Ing. Radek Janča, Ph.D.

Visuospatial perspective-taking (VPT) is the ability to imagine a scene from a position different from the one used in self-perspective judgments (SPJ). We typically use VPT to understand how others see the environment. VPT requires overcoming the self-perspective, and impairments in this process are implicated in various brain disorders, such as schizophrenia and autism. However, the underlying brain areas of VPT are not well distinguished from SPJ-related ones and from domain-general responses to both perspectives. In addition, hierarchical processing theory suggests that domain-specific processes emerge over time from domain-general ones. It mainly focuses on the sensory system, but outside of it, support for this hypothesis is lacking. Therefore, we aimed to spatiotemporally distinguish brain responses domain-specific to VPT from the specific ones to self-perspective, and domain-general responses to both perspectives. In particular, we intended to test whether VPT- and SPJ specific responses begin later than the general ones. We recorded intracranial EEG data from 30 patients with epilepsy who performed a task requiring laterality judgments during VPT and SPJ, and analyzed the spatiotemporal features of responses in the broad gamma band (50–150 Hz). We found VPT-specific processing in a more extensive brain network than SPJ-specific processing. Their dynamics were similar, but both differed from the general responses, which began earlier and lasted longer. Our results anatomically distinguish VPT-specific from SPJ-specific processing. Furthermore, we temporally differentiate between domain-specific and domain-general processes both inside and outside the sensory system, which serves as a novel example of hierarchical processing.

Introduction: The quality of fluorodeoxyglucose positron emission tomography (FDG-PET) images is limited by low effective resolution, which leads to blurring. The resolution can be additionally improved by partial volume correction (PVC) algorithms. The key input parameter influencing PVC performance is the correct specification of full width at half maximum (FWHM) of the scanner point spread function (PSF). The FWHM is standardly measured using a point source phantom under the standard protocol defined by the National Electrical Manufacturers Association (NEMA). However, the PSF varies in real conditions of human/animal neuroimaging and must be estimated individually. Therefore, a new method for fully automatic estimation of FWHM from FDG-PET images of the brain was proposed without the need for information on the scanner and protocol used. Methods: The method was based on estimating the parameters of edge spread function at the border of the gray matter from the FDG-PET image intensity function. The FWHM estimation algorithm was applied to FDG-PET brain images of 31 candidates for surgical epilepsy treatment in Motol University Hospital. The estimates were compared with FWHM measured according to NEMA standard on the same PET-CT scanners (Siemens Biograph mCT: 16 patients, Siemens Biograph Vision: 15 patients). Results: The median (interquartile range) value of estimated FWHM was 4 (3.9 - 4.1) mm, which was 11% lower than measured 4.5 mm for Siemens Biograph mCT scanner in transversal direction, and 6% lower 3.4 (3.3 -3.6) mm vs. measured 3.2 mm for Biograph Vision scanner. In the axial direction, estimated value 4.7 (4.5 - 5.2) mm was the same as the measured 4.7 mm for the Siemens Biograph mCT scanner, and 6% higher 3.6 (3.4 -3.8) mm vs. measured 3.4 mm for the Siemens Biograph Vision scanner. Conclusion: The proposed method can fully automatically estimate FWHM of PET scanner PSF only based on FDG-PET image. The estimates agreed with FWHM measured according to NEMA standard. FWHM estimation improves the performance of PVC algorithms that increase the resolution of FDG-PET images for the glucose metabolism assessment.

Early implantable epilepsy therapy devices provided open-loop electrical stimulation without brain sensing, computing, or an interface for synchronized behavioral inputs from patients. Recent epilepsy stimulation devices provide brain sensing but have not yet developed analytics for accurately tracking and quantifying behavior and seizures. Here we describe a distributed brain co-processor providing an intuitive bi-directional interface between patient, implanted neural stimulation and sensing device, and local and distributed computing resources. Automated analysis of continuous streaming electrophysiology is synchronized with patient reports using a hand-held device and integrated with distributed cloud computing resources for quantifying seizures, interictal epileptiform spikes, and patient symptoms during therapeutic electrical brain stimulation. The classification algorithms for interictal epileptiform spikes and seizures were developed and parameterized using long-term ambulatory data from 9 humans and 8 canines with epilepsy, and then implemented prospectively in out-of-sample testing in 2 pet canines and 4 humans with drug resistant epilepsy living in their natural environments. Accurate seizure diaries are needed as the primary clinical outcome measure of epilepsy therapy and to guide brain stimulation optimization. The brain co-processor system described here enables tracking interictal epileptiform spikes, seizures, and correlation with patient behavioral reports. In the future correlation of spikes and seizures with behavior will allow more detailed investigation of the clinical impact of spikes and seizures on patients.

An ischemic stroke is a local lesion that disrupts the large-scale structural and functional connectivity of the brain. Although local, the ischemic stroke often leads to deficits in cognitive functions which can’t be explained by local brain damage. It is believed that stroke-induced large-scale network alteration represents the mechanisms responsible for a decline in cognitive functions which are dependent on large-scale integration. To gain insight into the pathophysiological principles of how a local lesion results in a global cognitive decline requires a reliable and robust algorithm that can quantify the relationship between cognitive functions and network properties. In this study, we have developed, optimized, and tested a processing pipeline to parameterize complex neuropsychological evaluation and determine the functional connectivity from high-density EEG recordings. The developed algorithm was applied on a cohort of 27 patients who suffered a stroke and who were underwent cognitive examinations and high-density EEG monitoring one and two years after the stroke. The developed automatic algorithm demonstrated that it can reliably estimate functional connectivity and that it is robust against the physiological and technical artifacts. The proposed processing pipeline allows an unbiased and quantitative characterization of cognitive performance and its comparison with functional connectivity alterations.

Networked optical wireless communication, also denoted as LiFi, is expected to play an important role in so-called smart hospitals. In this paper, we present the first experimental study of LiFi in an operating room at Motol University Hospital in Prague, Czech Republic. First, we perform one-to-one measurements using an optical transmitter (Tx) and receiver (Rx) and observe that channels with a free LOS provide sufficient signal strength for mobile communication inside the operating room. Then we combine the individual LOS links into a multiple-input multiple-output (MIMO) link using four distributed transmitters representing a wireless infrastructure, and six distributed receivers representing medical devices. In this configuration, at least two strong singular values of the MIMO channel matrix are observed which allow spatial multiplexing. By appropriately clustering the transmitters and selecting the users, mobile devices can be served in parallel. For data transmission, several multiplexing schemes such as time-division multiple access (TDMA) with one and two best links, TDMA with spatial reuse, space-division multiple access (SDMA) with two best links with and without zero forcing (ZF) are considered. Results show that SDMA with ZF increases the data rate by 2.7 times compared to baseline TDMA, resulting in a total data rate of 600 Mbit/s. CCBY

Objective Epilepsy surgery fails in >30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis. Methods The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation. Results Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p<0.005) – ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p<0.012). Conclusion The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone. Significance Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.

Object Epilepsy surgery is an effective treatment for selected patients with focal intractable epilepsy. Complete removal of the epileptogenic zone significantly increases the chances for postoperative seizure-freedom. In complex surgical candidates, delineation of the epileptogenic zone requires a long-term invasive video/EEG from intracranial electrodes. It is especially challenging to achieve a complete resection in deep brain structures such as opercular-insular cortex. We report a novel approach utilizing intraoperative visual detection of stereotactically implanted depth electrodes to inform and guide the extent of surgical resection. Methods We retrospectively reviewed data of pediatric patients operated in Motol Epilepsy Center between October 2010 and June 2020 who underwent resections guided by intraoperative visual detection of depth electrodes following SEEG. The outcome in terms of seizure- and AED-freedom was assessed individually in each patient. Results Nineteen patients (age at surgery 2.9–18.6 years, median 13 years) were included in the study. The epileptogenic zone involved opercular-insular cortex in eighteen patients. The intraoperative detection of the electrodes was successful in seventeen patients and the surgery was regarded complete in sixteen. Thirteen patients were seizure-free at final follow-up including six drug-free cases. The successful intraoperative detection of the electrodes was associated with favorable outcome in terms of achieving complete resection and seizure-freedom in most cases. On the contrary, the patients in whom the procedure failed had poor postsurgical outcome. Conclusion The reported technique helps to achieve the complete resection in challenging patients with the epileptogenic zone in deep brain structures.

