Unsupervised Detection of High-Frequency Oscillations Using Time-Frequency Maps and Computer Vision

AimRipple-band epileptic high-frequency oscillations (HFOs) can be recorded by scalp electroencephalography (EEG), and tend to be associated with epileptic spikes. However, there is a concern that the filtration of steep waveforms such as spikes may cause spurious oscillations or “false ripples.” We excluded such possibility from at least some ripples by EEG differentiation, which, in theory, enhances high-frequency signals and does not generate spurious oscillations or ringing.MethodsThe subjects were 50 pediatric patients, and ten consecutive spikes during sleep were selected for each patient. Five hundred spike data segments were initially reviewed by two experienced electroencephalographers using consensus to identify the presence or absence of ripples in the ordinary filtered EEG and an associated spectral blob in time-frequency analysis (Session A). These EEG data were subjected to numerical differentiation (the second derivative was denoted as EEG″). The EEG″ trace of each spike data segment was shown to two other electroencephalographers who judged independently whether there were clear ripple oscillations or uncertain ripple oscillations or an absence of oscillations (Session B).ResultsIn Session A, ripples were identified in 57 spike data segments (Group A-R), but not in the other 443 data segments (Group A-N). In Session B, both reviewers identified clear ripples (strict criterion) in 11 spike data segments, all of which were in Group A-R (p < 0.0001 by Fisher’s exact test). When the extended criterion that included clear and/or uncertain ripples was used in Session B, both reviewers identified 25 spike data segments that fulfilled the criterion: 24 of these were in Group A-R (p < 0.0001).DiscussionWe have demonstrated that real ripples over scalp spikes exist in a certain proportion of patients. Ripples that were visualized consistently using both ordinary filters and the EEG″ method should be true, but failure to clarify ripples using the EEG″ method does not mean that true ripples are absent.ConclusionThe numerical differentiation of EEG data provides convincing evidence that HFOs were detected in terms of the presence of such unusually fast oscillations over the scalp and the importance of this electrophysiological phenomenon.

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Classification of High Frequency Oscillations in intracranial EEG signals based on coupled time-frequency and image-related features

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.103418 ◽

2022 ◽

Vol 73 ◽

pp. 103418

Author(s):

Fatma Krikid ◽

Ahmad Karfoul ◽

Sahbi Chaibi ◽

Amar Kachenoura ◽

Anca Nica ◽

...

Keyword(s):

High Frequency ◽

Eeg Signals ◽

Intracranial Eeg ◽

Time Frequency ◽

High Frequency Oscillations

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Motoneuron firing patterns underlying fast oscillations in phrenic nerve discharge in the rat

Journal of Neurophysiology ◽

10.1152/jn.00292.2012 ◽

2012 ◽

Vol 108 (8) ◽

pp. 2134-2143 ◽

Cited By ~ 24

Author(s):

Vitaliy Marchenko ◽

Michael G. Z. Ghali ◽

Robert F. Rogers

Keyword(s):

High Frequency ◽

Low Frequency ◽

Medium Frequency ◽

Adult Rats ◽

Time Frequency ◽

High Frequency Oscillations ◽

Novel Method ◽

Frequency Coherence ◽

Fast Oscillations ◽

First Time

Fast oscillations are ubiquitous throughout the mammalian central nervous system and are especially prominent in respiratory motor outputs, including the phrenic nerves (PhNs). Some investigators have argued for an epiphenomenological basis for PhN high-frequency oscillations because phrenic motoneurons (PhMNs) firing at these same frequencies have never been recorded, although their existence has never been tested systematically. Experiments were performed on 18 paralyzed, unanesthetized, decerebrate adult rats in which whole PhN and individual PhMN activity were recorded. A novel method for evaluating unit-nerve time-frequency coherence was applied to PhMN and PhN recordings. PhMNs were classified according to their maximal firing rate as high, medium, and low frequency, corresponding to the analogous bands in PhN spectra. For the first time, we report the existence of PhMNs firing at rates corresponding to high-frequency oscillations during eupneic motor output. The majority of PhMNs fired only during inspiration, but a small subpopulation possessed tonic activity throughout all phases of respiration. Significant time-varying PhMN-PhN coherence was observed for all PhMN classes. High-frequency, early-recruited units had significantly more consistent onset times than low-frequency, early/middle-recruited and medium-frequency, middle/late-recruited PhMNs. High- and medium-frequency PhMNs had significantly more consistent offset times than low-frequency units. This suggests that startup and termination of PhMNs with higher firing rates are more precisely controlled, which may contribute to the greater PhMN-PhN coherence at the beginning and end of inspiration. Our findings provide evidence that near-synchronous discharge of PhMNs firing at high rates may underlie fast oscillations in PhN discharge.

