13:30
Neurological disorders
Chair: Jan R. Buitenweg
13:30
15 mins
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Non-Convulsive Status Epilepticus Detection
Ying Wang, Xi Long, Johannes P. van Dijk, Richard H.C. Lazeron, Ronald M. Aarts, Johan Arends
Abstract: Non-convulsive status epilepticus (NCSE) is an epileptic process, where electrographic seizure activity persists over 10 minutes without noticeable motor symptoms [1]. Long-term NCSE with high degree of unresponsiveness may result in structural brain damage for the ICU patients. During the patients’ hospital stay, it is practically difficult to constantly make precise diagnosis of NCSE by clinicians via a routine procedure. Given the subtle and variable clinical symptoms, clinicians widely use electroencephalography (EEG) to diagnose NCSE. The ictal discharges during NCSE are visually analyzed by the clinicians based on some common morphological EEG patterns. However, the visual inspection by humans is time-consuming and subjective. Moreover, the safety of the chronic patients with NCSE is not guaranteed without proper monitoring. Daily monitoring of these patients unduly burdens their caregivers. Therefore, a 24/7 automatic NCSE detection system via continuous EEG signals is desirable at both hospital and home. We aim to develop a ‘brainwave’ chip, which can constantly monitor the EEG signals from NCSE patients. An automatic NCSE detection algorithm applied on this chip is investigated.
This is a retrospective observational study with existing EEG and one-lead ECG recordings from two groups: 16 participants with a clinical diagnosis of NCSE and a control group of 12 participants where a clinically suspected NCSE was not confirmed. The NCSE detection system was built and validated on the training and testing dataset in the NCSE group, respectively. Around 15 features were mainly extracted from the time and frequency domains of EEG signals [2]. We trained a 3-class RUSBoost classifier to score each epoch (2.56 seconds) in three categories: ictal, abnormal activities, and normal activities. The abnormal activities mainly indicate the electrographic activity during the transition between ictal and normal activities. The decision of the ictal or normal-activity event was based on the evolution of three-category scores in 20-second window. As a preliminary result, a 5-fold cross validation method was executed to achieve the classification performance within one subject. About 85% of ictal events could be detected using our system, and its precision achieves 78%. The performance of each participant will be presented in future work.
Non-convulsive status epilepticus (NCSE) is an epileptic process, where electrographic seizure activity persists over 10 minutes without noticeable motor symptoms [1]. Long-term NCSE with high degree of unresponsiveness may result in structural brain damage for the ICU patients. During the patients’ hospital stay, it is practically difficult to constantly make precise diagnosis of NCSE by clinicians via a routine procedure. Given the subtle and variable clinical symptoms, clinicians widely use electroencephalography (EEG) to diagnose NCSE. The ictal discharges during NCSE are visually analyzed by the clinicians based on some common morphological EEG patterns. However, the visual inspection by humans is time-consuming and subjective. Moreover, the safety of the chronic patients with NCSE is not guaranteed without proper monitoring. Daily monitoring of these patients unduly burdens their caregivers. Therefore, a 24/7 automatic NCSE detection system via continuous EEG signals is desirable at both hospital and home. We aim to develop a ‘brainwave’ chip, which can constantly monitor the EEG signals from NCSE patients. An automatic NCSE detection algorithm applied on this chip is investigated.
This is a retrospective observational study with existing EEG and one-lead ECG recordings from two groups: 16 participants with a clinical diagnosis of NCSE and a control group of 12 participants where a clinically suspected NCSE was not confirmed. The NCSE detection system was built and validated on the training and testing dataset in the NCSE group, respectively. Around 15 features were mainly extracted from the time and frequency domains of EEG signals [2]. We trained a 3-class RUSBoost classifier to score each epoch (2.56 seconds) in three categories: ictal, abnormal activities, and normal activities. The abnormal activities mainly indicate the electrographic activity during the transition between ictal and normal activities. The decision of the ictal or normal-activity event was based on the evolution of three-category scores in 20-second window. As a preliminary result, a 5-fold cross validation method was executed to achieve the classification performance within one subject. About 85% of ictal events could be detected using our system, and its precision achieves 78%. The performance of each participant will be presented in future work.
