by the Ethics Committee of Xuanwu hospital, Capital Medical University. We applied the automatic detection program based on maximum distribution peak point calculating the baseline which developed by our laboratory to perform HFOs analysis for all of patients. After that, we sort the channels from high to low according to the number of HFOs, then the resection ratio was calculated used the total number of channels which from the highest frequency channels to the first channel without resected divided the total number of produced HFOs channel. Finally, the resection rate was compared with the prognosis of the patients. Results Patients are in two groups based on Engel Classification. thirteen patients in good outcome which means Engel Ia (seizure free), and seven patients in poor outcome which means Engel Ib-IV (non-seizure free). In good outcome patients’ group, the resection ratio of ripples is 24% and fast ripple is 82% (the lowest ratio is 40%). In poor outcome patients’ group, the resection ratio of ripples is 6% and fast ripples is 12% (the highest ratio is 21.4%). We found that resection ratio of fast ripple has obvious statistical significance (P Conclusion Our results show that patients who remove higher ratio channels of the Fast ripple would have better surgical outcome, therefore, fast ripple is more accurate than ripple in localizing epileptogenic zone.

P361 Test-Retest reliability of the spatial distribution of high frequency oscillations (HFO) in intracranial EEG

Purpose High frequency oscillations (HFO 80–500 Hz) are recognized as biomarkers for epileptogenic brain tissue to be resected in epilepsy surgery. We analysed here the test-retest reliability of HFO recorded at different times. Method We analyzed long-term invasive night recordings of 11 patients who subsequently had an area of the brain resected and achieved seizure freedom. Four patients had mesial temporal lobe epilepsy (TLE) and seven extratemporal epilepsy (ETLE). For each night, 30-min recordings were extracted during slow wave sleep, artifacts were removed and recordings were segmented into 5-min intervals. HFO were detected by a fully automated algorithm and contacts with the highest rate designated the HFO area for each interval. Results The resected area included the HFO area for all intervals in 3/4 TLE and 5/7 ETLE patients. The scalar product of the normalized rate vectors significantly exceeded a random distribution for every night pair for 4/4 TLE and 3/5 ETLE patients with multiple nights and for all interval pairs for 3/4 TLE and 5/7 ETLE patients. When averaging over all intervals, the resected area included the HFO area for 10/11 patients. Conclusion While the intervals have to be selected carefully from slow wave sleep, the HFO analysis provided high reliability within and between nights for the individual patient.

How to record high-frequency oscillations in epilepsy: A practical guideline.

Technology for localizing epileptogenic brain regions plays a central role in surgical planning. Recent improvements in acquisition and electrode technology have revealed that high-frequency oscillations (HFOs) within the 80-500 Hz frequency range provide the neurophysiologist with new information about the extent of the epileptogenic tissue in addition to ictal and interictal lower frequency events. Nevertheless, two decades after their discovery there remain questions about HFOs as biomarkers of epileptogenic brain and there use in clinical practice. In this review, we provide practical, technical guidance for epileptologists and clinical researchers on recording, evaluation, and interpretation of ripples, fast ripples, and very high-frequency oscillations. We emphasize the importance of low noise recording to minimize artifacts. HFO analysis, either visual or with automatic detection methods, of high fidelity recordings can still be challenging because of various artifacts including muscle, movement, and filtering. Magnetoencephalography and intracranial electroencephalography (iEEG) recordings are subject to the same artifacts. High-frequency oscillations are promising new biomarkers in epilepsy. This review provides interested researchers and clinicians with a review of current state of the art of recording and identification and potential challenges to clinical translation.

Effect of sampling rate and filter settings on High Frequency Oscillation detections.

High Frequency Oscillations (HFOs) are being studied as a biomarker of epilepsy, yet it is unknown how various acquisition parameters at different centers affect detection and analysis of HFOs. This paper specifically quantifies effects of sampling rate (FS) and anti-aliasing filter (AAF) positions on automated HFO detection. HFOs were detected on intracranial EEG recordings (17 patients) with 5kHz FS. HFO detection was repeated on downsampled and/or filtered copies of the EEG data, mimicking sampling rates and low-pass filter settings of various acquisition equipment. For each setting, we compared the HFO detection sensitivity, HFO features, and ability to identify the ictal onset zone. The relative sensitivity remained above 80% for either FS ⩾2kHz or AAF ⩾500Hz. HFO feature distributions were consistent (AUROC<0.7) down to 1kHz FS or 200Hz AAF. HFO rate successfully identified ictal onset zone over most settings. HFO peak frequency was highly variable under most parameters (Spearman correlation<0.5). We recommend at least FS ⩾2kHz and AAF ⩾500Hz to detect HFOs. Additionally, HFO peak frequency is not robust at any setting: the same HFO event can be variably classified either as a ripple (<200Hz) or fast ripple (>250Hz) under different acquisition settings. These results inform clinical centers on requirements to analyze HFO rates and features.

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.

ID 355 – Comparison of automatic and visual high frequency oscillations detection

Purpose To compare automatic and visual high frequency oscillation (HFO) detection. Methods We compared automatic and visual high frequency oscillations (HFO) detections in 5 patients. HFO were automatically detected by a custom made matlab detection algorithm. First, the signal in the statistical window (10 s) was filtered in a series of overlapping frequency bands. Power envelopes and the frequency stability between narrow band signal and broad band signal were computed. HFO detections were obtained by thresholding the normalized dot product of the power envelopes and frequency stability. To increase algorithm specificity amplitude, frequency stability and duration thresholds, previously obtained from HFOs marked by expert reviewer, were applied on putative HFO. This automatic detection was compared with visual HFO analysis. Results We reached excellent congruence between manual and automatic HFO detection measured by Cohen’s kappa ( ⊇ = 0.8 ). Conclusion We managed to prove excellent concordance between automatic and visual HFO detection.

53. Methods of high frequency oscillations detection: Advantages and disadvantages

Background High frequency oscillations (HFOs) represent new electrographic marker of epileptogenic tissue and they are considered as a surrogate marker of seizure onset and epileptogenic zones. HFOs are recorded mainly in intracranial recordings. Visual analysis of HFOs in long-term recordings is extremely difficult due to the low signal-to-noise ratio of HFOs. Successful integration of HFOs into presurgical evaluation requires development of reliable methods of automatic HFO detection and quantification. We aimed to examine performance of three new HFO detecting algorithms and compared their performance with published detectors. Methods We implemented three published detectors which utilize RMS, line length or Hilbert transform approach to detect HFOs. We have developed additional three types of detectors which utilize short time energy estimation, Hilbert envelope and Bayesian evidence. All HFO detecting algorithms were applied to gold standard datasets and their performance quantified. Results Line length and Hilbert detectors detected the highest number of HFOs. The lowest number of the detections was achieved by RMS and energy estimating detectors. According to the results, the detectors can be divided into two groups. One group is characterized by high sensitivity. These algorithms detect nearly all the labeled HFOs events, but suffer from the high false positive detection rate. Second group of detectors have high positive prediction value but lower sensitivity. Our Hilbert envelope detector demonstrated the best performance of all evaluated detectors. Conclusions To improve the performance of detectors with high sensitivity will require to develop additional post-processing steps to remove the majority of false detections. Meanwhile detectors with low sensitivity will detect only high-amplitude HFOs. Future selection of the most appropriate algorithm for HFO detection in intracranial recordings will require detail understanding of the clinical significance of low-amplitude HFOs and major sources of false positive detections. Supported by Grants from IGA NT11460, NT13357, NT14489, GACR 14-02634S and Neuron Fund (NFKJ 001/2012).