C-Trend parameters and possibilities of federated learning

Abstract. In this observational study, federated learning, a cutting-edge approach to machine learning, was applied to one of the parameters provided by C-Trend Technology developed by Cerenion Oy. The aim was to compare the performance of federated learning to that of conventional machine learning. Additionally, the potential of federated learning for resolving the privacy concerns that prevent machine learning from realizing its full potential in the medical field was explored. Federated learning was applied to burst-suppression ratio’s machine learning and it was compared to the conventional machine learning of burst-suppression ratio calculated on the same dataset. A suitable aggregation method was developed and used in the updating of the global model. The performance metrics were compared and a descriptive analysis including box plots and histograms was conducted. As anticipated, towards the end of the training, federated learning’s performance was able to approach that of conventional machine learning. The strategy can be regarded to be valid because the performance metric values remained below the set test criterion levels. With this strategy, we will potentially be able to make use of data that would normally be kept confidential and, as we gain access to more data, eventually develop machine learning models that perform better. Federated learning has some great advantages and utilizing it in the context of qEEGs’ machine learning could potentially lead to models, which reach better performance by receiving data from multiple institutions without the difficulties of privacy restrictions. Some possible future directions include an implementation on heterogeneous data and on larger data volume.C-Trend-teknologian parametrit ja federoidun oppimisen mahdollisuudet. Tiivistelmä. Tässä havainnointitutkimuksessa federoitua oppimista, koneoppimisen huippuluokan lähestymistapaa, sovellettiin yhteen Cerenion Oy:n kehittämään C-Trend-teknologian tarjoamaan parametriin. Tavoitteena oli verrata federoidun oppimisen suorituskykyä perinteisen koneoppimisen suorituskykyyn. Lisäksi tutkittiin federoidun oppimisen mahdollisuuksia ratkaista yksityisyyden suojaan liittyviä rajoitteita, jotka estävät koneoppimista hyödyntämästä täyttä potentiaaliaan lääketieteen alalla. Federoitua oppimista sovellettiin purskevaimentumasuhteen koneoppimiseen ja sitä verrattiin purskevaimentumasuhteen laskemiseen, johon käytettiin perinteistä koneoppimista. Kummankin laskentaan käytettiin samaa dataa. Sopiva aggregointimenetelmä kehitettiin, jota käytettiin globaalin mallin päivittämisessä. Suorituskykymittareiden tuloksia verrattiin keskenään ja tehtiin kuvaileva analyysi, johon sisältyi laatikkokuvioita ja histogrammeja. Odotetusti opetuksen loppupuolella federoidun oppimisen suorituskyky pystyi lähestymään perinteisen koneoppimisen suorituskykyä. Menetelmää voidaan pitää pätevänä, koska suorituskykymittarin arvot pysyivät alle asetettujen testikriteerien tasojen. Tämän menetelmän avulla voimme ehkä hyödyntää dataa, joka normaalisti pidettäisiin salassa, ja kun saamme lisää dataa käyttöömme, voimme lopulta kehittää koneoppimismalleja, jotka saavuttavat paremman suorituskyvyn. Federoidulla oppimisella on joitakin suuria etuja, ja sen hyödyntäminen qEEG:n koneoppimisen yhteydessä voisi mahdollisesti johtaa malleihin, jotka saavuttavat paremman suorituskyvyn saamalla tietoja useista eri lähteistä ilman yksityisyyden suojaan liittyviä rajoituksia. Joitakin mahdollisia tulevia suuntauksia ovat muun muassa heterogeenisen datan ja suurempien tietomäärien käyttö

EEG changes and C-Trend parameters in healthy patients during induction of anesthesia

