Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

Software defined radio datalink implementation using PC-type computers

The objective of this thesis was to examine the feasibility of implementation and the performance of a Software Defined Radio datalink, using a common PC type host computer and a high level programming language. Dedicated transceivers were used, plugged on the PCI bus of host PCs running Windows 2000. Most of the functionality was programmed using the Microsoft Visual C++ language. The tasks to be performed included the channels configuration (number of active channels, center frequencies, sampling and data rates, choice of the appropriate up and down conversion filters), the management of the data transfer between the host computer and the transceiver, the baseband data modulation and demodulation, and the data organization into packets with appropriate headers in order to achieve phase and time synchronization solely by software. A part of the transceivers' configuration was achieved using a configuration utility running in Excel, provided by the manufacturer. Several combinations of M-PSK modulation schemes, channel numbers and datarates were tested in order to measure the performance limits of the system and its ability to perform the required tasks in real-time. The received data streams were further analyzed with the use of Matlab, in order to verify the proper functionality of the communication scheme.http://archive.org/details/softwaredefinedr109456245Captain, Hellenic Air ForceApproved for public release; distribution is unlimited

Scalability and Replicability for Smart Grid Innovation Projects and the Improvement of Renewable Energy Sources Exploitation: The FLEXITRANSTORE Case

In this paper, detailed scalability and replicability plans have been developed to facilitate the adoption of innovation technologies in the pan-EU market. Smart grid development must enable both information and power exchange between suppliers and customers, thanks to the enormous innovation in intelligent communication, monitoring, and management systems. Implementing physical infrastructure alone is not enough, but a smart grid must include new business models and new regulations. In recent years, the number, participants, and scope of smart grid initiatives have increased, with different goals and results. FLEXITRANSTORE project integrates hardware and software solutions in all areas of the transmission system and wholesale markets, unleashing the potential for full flexibility of power systems and promoting the penetration of renewable energy sources and pan-EU markets. Full deployment of these demonstrated solutions requires a reasonable level of scalability and replicability to prevent project demonstrators from continuing local experimental exercises. Scalability and replicability are fundamental requirements for successful scaling-up and replication. Therefore, scalability and replicability enable or at least reduce barriers to the growth and reuse of project demonstrator results

Scalability and Replicability for Smart Grid Innovation Projects and the Improvement of Renewable Energy Sources Exploitation: The FLEXITRANSTORE Case

In this paper, detailed scalability and replicability plans have been developed to facilitate the adoption of innovation technologies in the pan-EU market. Smart grid development must enable both information and power exchange between suppliers and customers, thanks to the enormous innovation in intelligent communication, monitoring, and management systems. Implementing physical infrastructure alone is not enough, but a smart grid must include new business models and new regulations. In recent years, the number, participants, and scope of smart grid initiatives have increased, with different goals and results. FLEXITRANSTORE project integrates hardware and software solutions in all areas of the transmission system and wholesale markets, unleashing the potential for full flexibility of power systems and promoting the penetration of renewable energy sources and pan-EU markets. Full deployment of these demonstrated solutions requires a reasonable level of scalability and replicability to prevent project demonstrators from continuing local experimental exercises. Scalability and replicability are fundamental requirements for successful scaling-up and replication. Therefore, scalability and replicability enable or at least reduce barriers to the growth and reuse of project demonstrator results

Solar hydrogen generation from ambient humidity using functionalized porous photoanodes

Solar hydrogen is a promising sustainable energy vector, and steady progress has been made in the development of photoelectrochemical (PEC) cells. Most research in this field has focused on using acidic or alkaline liquid electrolytes for ionic transfer. However, the performance is limited by (i) scattering of light and blocking of catalytic sites by gas bubbles and (ii) mass transport limitations. An attractive alternative to a liquid water feedstock is to use the water vapor present as humidity in ambient air, which has been demonstrated to mitigate the above problems and can expand the geographical range where these devices can be utilized. Here, we show how the functionalization of porous TiO2 and WO3 photoanodes with solid electrolytes - proton conducting Aquivion and Nafion ionomers - enables the capture of water from ambient air and allows subsequent PEC hydrogen production. The optimization strategy of photoanode functionalization was examined through testing the effect of ionomer loading and the ionomer composition. Optimized functionalized photoanodes operating at 60% relative humidity (RH) and Tcell = 30-70 °C were able to recover up to 90% of the performance obtained at 1.23 V versus reverse hydrogen electrode (RHE) when water is introduced in the liquid phase (i.e., conventional PEC operation). Full performance recovery is achieved at a higher applied potential. In addition, long-term experiments have shown remarkable stability at 60% RH for 64 h of cycling (8 h continuous illumination-8 h dark), demonstrating that the concept can be applicable outdoors

Developing Nickel–Zirconia Co-Precipitated Catalysts for Production of Green Diesel

The transformation of sunflower oil (SO) and waste cooking oil (WCO) into green diesel over co-precipitated nickel&#8315;zirconia catalysts was studied. Two series of catalysts were prepared. The first series included catalysts with various Ni loadings prepared using zirconium oxy-chloride, whereas the second series included catalysts with 60&#8315;80 wt % Ni loading prepared using zirconium oxy-nitrate as zirconium source. The catalysts were characterized and evaluated in the transformation of SO into green diesel. The best catalysts were also evaluated for green diesel production using waste cooking oil. The catalysts performance for green diesel production is mainly governed by the Ni surface exposed, their acidity, and the reducibility of the ZrO2. These characteristics depend on the preparation method and the Zr salt used. The presence of chlorine in the catalysts drawn from the zirconium oxy-chloride results to catalysts with relatively low Ni surface, high acidity and hardly reduced ZrO2 phase. These characteristics lead to relatively low activity for green diesel production, whereas they favor high yields of wax esters. Ni-ZrO2 catalysts with Ni loading in the range 60&#8315;80 wt %, prepared by urea hydrothermal co-precipitation method using zirconium oxy-nitrate as ZrO2 precursor salt exhibited higher Ni surface, moderate acidity, and higher reducibility of ZrO2 phase. The latter catalysts were proved to be very promising for green diesel production

PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types

To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO&rsquo;s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes

Pain and Anxiety versus Sense of Family Support in Lung Cancer Patients

Lung cancer is a stressful condition for both patient and family. The anxiety and pain accompanying cancer and its treatment have a significant negative influence on the patient’s quality of life. The aim of this study was to investigate the correlation between anxiety, pain, and perceived family support in a sample of lung cancer patients. The sample consisted of a total of 101 lung cancer outpatients receiving treatment at the oncology department of a general hospital. Anxiety, pain (severity and impact on everyday life), and perceived family support were assessed using Spielberger’s State-Trait Anxiety Inventory, the Brief Pain Inventory, and the Family Support Scale, respectively. Statistical analyses revealed correlations between anxiety, pain, and family support as perceived by the patients. The intensity of pain had a positive correlation with both state and trait anxiety and a negative correlation with family support. Anxiety (state and trait) had a significant negative correlation with family support. In conclusion, high prevalence rates of anxiety disorders were observed in lung cancer patients. Females appeared more susceptible to anxiety symptoms with a less sense of family support. A negative correlation was evidenced between family support and anxiety and a positive one between anxiety and pain