The thermal effect of a novel effective electrical stimulation mapping (ESM) technique using an Ojemann’s stimulation electrode in open craniotomy areas causes a nondestructive local increase in temperature. Another type of stimulating electrode is a subdural strip, routinely used in intraoperative electrocorticography (ECoG), which applies ESM in a covered subdural area over the motor cortex. ECoG electrode geometry produces a different electrical field, causing a different Joule heat distribution in tissue, one that is impossible to this date to measure in subdural space. Therefore, the previous safety control study of the novel ESM technique needed to be extended to include an assessment of the thermal effect of ECoG strip electrodes. We adapted a previously well-validated numerical model and performed coupled complex electro-thermal transient simulations for short-time (28.4 ms) high-frequency (500 Hz) and hyperintense (peak 100 mA) ESM paradigm. The risk of heat-induced cellular damage was assessed by applying the Arrhenius equation integral on the computed time-dependent spatial distribution of temperature in the brain tissue during ESM stimulation and during the cooldown period. The results showed increases in temperature in the proximity around ECoG electrode discs in a safe range without destructive effects. As opposed to open craniotomy, subdural space is not cooled by the air; hence a higher – but still safe – induced temperature was observed. The presented simulation agrees with the previously published histopathological examination of the stimulated brain tissue, and confirms the safety of the novel ESM technique when applied using ECoG strip electrodes.

OBJECTIVE: Resective epilepsy surgery is an established treatment method for children with focal intractable epilepsy, but the use of this method introduces the risk of postsurgical motor deficits. Electrical stimulation mapping (ESM), used to define motor areas and pathways, frequently fails in children. The authors developed and tested a novel ESM protocol in children of all age categories. METHODS: The ESM protocol utilizes high-frequency electric cortical stimulation combined with continuous intraoperative motor-evoked potential (MEP) monitoring. The relationships between stimulation current intensity and selected presurgical and surgery-associated variables were analyzed in 66 children (aged 7 months to 18 years) undergoing 70 resective epilepsy surgeries in proximity to the motor cortex or corticospinal tracts. RESULTS: ESM elicited MEP responses in all children. Stimulation current intensity was associated with patient age at surgery and date of surgery (F value = 6.81, p < 0.001). Increase in stimulation current intensity predicted postsurgical motor deficits (F value = 44.5, p < 0.001) without effects on patient postsurgical seizure freedom (p > 0.05). CONCLUSIONS: The proposed ESM paradigm developed in our center represents a reliable method for preventing and predicting postsurgical motor deficits in all age groups of children. This novel ESM protocol may increase the safety and possibly also the completeness of epilepsy surgery. It could be adopted in pediatric epilepsy surgery centers.

We present LiFi channel measurements in a neurosurgery room of Motol University Hospital in Prague. Individual channels are combined into a virtual multiuser MIMO link. We report achievable data rates for different LiFi transmission schemes.

Human perception and cognition are based predominantly on visual information processing. Much of the information regarding neuronal correlates of visual processing has been derived from functional imaging studies, which have identified a variety of brain areas contributing to visual analysis, recognition, and processing of objects and scenes. However, only two of these areas, namely the parahippocampal place area (PPA) and the lateral occipital complex (LOC), were verified and further characterized by intracranial electroencephalogram (iEEG). iEEG is a unique measurement technique that samples a local neuronal population with high temporal and anatomical resolution. In the present study, we aimed to expand on previous reports and examine brain activity for selectivity of scenes and objects in the broadband high-gamma frequency range (50–150 Hz). We collected iEEG data from 27 epileptic patients while they watched a series of images, containing objects and scenes, and we identified 375 bipolar channels responding to at least one of these two categories. Using K-means clustering, we delineated their brain localization. In addition to the two areas described previously, we detected significant responses in two other scene-selective areas, not yet reported by any electrophysiological studies; namely the occipital place area (OPA) and the retrosplenial complex. Moreover, using iEEG we revealed a much broader network underlying visual processing than that described to date, using specialized functional imaging experimental designs. ...

This paper aims to employ numerical simulations to assess the risk of cellular damage during the application of a novel paradigm of electrical stimulation mapping (ESM) used in neurosurgery. The core principle of the paradigm is the use of short, high-intensity and high-frequency stimulation pulses. We developed a complex numerical model and performed coupled electro-thermal transient simulations. The model was optimized by incorporating ESM electrodes’ resistance obtained during multiple intraoperative measurements and validated by comparing them with the results of temperature distribution measurement acquired by thermal imaging. The risk of heatinduced celluar damage was assessed by applying the Arrhenius equation integral on the computed time-dependent spatial distribution of temperature in the brain tissue. Our results suggest that the impact of the temperature increase during our novel ESM paradigm is non-destructive and thus prove that it is safe. The presented simulation results match the previously published thermographic measurement and histopathological examination of the stimulated brain tissue, and confirm the safety of novel ESM.

A standard procedure for continuous intraoperative monitoring of the integrity of the corticospinal tracts by eliciting muscle responses is the electric stimulation mapping (ESM). However, standard ESM protocols are ineffective in 20% of young children. We have developed a novel, highly efficient paradigm consisting of short-time burst (30 ms) of high frequency (500 Hz) and high peak current (≤100 mA), which may cause local tissue overheating. The presented safety control study was therefore designed. The infrared thermography camera captured to-be-resected cortex of 13 patients in vivo during ESM. Thermograms were image processed to reveal discrete ESM thermal effect of currents from 10 to 100 mA. Peak 100 mA currents induced a maximal increase in temperature of 3.1 °C, 1.23±0.72 °C in average. The warming correlated with stimulating electrode resistance ( p<0.001 ). The measurement uncertainty was estimated ± 1.01 ºC for the most skeptical conditions. The histopathological evaluation of stimulated tissue (performed in all cases) did not show any destructive changes. Our study demonstrates the ability of the thermographic camera to measure the discrete thermal effect of the ESM. The results provide evidence for the safety of the proposed protocol for full range currents with minimal risk of brain tissue damage.

The mechanisms of seizure emergence, and the role of brief interictal epileptiform discharges (IEDs) in seizure generation are two of the most important unresolved issues in modern epilepsy research. Our study shows that the transition to seizure is not a sudden phenomenon, but a slow process characterized by the progressive loss of neuronal network resilience. From a dynamical perspective, the slow transition is governed by the principles of critical slowing, a robust natural phenomenon observable in systems characterized by transitions between dynamical regimes. In epilepsy, this process is modulated by the synchronous synaptic input from IEDs. IEDs are external perturbations that produce phasic changes in the slow transition process and exert opposing effects on the dynamics of a seizure-generating network, causing either anti-seizure or pro-seizure effects. We show that the multifaceted nature of IEDs is defined by the dynamical state of the network at the moment of the discharge occurrence.

Between seizures irritative network generates frequent brief synchronous activity, which manifests on the EEG as interictal epileptiform discharges (IEDs). Recent insights into the mechanism of IEDs at the microscopic level have demonstrated a high variance in the recruitment of neuronal populations generating IEDs and a high variability in the trajectories through which IEDs propagate across the brain. These phenomena represent one of the major constraints for precise characterization of network organization and for the utilization of IEDs during presurgical evaluations. We have developed a new approach to dissect human neocortical irritative networks and quantify their properties. We have demonstrated that irritative network has modular nature and it is composed of multiple independent sub-regions, each with specific IED propagation trajectories and differing in the extent of IED activity generated. The global activity of the irritative network is determined by long-term and circadian fluctuations in sub-region spatiotemporal properties. Also, the most active sub-region co-localizes with the seizure onset zone in 12/14 cases. This study demonstrates that principles of recruitment variability and propagation are conserved at the macroscopic level and that they determine irritative network properties in humans. Functional stratification of the irritative network increases the diagnostic yield of intracranial investigations with the potential to improve the outcomes of surgical treatment of neocortical epilepsy.

The cortical Electric Stimulation Mapping (ESM) procedure is used as a standard approach to localize and continuously monitor function of the eloquent cortex and corticospinal tract during neurosurgical intervention. However, eliciting motor responses using standard ESM paradigm is frequently difficult to young children. We have thus developed and tested a novel EMS protocol, which uses intense, high frequency and short stimulation pulses. However, the intense stimulation peak-peak current (up to 100 mA) possess the potential risk of tissue damage.The thermographic measurement was performed in four selected patients in vivo using the high-resolution thermographic camera during resective epilepsy surgery to verify the safety of the novel EMS paradigm. The EMS paradigm was systematically tested for pulse currents gradually increased from 10 to 100 mA. A moving thermographic picture was stabilized and emissivity was corrected for each pixel to reach the correct temperature interpretation. The results show a local temperature increase in the brain tissue close to the stimulation electrode during the ESM with current intensity above 40 mA. The 100 mA current caused the maximal temperature increase +0.4 °C. This value added to patient basal temperature is far under safety level 39 °C. Although the temperature increase observed around the stimulating electrode during our ESM paradigm is very low, we are aware that the borderline between electrode and cortex could not be reliably measured. Estimation of the electrical current density and the temperature distribution must be modeled using 3D numerical simulations and compared with the thermographic measurement in future work.

Intraoperative electrical stimulation mapping is used to localization and control of eloquent cortex and tracts condition during surgery intervention, however standard protocol is ineffective in pediatric patients. Therefore, the novel paradigm with higher amplitudes of stimulating currents must be applied. A 3D numerical model verified thermal effects of the stimulation to brain tissues. Numerical results of temperature distribution were compared to measurements.

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

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector’s specificity and having important implications for future development of spike detectors in general.

Cílem chirurgické léčby epilepsie je odstranění mozkové tkáně představující tzv. epileptogenní zónu (EZ), která je primárně zodpovědná za vznik záchvatů. U řady pacientů je k přesnému určení EZ nezbytná dlouhodobá monitorace intrakraniálního EEG pomocí nitrolebních elektrod. Hranice EZ je následně stanovena průnikem výsledků neurozobrazovacích vyšetření a hodnocením intrakraniálního EEG. Výskyt epileptiformních grafoelementů v EEG v období mezi záchvaty je jedním z ukazatelů lokalizace epileptogenní tkáně. Kvantifikace epileptiformní aktivity (qEEG) v jednotlivých kanálech napomáhá lokalizaci a zpřesnění hranice EZ. Přesná prostorová interpretace výsledků automatického qEEG hodnocení vyžaduje koregistraci se zobrazovacími metodami, především s počítačovou tomografií (CT) a magnetickou rezonancí (MRI). V programovém prostředí MATLAB byl navržen postup, jež dovoluje fúzi medicínských obrazů s výsledky qEEG a vytváří tak multimodální zobrazení klinických a analytických dat.

Interictal epileptiform discharges (spikes) represent electrographic marker of epileptogenic brain tissue. Besides ictal onsets, localization of interictal epileptiform discharges provides additional information to plan resective epilepsy surgery. The main goals of this study were: 1) to develop a reliable automatic algorithm to detect high and low amplitude interictal epileptiform discharges in intracranial EEG recordings and 2) to design a clustering method to extract spatial patterns of their propagation. For detection, we used a signal envelope modeling technique which adaptively identifies statistical parameters of signals containing spikes. Application of this technique to human intracranial EEG data demonstrated that it was superior to expert labeling and it was able to detect even small amplitude interictal epileptiform discharges. In the second task, detected spikes were clustered by principal component analysis according to their spatial distribution. Preliminary results showed that this unsupervised approach is able to identify distinct sources of interictal epileptiform discharges and has the potential to increase the yield of presurgical examination by improved delineation of the irritative zone.