Abstract. EEG has become technically viable tool in intensive care and there are technologies like C-Trend that compress the complex EEG recording into easy-to-interpret parameters. C-Trend Index is especially designed for evaluation of the brain function during sedation in intensive care patients. In this work, the precepts of EEG, EEG usage during anesthesia practice, including the use of EEG parameters in patients’ overall assessment and C-Trend monitoring in identifying brain states under propofol were discussed. The data consisted of segments of 20 EEG recordings collected from healthy subjects who underwent anesthetic infusions while responding to stimuli to directly determine unconsciousness. The purpose of this work was to evaluate how the parameters that C-Trend provides (C-Trend Index, BSR, aEEG and ADR) behave in patients with healthy brain function during anesthesia and how they correlate with changes that we see in raw EEG and clinical signs such as loss of consciousness. It has been demonstrated that some parameters of qEEG, such as the C-Trend Index and BSR, may predict unconsciousness (results from individual t-test for C-Trend before and after loss of consciousness state are 65.70, p-value is < 0.001 for the smoothed C-Trend Index). Hence, these measures can be utilised in future studies to monitor a neurologically healthy patient’s level of anaesthesia

Surface EMG decomposition using a novel approach for blind source separation

We introduce a new method to perform a blind deconvolution of the surface electromyogram (EMG) signals generated by isometric muscle contractions. The method extracts the information from the raw EMG signals detected only on the skin surface, enabling longtime noninvasive monitoring of the electromuscular properties. Its preliminary results show that surface EMG signals can be used to determine the number of active motor units, the motor unit firing rate and the shape of the average action potential in each motor unit

Noninvasive methods for children\u27s cholesterol level determination

Today, there is a controversy about the role of cholesterol in infants and the measurement and management of blood cholesterol in children. Several scientific evidences are supporting relationship between elevated blood cholesterol in children and high cholesterol in adults and development of adult arteriosclerotic diseases such as cardiovascular and cerebrovascular disease. Therefore controlling the level of blood cholesterol in children is very important for the health of the whole population. Non-invasive methods are much more convenient for the children because of their anxieties about blood examinations. In this paper we will present a new try to find non-invasive methods for determining the level of blood cholesterol in children with the use of intelligent system

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Brain computer interfaces: an engineering view. Design, implementation and test of a SSVEP-based BCI.

This thesis presents the realization of a compact, yet flexible BCI platform, which, when compared to most commercially-available solution, can offer an optimal trade-off between the following requirements: (i) minimal, easy experimental setup; (ii) flexibility, allowing simultaneous studies on other bio-potentials; (iii) cost effectiveness (e.g. < 1000 €); (iv) robust design, suitable for operation outside lab environments. The thesis encompasses all the project phases, from hardware design and realization, up to software and signal processing. The work started from the development of the hardware acquisition unit. It resulted in a compact, battery-operated module, whose medium-to-large scale production costs are in the range of 300 €. The module features 16 input channels and can be used to acquire different bio-potentials, including EEG, EMG, ECG. Module performance is very good (RTI noise < 1.3 uVpp), and was favourably compared against a commercial device (g.tec USBamp). The device was integrated into an ad-hoc developed Matlab-based platform, which handles the hardware control, as well as the data streaming, logging and processing. Via a specifically developed plug-in, incoming data can also be streamed to a TOBI-interface compatible system. As a demonstrator, the BCI was developed for AAL (Ambient Assisted Living) system-control purposes, having in mind the following requirements: (i) online, self-paced BCI operation (i.e., the BCI monitors the EEG in real-time and must discern between intentional control periods, and non-intentional, rest ones, interpreting the user’s intent only in the first case); (ii) calibration-free approach (“ready-to-use”, “Plug&Play”); (iii) subject-independence (general approach). The choice of the BCI operating paradigm fell on Steady State visual Evoked Potential (SSVEP). Two offline SSVEP classification algorithms were proposed and compared against reference literature, highlighting good performance, especially in terms of lower computational complexity. A method for improving classification accuracy was presented, suitable for use in online, self-paced scenarios (since it can be used to discriminate between intentional control periods and non-intentional ones). Results show a very good performance, in particular in terms of false positives immunity (0.26 min^-1), significantly improving over the state of the art. The whole BCI setup was tested both in lab condition, as well as in relatively harsher ones (in terms of environmental noise and non-idealities), such as in the context of the Handimatica 2014 exhibition. In both cases, a demonstrator allowing control of home appliances through BCI was developed

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease