Circadian Dynamics of High Frequency Oscillations in Patients with

Epilepsy

Jirí Balach

, Petr Jezdik

1,2

, Radek Janca

, Roman Cmejla

, Pavel Krsek

, Petr Marusic

and Premysl Jiruska

Departement of Circuit Theory, Czech Technical University in Prague, Technická 2, Prague, Czech Republic

Department of Measurement, Czech Technical University in Prague, Technická 2, Prague, Czech Republic

Department of Pediatric Neurology, 2

Faculty of Medicine, Charles University in Prague, Motol University Hospital,

Prague, 150 06, Czech Republic

Department of Neurology, 2

Faculty of Medicine, Charles University in Prague, Motol University Hospital, Prague,

150 06, Czech Republic

Department of Developmental Epileptology, Institute of Physiology, The Czech Academy of Sciences, Prague, CZ-14220,

Czech Republic

Keywords: Intracerebral EEG, High-frequency Oscillations, Circadian Rhythms, Epilepsy, Seizure Onset Zone.

Abstract: High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to

localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity.

It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this

study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of

automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic

detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates

significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed

in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed

existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO

detection in the analysis of long-term intracranial recordings in humans. Obtained results can foster practical

implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease

human labour and increase the information yield of HFOs.

1 INTRODUCTION

High-frequency oscillations (HFOs) are sinus like

oscillations significantly rising above the background

in the frequency range above 80 Hz (Bragin et al.,

2002; Jacobs et al., 2008; Staba et al., 2002;

Urrestarazu et al., 2007). HFOs are divided into two

types according to their frequency profile.

Oscillations in range 80-200 Hz are classified as

ripples while oscillations over 200 Hz are called fast

ripples. HFOs represent a novel biomarker of

epileptogenic tissue with the potential to increase the

information yield of presurgical evaluations and to

improve the outcomes of epilepsy surgery.

Visual analysis of HFO in intracranial recordings

is a time consuming process. According to Zelmann

et al., (2012) it takes 10 hours of concentrated human

work to analyse 10 channel data with duration of 10

minutes. Visual review of long-term signals from a

hundreds of channels is virtually impossible in a

reasonable time period and it also suffers from human

bias. Successful implementation of HFOs analysis

into the clinical practice requires development of new

techniques of automatic HFOs detection which would

provide reliable information about HFO

spatiotemporal dynamics. Substantial number of

studies focused on the HFOs and their utilization in

presurgical examination evaluated only short-term

recordings. Only selected segments of invasive EEG

(iEEG) usually up to 10 minutes long were evaluated

in large number of HFO studies (Jacobs et al., 2010;

Kerber, 2013). In these studies HFOs were labelled

manually or semi-automatically using detection

algorithms for preselection of candidate HFO events

(Zelmann et al., 2012; Worrell, 2008; Crépon, 2010;

Staba et al., 2002; Cho et al., 2014).

284

Balach, J., Jezdik, P., Janca, R., Cmejla, R., Krsek, P., Marusic, P. and Jiruska, P.

Circadian Dynamics of High Frequency Oscillations in Patients with Epilepsy.

DOI: 10.5220/0005827602840289

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 284-289

ISBN: 978-989-758-170-0

Table 1: Summary of patient dataset information; *Original frequency, resampled to 512Hz.

Patient

number

Age /

Gender

MRI findings

Number of channels of

implanted electrodes

Sampling

frequency (Hz)

Record length

(hours.)

1 child/M FCD Ib 122 1000* 12

2 adult/F Normal 128 512 24.5

3 adult/F FCD susp. 128 512 22.5

4 child/M FCD IIb 71 1000* 22

5 adult/F FCD Ia 128 512 12

6 child/M FCD IIb 109 1000* 14.5

The advantage of algorithms of automated HFO

detection is their capability to analyse large amount

of data in a relatively short time. Recordings are

analysed with the same conditions and by the same

measures and obtained results are not biased. The

major downside of automated algorithms represents

inconsistent number of false detections.

Several studies have demonstrated that spatial

distribution of HFOs correlates with the localization

of seizure onset zone (Bagshaw et al., 2009; Brázdil

et al., 2010; Jacobs et al., 2009, 2008; Urrestarazu et

al., 2007). Moreover, resection of HFO generating

regions has been associated with better surgical

outcomes (Akiyama et al., 2011; Cho et al., 2014;

Fujiwara et al., 2012; Haegelen et al., 2013; Sakuraba

et al., 2015). Long-term HFOs dynamics and

spatiotemporal profile are, however, modulated by

several factors like levels of inhibition, vigilance and

by sleep. It has been shown that HFO rate increases

during slow wave sleep (Clemens et al., 2007 ,

Bagshaw et al., 2009) but individual stages of NREM

sleep (NREM1-NREM4) do not influence HFO rates.

Ripples and the fast ripples were confined to SOZ

during NREM. Another study demonstrated that good

surgical outcome was achieved, if the resection

involved brain tissue where ripples occurred during

REM stage (Sakuraba et al., 2015).

These long-term changes in HFO properties must

be taken in account in analysis of HFOs and

interpretation of the results. In the current study, we

aimed to examine the long-term HFO dynamics using

automatic HFO detector (Balach et al., 2014).

2 DATA & METHODS

2.1 Database

We analyzed long-term recordings from three

paediatric and three adult patients implanted with

subdural and/or depth electrodes as a part of the

presurgical examination (Table 1.). Signals were

sampled at 512 Hz (adults) and 1 kHz (children).

High sampling rate signals were resampled to 512 Hz.

The average length of recordings was 17.9 ± 5.7

hours. Each dataset contained 114 ± 22 contacts.

Electrode contacts inside the SOZ were marked by

experienced neurologists. The SOZ was defined as

the area of the brain with the earliest occurrence of

ictal discharges (Litt et al., 2001; Marsh et al., 2010;

Thornton et al., 2011). Research procedures and data

collection were approved by the institutional ethical

committee and patient or parent informed consent

was obtained.

2.2 Methodology

HFO detector was applied to recorded data. The HFO

detection algorithm is based on the dynamical

thresholding of short-time energy changes, followed

by calculation of the number of cycles within the

detected HFO event and identification of peak

frequency within HFO frequency bands (Balach et al.,

2014). To obtain average HFO rate per minute we

used 5-minute sliding window with 80% overlap. Due

to high inter-patient variance of HFO rate, the rate

was normalized by maximal rate observed in each

dataset. Evaluation of circadian changes in HFO

properties required identification of sleep and

wakefulness. Because standard polysomnographic

(PSG) signals (scalp EEG, EOG and EMG) could not

be recorded during invasive monitoring, we

determined the sleep and wakefulness indirectly from

iEEG. First, signals with frequent interictal

epileptiform discharges were removed. The

discharges were detected using highly sensitive spike

detector (Janca et al., 2014).Channels with interictal

epileptiform discharge rate higher than the first

quartile were excluded. Selected data were band-pass

filtered in 2-15 Hz, segmented by 3.5 minutes

window with 70 % overlap. For each data segment we

calculated mean energy and signal zero crossing

frequency. To minimize the energetic and spectral

impact of artefacts, both parameters were normalised

by their 99

percentiles and recalculated to a PSG

parameter. This parameters represents frequency to

energy ratio, eq. 1.

Circadian Dynamics of High Frequency Oscillations in Patients with Epilepsy

285

Figure 1: HFO rates in a one-day cycle in patient 3. Awake and sleeping stages were set manually according to HFO rates,

daytime and PSG parameter. A) Mean HFO rate during day/night cycle. B, C) Example of HFO rates from selected brain

structures. Electrode contacts were assigned to anatomical structures according to anatomical atlas (Eickhoff et al., 2005).

Assignments were made manually from the CT/MRI co-registered images. D) Temporal profile of PSG parameter.

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(1)

During the NREM sleep, the normalised

frequency decreases towards the lower frequencies

from delta frequency band while the normalised

energy increases. PSG values drop during the NREM

stage and increase during REM sleep and

wakefulness.

We manually identified wakefulness, REM and

NREM from the PSG parameter. Due to difficult

differentiation between REM sleep and awake states

we classified them as a single awake+REM state.

Utilizing automatic HFO detector we aimed to

address following questions:

1) Does HFO rate varies between awake+REM

and NREM states?

2) Does HFO rate in SOZ and outside display

different circadian dynamics?

3) Does HFO rate correlates with localization of

SOZ?

3 RESULTS

In total 833,199 HFO events were detected in 107.5

hours of iEEG data from all six patients. The

normalised HFOs rates were significantly higher

during NREM than in awake+REM state in 5/6

patients (p<0.05, Wilcoxon’s test, Figure 2).

Figure 2: HFO rates in NREM and Awake+REM states in

each patient (*p<0.05; **p<0.01; ***p<0.001). Patient 2

p=0.1255.

HFO rates during Awake+REM state did not

differ inside and outside SOZ. However, in NREM

state HFO rates in SOZ significantly increased

(p<0.001, Wilcoxon’s test, Figure 3).

The most HFO active regions overlapped with the

SOZ. Better overlap was observed during NREM

(62.8±40%) than during awake+REM state

(44.8±33.7%).

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286

Figure 3: HFO rates significantly increased inside SOZ

during NREM stages. (***p<0.001).

Table 2: Localization ability of channels with HFO

maximal rates ( > 75 percentile).

Patient

Number of

channels in 4

quartile

In/Total SOZ

contacts in

awake/REM

In/Total SOZ

contacts in

NREM

1 28 3/16 (19%) 6/16 (38%)

2 29 0/11 (0%) 0/11 (0%)

3 27 17/23 (74%) 20/23 (87%)

4 16 5/19 (26%) 10/19 (52%)

5 16 3/4 (75%) 4/4 (100%)

6 27 3/4 (75%) 4/4 (100%)

In patient 2 we analyzed 14 days of iEEG

recording to evaluate reliability of automated HFO

analysis over very long time period (Figure 4).

Processing of the whole dataset took 130 hours and

4,393,892 HFO events were detected. According to

Zelmann et al., (2012), visual review of such dataset

from HFO perspective would take approximately

258048 hours (~29 years) of human work. We

observed the dynamical changes of HFO occurrence

from the start of the recording. The HFO rate was

stable in majority of brain structures and displayed

circadian fluctuations. However, in left middle

temporal gyrus HFO rates were progressively

increasing. After day 8, the HFO rates were stable in

all studied structures, but the quality of signal

deteriorated due to increased number artefacts

leading to higher number of false detections.

4 DISCUSSION

In this study we have demonstrated reliability of

automatic HFO detection in the analysis of long-term

intracranial recordings in humans. Utilization of

automatic detectors is able to reveal circadian and

long-term dynamics of HFO rate. Obtained results

can foster practical implementation of automatic

HFO detecting algorithms into presurgical

examination, dramatically decrease human labour

and increase the information yield of HFOs (Zelmann

et al., 2012).

This study demonstrates the importance of

understanding of long-term spatiotemporal dynamics

of HFO rates for appropriate interpretation of the

obtained results. Dynamical changes in HFO rates

during NREM sleep inside and outside can provide

better localizing information about SOZ than

recordings obtained during the REM sleep or

wakefulness (Clemens et al., 2007; Bagshaw et al.,

2009; Sakuraba et al., 2015).

Figure 4: HFO rate analysis of continuous 14 day recordings. Mean HFO rate across all implanted electrodes and rates from

electrodes which are inside three chosen anatomy structures. Dashed vertical lines are marks of midnights.

Circadian Dynamics of High Frequency Oscillations in Patients with Epilepsy

287

Application of the analysis to data with very long

duration revealed fast dynamical changes of HFO rate

in respect to circadian rhythms, but also slow

ultradian changes which may reflect various

phenomena like effect of anaesthesia, changes

medication, changes in neurotransmitter and

neuromodulator systems, propensity to generate

seizures and tissue response to implanted electrodes

(Haut, 2006; Zijlmans et al., 2009). Future studies

focused on HFOs will be required to gain insight into

the mechanisms responsible for long-term changes in

HFOs dynamics.

Implemented method of sleep and wakefulness

estimation from iEEG records is not optimal and must

be also considered when interpreting result of the

current study. Combination of EOG and EMG

channels with iEEG may substantially increase the

specificity of the PSG parameter to discriminate each

brain states.

ACKNOWLEDGEMENTS

This study was supported by the grants,

SGS15/198/OHK3/3T/13, the Ministry of Health of

the Czech Republic (NT/13357, NT/14489-3), AZV

15-29835A), Neuron Fund for Support of Science

(001/2012) and the Czech Science Foundation

(GACR 14-02634S).

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