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Selective loss of high-frequency oscillations in phrenic and hypoglossal activity in the decerebrate rat during gasping

AJP Regulatory Integrative and Comparative Physiology ◽

10.1152/ajpregu.00217.2006 ◽

2006 ◽

Vol 291 (5) ◽

pp. R1414-R1429 ◽

Cited By ~ 13

Author(s):

Vitaliy Marchenko ◽

Robert F. Rogers

Keyword(s):

Power Distribution ◽

High Frequency ◽

Spectral Characteristics ◽

Low Frequency ◽

High Amplitude ◽

Medial Branch ◽

Adult Rats ◽

Time Frequency ◽

High Frequency Oscillations ◽

Nerve Recordings

Respiratory motor outputs contain medium-(MFO) and high-frequency oscillations (HFO) that are much faster than the fundamental breathing rhythm. However, the associated changes in power spectral characteristics of the major respiratory outputs in unanesthetized animals during the transition from normal eupneic breathing to hypoxic gasping have not been well characterized. Experiments were performed on nine unanesthetized, chemo- and barodenervated, decerebrate adult rats, in which asphyxia elicited hyperpnea, followed by apnea and gasping. A gated fast Fourier transform (FFT) analysis and a novel time-frequency representation (TFR) analysis were developed and applied to whole phrenic and to medial branch hypoglossal nerve recordings. Our results revealed one MFO and one HFO peak in the phrenic output during eupnea, where HFO was prominent in the first two-thirds of the burst and MFO was prominent in the latter two-thirds of the burst. The hypoglossal activity contained broadband power distribution with several distinct peaks. During gasping, two high-amplitude MFO peaks were present in phrenic activity, and this state was characterized by a conspicuous loss in HFO power. Hypoglossal activity showed a significant reduction in power and a shift in its distribution toward lower frequencies during gasping. TFR analysis of phrenic activity revealed the increasing importance of an initial low-frequency “start-up” burst that grew in relative intensity as hypoxic conditions persisted. Significant changes in MFO and HFO rhythm generation during the transition from eupnea to gasping presumably reflect a reconfiguration of the respiratory network and/or alterations in signal processing by the circuitry associated with the two motor pools.

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

International Journal of Neural Systems ◽

10.1142/s0129065718500016 ◽

2018 ◽

Vol 28 (07) ◽

pp. 1850001 ◽

Cited By ~ 11

Author(s):

Lucia Rita Quitadamo ◽

Roberto Mai ◽

Francesca Gozzo ◽

Veronica Pelliccia ◽

Francesco Cardinale ◽

...

Keyword(s):

High Frequency ◽

Computational Time ◽

Seizure Onset ◽

Time Frequency ◽

High Frequency Oscillations ◽

Epileptiform Discharges ◽

Seizure Onset Zone ◽

Potential Biomarker ◽

Eeg Data

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%.

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Time-frequency analysis reveals decreased high-frequency oscillations in writer's cramp

Brain ◽

10.1093/brain/awl259 ◽

2006 ◽

Vol 130 (1) ◽

pp. 198-205 ◽

Cited By ~ 20

Author(s):

Z. Cimatti ◽

D. P Schwartz ◽

F. Bourdain ◽

S. Meunier ◽

J.-P. Bleton ◽

...

Keyword(s):

Frequency Analysis ◽

High Frequency ◽

Time Frequency Analysis ◽

Time Frequency ◽

Writer’S Cramp ◽

High Frequency Oscillations ◽

Writer's Cramp

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Human Intracranial High Frequency Oscillations (HFOs) Detected by Automatic Time-Frequency Analysis

PLoS ONE ◽

10.1371/journal.pone.0094381 ◽

2014 ◽

Vol 9 (4) ◽

pp. e94381 ◽

Cited By ~ 79

Author(s):

Sergey Burnos ◽

Peter Hilfiker ◽

Oguzkan Sürücü ◽

Felix Scholkmann ◽

Niklaus Krayenbühl ◽

...

Keyword(s):

Frequency Analysis ◽

High Frequency ◽

Time Frequency Analysis ◽

Time Frequency ◽

High Frequency Oscillations

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Pathological and physiological high-frequency oscillations in focal human epilepsy

Journal of Neurophysiology ◽

10.1152/jn.00341.2013 ◽

2013 ◽

Vol 110 (8) ◽

pp. 1958-1964 ◽

Cited By ~ 110

Author(s):

Andrew Matsumoto ◽

Benjamin H. Brinkmann ◽

S. Matthew Stead ◽

Joseph Matsumoto ◽

Michal T. Kucewicz ◽

...

Keyword(s):

High Frequency ◽

Motor Task ◽

High Amplitude ◽

Spectral Amplitude ◽

Support Vector ◽

Frequency Spectra ◽

Time Frequency ◽

Human Epilepsy ◽

High Frequency Oscillations ◽

Measuring Frequency

High-frequency oscillations (HFO; gamma: 40–100 Hz, ripples: 100–200 Hz, and fast ripples: 250–500 Hz) have been widely studied in health and disease. These phenomena may serve as biomarkers for epileptic brain; however, a means of differentiating between pathological and normal physiological HFO is essential. We categorized task-induced physiological HFO during periods of HFO induced by a visual or motor task by measuring frequency, duration, and spectral amplitude of each event in single trial time-frequency spectra and compared them to pathological HFO similarly measured. Pathological HFO had higher mean spectral amplitude, longer mean duration, and lower mean frequency than physiological-induced HFO. In individual patients, support vector machine analysis correctly classified pathological HFO with sensitivities ranging from 70–98% and specificities >90% in all but one patient. In this patient, infrequent high-amplitude HFO were observed in the motor cortex just before movement onset in the motor task. This finding raises the possibility that in epileptic brain physiological-induced gamma can assume higher spectral amplitudes similar to those seen in pathologic HFO. This method if automated and validated could provide a step towards differentiating physiological HFO from pathological HFO and improving localization of epileptogenic brain.

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