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13:45
15 mins
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Hyper-Spectral Imaging of the Human Brain Revealing Slow Sinusoidal, Hemodynamic Oscillations at Distinct Frequencies
Herke Jan Noordmans, Dorien van Blooijs, Jeroen Siero, Jaco Zwanenburg, John Klaessens, Nick Ramsey
Abstract: Introduction
Slow sinusoidal, hemodynamic oscillations (SSHOs) around 0.1 Hz are frequently seen in mammalian and human brains. Four patients undergoing functional surgery for epilepsy offered a unique opportunity to determine which SSHOs are present on the human brain.
Methods
Hyper-spectral recordings of 4 to 7 wavelengths were made with a two systems: 1) consisting of liquid crystal tunable filter and a monochrome camera mounted to the surgical microscope, 2) consisting of a flat panel light source with 600 LEDs with 17 peak-wavelengths with a monochrome camera mounted in the middle. Concentrations of oxy- and deoxy-hemoglobin were calculated for each set of wavelengths and image pixel. A Fourier transform was applied along the time axis to determine the oscillating amplitude at each frequency. Oscillating regions were determined by manually delineating bright areas.
Results
For all 4 patients multiple SSHOs were constantly visible during the entire 4 to 10 minute acquisition time. The observed SSHOs were localized to specific cortical regions with a very distinct frequencies and showed a fixed but sometimes large phase difference within that region. SSHOs of deoxygenated hemoglobin appeared to have an opposite phase with respect to the oxygenated hemoglobin SSHOs. Deoxyhemoglobin SSHOs’ amplitude was 90% of the oxygenated hemoglobin SSHOs’ amplitude.
Discussion & Conclusion
Despite the fact that SSHOs have been known for many decades, their function is still unknown. This study shows that SSHOs have very specific characteristics like frequency, phase and location. More research is needed to study their dependence on pathology, anesthetics and electrical or visual stimuli. Hyper-spectral imaging of the human brain offers a new way to study the origin and function of SSHOs on the human brain.
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14:00
15 mins
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The Interaction Between a Cognitive Dual-Task and Visual Cues on Freezing-Severity and Gait in Parkinson's Disease
Janne Heijs, Sabine Janssen, Ciska Heida, Richard van Wezel
Abstract: Freezing of gait (FOG), “an episodic reduction of forward progression of the feet despite the intention to walk”, is frequently seen in advanced stages of Parkinson’s disease (PD). The gait pattern of persons with PD experiencing FOG (PD-FOG) is affected, causing balance disturbances and falls. In contrast to dual-tasks, external rhythmic cues are found to be effective in continuation of gait and reducing the number of FOG-episodes, which may be due to increasing attention to walking. FOG is experienced most in the domestic environment, where tasks are often performed simultaneously. This study examines the influence of external visual cues during dual-task performance on the gait pattern and FOG-severity in PD-FOG and healthy controls.
20 individuals with PD experiencing freezing at least twice a day (PD-FOG), and 15 age-matched healthy controls (HC) were included for participation. Participants were instructed to walk along a 30m-long corridor. The dual-task used in this study, the Adjusted Auditory Stoop (AAS)-task, is a new variant of the auditory Stroop task, intended to evoke FOG. The visual cues (VC) were transverse lines on the floor. Four conditions were measured: (I) normal gait without AAS-task and VC (AAS-/VC-), (II) gait during the AAS-task, without VC (AAS+/VC-), (III) gait without AAS-task, with VC (AAS-/VC+), and (IV) gait during the AAS-task, with VC (AAS+/VC+). Inertial Measurements Units, consisting of gyroscopes, magnetometers and accelerometers, recorded body movements during the experiment that are used for calculation of gait parameters in Matlab. FOG was analysed individually by two experienced clinicians using video recordings.
Preliminary results show a decreased cadence, velocity and stride length, and an increased stride time and stride length -and time variability for AAS+/VC- compared to AAS-/VC- in both PD-FOGs and HCs (p<0.05). In PD-FOG a decreased cadence and increased stride length, stride time and stride time variability was seen for AAS-/VC+ compared to AAS-/VC- (p<0.05). In HCs a decreased velocity and cadence, and an increased stride time was seen in for AAS-/VC+ compared to AAS-/VC- (p<0.05). From these preliminary results we conclude that VC and the AAS-task both have an influence on the gait pattern in PD-FOG and HC.
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14:15
15 mins
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Design of a Multi-Functional Smart Optrode for Electrophysiology and Optogenetics
Ronaldo Ponte, Chengyu Huang, Vasiliki Giagka, Wouter Serdijn
Abstract: Optogenetics is a neuromodulation method that holds great potential for the realization of advanced neuroprostheses due to its precise spatial-temporal control of neuronal activity [1]. The development of novel optogenetic implants (optrodes) may open new doors to investigate complex brain circuitry and chronical brain disorders, such as epilepsy, migraine, autism, Parkinson's disease, etc [2]. Design challenges for the optrode include interference minimization between the µLED drivers and the recording electrodes, selection of proper materials, structures and dimensions to minimize tissue damage, biocompability, and batch production.
In this work, we propose the construction of a multi-functional optrode to be used for physiological studies in group-housed, freely-moving rodents. It comprises commercial blue-light µLEDs for optical stimulation, an active electrode array for recording the local field potentials at different depths in the brain, and a time-domain temperature sensor.
To accomplish this, silicon bulk micromachining is the essential technique used for the device manufacturation. Process steps include epitaxial growth, layers deposition, geometrical etching, ionic implantation, oxidation and diffusion.
For the interconnection of the µLEDs, flip-chip bonding is used. Light intensity and frequency can be controlled via a microcontroller interface assembled on a flexible PCB mounted on the rodent head-stage. The active microelectrode array (MEA) is constructed from a Ti/TiN layer to both meet the biocompatibility requirements and to reduce the electrode-tissue interface impedance, and by this the associated thermal noise. Using a custom, simple, robust and cost-effective BiFET in-house IC technology, the recording amplifiers are monolithically integrated into the MEA to achieve a high signal-to-noise ratio (SNR) and to minimize potential crosstalk coming from the µLED drivers.
Using the same BiFET IC technology, a time-domain temperature sensor is monolithically integrated into the optrode to anticipate possible brain tissue temperature changes of more than 1oC that may come from heat dissipation in the µLEDs or circuit power dissipation.
Finally, the optrode is coated with a PDMS film to electrically protect the µLEDs from the tissue and avoid uncontrollable electrical stimulation of the brain tissue.
REFERENCES
[1] Zhao, Hubin. “Recent Progress of Development of Optogenetic Implantable Neural Probes,” in International Journal of Molecular Sciences, vol. 18, no 8, pp. 1751, 2017.
[2] Gradinaru, V. et al. “Optical Deconstruction of Parkinsonian Neural Circuitry”, in Science, vol. 324, pp. 354, 2009.
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14:30
15 mins
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Visibility Graph Methods in Nonconvulsive Seizure Detection
Hui Du
Abstract: With the development of the visibility graph, a single-channel EEG signal can be characterized in the graph domain as well.
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14:45
15 mins
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Embedding Small Electronic Components into Tiny Flexible Implants
Anna Pak, Wouter Serdijn, Vasiliki Giagka
Abstract: Electronic components in the form of application-specific integrated circuits (ASICs) establishing the communication between the body and the implant, such as stimulation and recording, have, nowadays, become essential elements for current and future generations of implantable devices, as medicine is looking into substituting its traditional pharmaceuticals with electroceuticals, or bioelectronic medicines [1]. Protection of implant components inside the body is a mandatory important step to ensure longevity and reliable performance of the device. The package of the implant should act as a bidirectional diffusion barrier protecting the electronics of the device from body liquids, and also preventing diffusion of toxic materials from the implant towards the tissue. At the same time the implant’s outer layer should match the tissue’s mechanical properties in order not to cause scar growth around the implant or damage the body. Current implants do not completely fulfill the desired properties mentioned above, either lacking hermeticity or softness. In this work, an embedding process developed at Fraunhofer IZM [2] and used in the semiconductor packaging field for chip encapsulation is proposed to be modified and used for protecting implantable ASICs. Such a method will have a number of advantages, such as miniaturization, in comparison with conventional titanium case packaging. Furthermore, embedding allows to avoid long interconnects, which can be a crucial problem for the device implanted inside a constantly moving body. The other advantage is that the geometry of these interconnects can be well controlled, and the amount of contact pads can be higher than in widely used wire bonding technology, because the distribution of solder bumps during embedding can take place on the whole chip area. In the proposed process, biocompatible polymer materials, such as ParyleneC and Polyurethane, together with thin glass films will be employed to provide the implant with the required hermeticity and at the same time flexibility. The developed embedding process technology will ensure homogeneous distribution of mechanical stresses, resulting in high reliability for uninterrupted long-term use of smart implants.
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