High-frequency oscillations (HFOs) represent relatively new electrographic marker of epileptogenic tissue. It is starting to be used in presurgical examination to better plan surgical resection and to improve outcome of epilepsy surgery. Development of new techniques of unsupervised HFOs detection is required to further investigate the role of HFO in the pathophysiology of epilepsy and to increase the yield of presurgical examination. In this study we applied an envelope distribution modelling technique on experimental and human invasive data to detect HFOs. Application to experimental microelectrode recordings demonstrated satisfactory results with sensitivity 89.9% and false positive rate 2.1 per minute. Application of this algorithm to human invasive recordings achieved sensitivity 80%. High numbers of false positive detections required utilization of post-processing steps to eliminate the majority of them. This study shows that envelope distribution modelling represents a promising approach to detect HFOs in intracranial recordings. Advantages of this approach are quick adjustments to changes in background activity and resistance to signal non-stationarities. However, successful application to clinical practice requires development of secondary processing steps that will decrease the rate of false positive detections.

Selected patients with refractory epilepsy can benefit from surgical treatment. The main purpose of presurgical examination is to identify and delineate epileptogenic areas of the brain which should be removed. Epileptogenic areas are determined according to the spatial distribution of seizure onsets, interictal epileptiform discharges or high-frequency oscillations. Specificity of interictal epileptiform discharges to mark epileptogenic tissue is decreased by the fact, that they are also observed outside the epileptogenic areas. To improve the localizing yield of interictal discharges, identification of specific features of the discharges generated only within epileptogenic region is required. The main aim of this project was to develop self-clustering algorithm which will discriminate distinct populations of interictal epileptiform discharges according to the morphology of their waveforms. First step of the developed algorithm extracts nine basic morphological features of each interictal epileptiform discharge detected in band-pass filtered (2-60 Hz) intracranial recordings. Principal component analysis is applied on extracted features to reduce their dimension. Only the first principle components with cumulative variance of 80 % or above are used for clustering. Gaussian Mixture Distribution method is utilized to assign each discharge to appropriate morphological cluster. Results of the clustering algorithm are displayed in the form of cortical maps together with medians of the clustered discharge waveform. Developed algorithm was tested in the model of intracranial EEG signal and on data recorded in patients who underwent intracranial monitoring. Results demonstrate the ability of the algorithm to separate interictal epileptiform discharges according to their morphological features.

Surgical treatment of epilepsy is based on exact localization of “epileptogenic area”. During presurgical examination tracing of interictal discharges (spikes) propagation is used as additional information about location of this area. The tracking may be done by estimation of delay between spikes in EEG channels. Medical EEG recordings are not recorded with high sampling frequency. That may cause problem, because sometimes the speed of spike propagation is so high, that the delay lies under sampling resolution. The main goal of this preliminary study is to use coherence method on intracranial EEG data and examine if it is able to estimate delay between (IED) even under sampling resolution with reasonable error. For examination simulated EEG signals, with known delays are used. The simulation is done by using IED pattern made from “golden standard” epileptic discharges. Preliminary result made on simulated data shows that this coherence based method is able to examine delays even under sampling resolution for signals with amplitude signal-to-noise ratio (SNR) higher than 10 dB. Such high values of SNR are reached in real EEG recording only by the biggest spikes. Therefore this method is not better for purposes of spike propagation tracking than for example cross-correlation based methods.

This paper deals with analysis of electrodynamic activity of yeast cells based on theoretical assumptions about generating electrodynamic cells field. Data were measured by laboratory apparatus with tip sensor in the acoustic range. This paper describes data preprocessing and standard statistic methods that seek power intensity differences at time between synchronized and non-synchronized yeast cells. The second goal is detection of main oscillatory frequencies of yeast cells. Summary 49 yeasts sets were tested (23 synchronized and 26 non-synchronized). Measured data were preprocessed in program MATLAB (company The Math-Works, version 2011b. Power increasing was observed in analysis of synchronized yeast cells in 8 to 12.5 minutes that corresponds with prometaphase and early metaphase cell cycle. This result shows 7% power increasing of synchronized yeast cells versus non-synchronized. As important frequency ranges were intended 440-449,670--679, 780-789,980-989, 1250-1259, 1520-1529 Hz. These methods were first used for this type of data. Results from seeking frequency modes were verified by other type of method before than it determine as oscillation modes.

The Jerk-locked backaveraging is the method for bio-signal processing used especially in the field of neurology and neuroscience. The goal is to find if there is any connection between involuntary muscle movements or jerks and preceding neurological activity. This paper summarizes the basic principles of this method and requirements for signals which are needed, such as type of signals, sampling frequency etc. One of the necessary prerequisity is to determine EMG bursts onsets, which serves as a synchronization points for the following averaging. Several EMG onsets detection methods are described also with signal preprocessing enabling EMG detection. All methods are applied to signals recorded from real patient, who suffers from involuntary muscles jerks. All methods are compared, designating the BSCD onset detection method as the best result, because of highest peak-to-peak amplitudes after back-averaging.

This article discuss problematic of using automated seizure detection as alarm or support in epilepsy screenings. Seizures are detected from scalp EEG which is less resistant to disturbance, but it is noninvasive. As method for detection use directed transfer function (DTF) a method based on multidimensional autoregressive models and discuss its reliability of seizure detection. Implementation in Matlab and discuss its results.

Vysokofrekvenční oscilace (HFO) hrají důležitou roli jak v normální funkci mozku, tak v patofyziologii neurologických onemocnění, především v epilepsii. Experimentálně a klinicky bylo prokázáno, že specifický typ vysokofrekvenčních oscilací je generován v oblastech mozku, ve kterých vznikají epileptické záchvaty. Tato vlastnost vysokofrekvenčních oscilací je v současnosti využívána v předoperační diagnostice za účelem zlepšení výsledků chirurgické léčby epilepsie. Předpokládá se, že vysokofrekvenční oscilace umožní přesnější určení oblasti mozku, kterou je nezbytné chirurgicky odstranit, aby došlo k vymezení záchvatů. Úspěšné použití vysokofrekvenčních oscilací v klinické praxi však vyžaduje vývoj nových algoritmů, které umožní spolehlivě detekovat tyto oscilace. V této práci bylo vyvinuto a testováno využití automatického detekčního algoritmu založeného na odlišnosti statistických parametrů základní aktivity pozadí a vysokofrekvenčních událostí. Tyto parametry byly určeny použitím Hilbertovy transformace, následované nalezením vhodného modelu logaritmicko-normální distribuce transformovaného signálu. Navržený postup umožňuje identifikovat vysokofrekvenční úseky signálů, neboť jejich distribuce jsou statisticky významně odchýlené od distribucí základní aktivity.

Článek se zabývá možnostmi detekce a analýzy epileptiformních výbojů v peroperační ortikografii. Zařazení jednotlivých výbojů do klastrů umožňuje definovat oblasti, ve kterých výboje vznikají, a které jsou zodpovědné za vznik epileptických záchvatů. Lokalizace epileptiformních ložisek během operace může sloužit jako diagnostický nástroj pro určení rozsahu resekční oblasti při epileptochirurgické léčbě.

The spike-wave complex as marker of epileptical activity is occurred in parts of the brain that may trigger epileptic seizure. Localization of this area is necessary for precisely neuro-surgical treatment. The aim of the project is developing of automatic objective robust spike detector.

The paper presents a possibility for quantitative evaluation of the electroencephalography (EEG) through detecting for discharge artifacts in the intracranial signals of epileptic patients. Occurrence of spike-wave complex artifacts directly relates to affected parts of the brain. The described method uses the spike detector based on separation discharges and baseline activity using individual setting thresholds. The settings of the thresholds follow the rule which is dependent on statistical distribution and its MLE estimation of the signal envelope. Two patient preview results are introduced. The proposed algorithm is designed for automatic evaluation of intracranial EEG signal. Method allows increase objectivity and save time for neurologist examinations.

The aim of the project is to create suitable algorithms for detecting the presence, localization and extent of epileptogenic focus. The methods have been developed for providing an objective view of the diagnostic symptoms, and are based on search and evaluation of relations in the intracranial EEG signals recordings During the course of the project in the past year, we have focused mainly on the methods for detection of interictal spikes, methods using one-channel time-frequency analysis through Bayesian change-point detectors, methods using multi-channel detection based on multidimensional autoregressive models, and methods based on correlation analysis.

This paper presents a possibility of localization sources of epileptiform discharges in the Electrocorticography using time delay estimation. The work provides the first results of the proposed alorithm. It is presented on two patients with different time of propagation. The method can estimate delay under sample rate resolution.

Mezi základní parametry neinvazivních měření plicních funkcí patří plicní ventilace (kapacita a objem plic), maximální výdechový objem (usilovný výdech) a maximální volní ventilace (hluboké dýchání). Motivací této práce je laboratorní úloha určená pro výuku studentů biomedicínského inženýrství ČVUT FEL v předmětu Biologické signály. Pro všechna měření byl využit spirometr z profesionálního měřícího zařízení Biopac Student Lab a následně byly vyvinuty vlastní jednoduché algoritmy pro automatickou extrakci parametrů ze signálů ve vývojovém prostředí Matlab.

Fröhlich postulated coherent polar oscillations as a fundamental biophysical property of biological systems. This paper describes the measurement of electric activity of yeast cells in the acoustic frequency range. It is a continuation of the article of the authors published in Electromagnetic Biology and Medicine in 2009. This paper describes briefly a development of a new point sensor and new analysis of the data obtained with previous sensor. The sensor with amplifiers fed by batteries are placed in an electromagnetically screened box. Detected signals of synchronized and non synchronized mutants of yeast cells were measured by a spectrum analyzer and stored in a computer memory. The power analysis of measured values was performed. Additional data mining by multi-layer neural network was designed. The evaluation of measured values showed that the electric activity of synchronized cells in the M phase is greater than of non synchronized cells.

This paper deals with data analysis of electrodynamic activity of two mutants of yeast cells, cell cycle of which is synchronized and non-synchronized, respectively. We used data already published by Jelinek et al. and treat them with data mining method based on the multilayer neural network. Intersection of data mining and statistical distribution of the noise shows significant difference between synchronized and non- synchronized yeasts not only in total power, but also discrete frequencies.

This paper describes a methodology for finding appropriate wavelets to parameterizing of intracranial electroencephalography (iEEG) records. Suitable wavelets were calculated according to mutual energy in defined frequency ranges that correspond with standard distributions of EEG bands. The appropriate wavelets were selecting from 84 types of wavelets. The wavelets will be used to parameterizing iEEG records for classifier that will be localizing the seizure onset zone (SOZ) in the epileptology.

This paper presents a possibility for quantitative evaluation of the electroencephalography (EEG) through searching for discharge artifacts in the intracranial electrocorticography signals of epileptic patients. Occurrence of the discharges as a spike-wave complex directly relates to affected parts of the brain. The described method uses the spike detector based on comparison of the energy envelope of the filtered signals and the individual settings of thresholds. The settings of the thresholds follow the rule which is dependent on statistical distribution and its MLE approximation of the envelope. Two patient results are introduced as a preview. The proposed algorithm is designed for automatic evaluation of intracranial EEG signals to increase objectivity and save time for neurologist examinations.

This paper presents the possibility of early detection and localization of epileptogenic focus in the iEEG (intracranial Electroencephalography) signal using a method based on multidimensional autoregressive models. The work provides the first results of the method in the iEEG signal, and discusses technical aspects in terms of the suitability of the sampling frequency, AR model order and segmentation step.

Příspěvek představuje možnosti detekce počátku epileptických záchvatů za účelem lokalizace epileptoformních ložisek z intrakraniálního EEG. Shrnuje několik metod postavených na extrakci parametrů z časově-frekvenční analýzy, transformaci signálů neuronovou sítí, korelační analýze a hlouběji rozvádí metodu na bázi multidimenzionálních autoregresních modelů. Dále jsou představeny prvotní výsledky zmíněné metody implementované v prostředí Matlab.