Comparison of smoothing filters in analysis of EEG data for the medical diagnostics purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.Web of Science203art. no. 80

Iontophoresis of the eye - a computational approach

Iontophoresis is an effective, non-invasive method of intraocular drug delivery based on electric current. However, it has many limitations that can be addressed by effective computational models based on both machine learning (a data-driven approach) and other artificial intelligence methods and techniques. To date, computational models using AI/ML are lacking, including for the iontophoresis mechanism itself. Their wider use would help facilitate the delivery of drugs to the eye, which remains a major challenge due to the multiple barriers in the eye. The aim of this paper is to explore the feasibility of developing a computational model for ocular iontophoresis using available AI methods and techniques.Jonoforeza jest skuteczną, nieinwazyjną metodą wewnątrzgałkowego podawania leków opartą na prądzie elektrycznym. Ma jednak wiele ograniczeń, które można rozwiązać za pomocą skutecznych modeli obliczeniowych opartych zarówno na uczeniu maszynowym (podejście oparte na danych), jak i innych metodach i technikach sztucznej inteligencji. Do tej pory brakuje modeli obliczeniowych wykorzystujących AI/ML, w tym dla samego mechanizmu jonoforezy. Ich szersze zastosowanie pomogłoby ułatwić dostarczanie leków do oczu, co pozostaje poważnym wyzwaniem ze względu na liczne bariery w oku. Celem artykułu jest zbadanie wykonalności opracowania modelu obliczeniowego dla jonoforezy ocznej przy użyciu dostępnych metod i technik sztucznej inteligencji

Principles of electrostimulation of the face and neck muscles - a medical and biocybernetic approach

The facial nerve has a tortuous and complex course from the parotid-cerebellar junction to various target sites, withindividually varied and complex branching patterns and connections to several other cranial nerves. This makes research-based computational models a key component of modern diagnostics and therapy, as well as patient monitoring and the design of devices to support the above-mentioned processes. To date, no good computational model has been proposed in this area and the concepts presented are in the preliminary research phase. The aim of this study is to develop guidelines for a computational model of electrostimulation of facial and neck muscles in order to improve diagnosis and therapy, but also for the future development of a virtual twin for eHealth.Nerw twarzowy ma kręty i złożony przebieg od połączenia ślinianki przyusznej i móżdżku do różnych miejsc docelowych, z indywidualnie zróżnicowanymi i złożonymi wzorcami rozgałęzień i połączeniami z kilkoma innymi nerwami czaszkowymi. Sprawia to, że modele obliczeniowe oparte na badaniach są kluczowym elementem nowoczesnej diagnostyki i terapii, a także monitorowania pacjentów i projektowania urządzeń wspierających wyżej wymienione procesy. Do tej pory nie zaproponowano dobrego modelu obliczeniowego w tym obszarze, a przedstawione koncepcje znajdują się we wstępnej fazie badań. Celem niniejszego badania jest opracowanie wytycznych dla modelu obliczeniowego elektrostymulacji mięśni twarzy i szyi w celu poprawy diagnostyki i terapii, ale także dla przyszłego rozwoju wirtualnego bliźniaka dla eZdrowi

Dispositivo de Deteção do Bruxismo do Sono

This thesis aims to explore and, ultimately, develop a system capable of monitoring physiological signals to detect bruxism events. Bruxism is a disorder characterized by the habit of pressing and grinding the teeth. These events can either occur during the day (Awake Bruxism) or during the night (Sleep Bruxism). Studies suggest that 20% of the adult population suffer from Awake Bruxism, and 8-16% from Sleep Bruxism. The consequences of this disorder are several, ranging from tooth wear, dental fractures, or abfraction, resulting in headaches, or facial myalgia. This dissertation focuses on the Sleep Bruxism type since it’s harder to detect and treat. First, a study about the evolution of technology in healthcare is carried out, fundamentally about how it was introduced and how did it get to the point it is now. The topic of wearable devices is also explored, in the sense that it’s where the market is going and how these devices can transform healthcare. Then, the study converges on the devices developed especially for bruxism, namely which devices, and what type of techniques are used. Subsequently, the general concept for the system is elaborated, exploring several options both in terms of devices and physiological data to be parameterized. However, some restrictions exist for the construction of the system. For the construction of an intraoral system, the device has to be of small dimensions and with low energy consumption. With these constraints, the system has implemented an Inertial Measurement Unit to estimate the orientation of the patient’s sleeping position, and force sensors to measure the force exerted between the teeth. For compactness, a Systemon-Chip is used, since it includes an ARM Cortex M4 processor, several peripherals, and an RF transceiver in one package. The system is not only responsible for the data acquisition, but also the data transmission. This is accomplished by using Bluetooth Low Energy, which is one of the most common protocols for low-power devices. Customized service is developed for this purpose, consisting of three different characteristics: the force characteristic, the accelerometer characteristic, and the gyroscope characteristic. The reason is for maximizing efficiency. The last step was to develop the prototype, testing its functionalities and try to project next iterations of the prototype

SleepGuard:capturing rich sleep information using smartwatch sensing data

Sleep is an important part of our daily routine – we spend about one-third of our time doing it. By tracking sleep-related events and activities, sleep monitoring provides decision support to help us understand sleep quality and causes of poor sleep. Wearable devices provide a new way for sleep monitoring, allowing us to monitor sleep from the comfort of our own home. However, existing solutions do not take full advantage of the rich sensor data provided by these devices. In this paper, we present the design and development of SleepGuard, a novel approach to track a wide range of sleep-related events using smartwatches. We show that using merely a single smartwatch, it is possible to capture a rich amount of information about sleep events and sleeping context, including body posture and movements, acoustic events, and illumination conditions. We demonstrate that through these events it is possible to estimate sleep quality and identify factors affecting it most. We evaluate our approach by conducting extensive experiments involved fifteen users across a 2-week period. Our experimental results show that our approach can track a richer set of sleep events, provide better decision support for evaluating sleep quality, and help to identify causes for sleep problems compared to prior work

A gaussian mixture-based approach to synthesizing nonlinear feature functions for automated object detection

Feature design is an important part to identify objects of interest into a known number of categories or classes in object detection. Based on the depth-first search for higher order feature functions, the technique of automated feature synthesis is generally considered to be a process of creating more effective features from raw feature data during the run of the algorithms. This dynamic synthesis of nonlinear feature functions is a challenging problem in object detection. This thesis presents a combinatorial approach of genetic programming and the expectation maximization algorithm (GP-EM) to synthesize nonlinear feature functions automatically in order to solve the given tasks of object detection. The EM algorithm investigates the use of Gaussian mixture which is able to model the behaviour of the training samples during an optimal GP search strategy. Based on the Gaussian probability assumption, the GP-EM method is capable of performing simultaneously dynamic feature synthesis and model-based generalization. The EM part of the approach leads to the application of the maximum likelihood (ML) operation that provides protection against inter-cluster data separation and thus exhibits improved convergence. Additionally, with the GP-EM method, an innovative technique, called the histogram region of interest by thresholds (HROIBT), is introduced for diagnosing protein conformation defects (PCD) from microscopic imagery. The experimental results show that the proposed approach improves the detection accuracy and efficiency of pattern object discovery, as compared to single GP-based feature synthesis methods and also a number of other object detection systems. The GP-EM method projects the hyperspace of the raw data onto lower-dimensional spaces efficiently, resulting in faster computational classification processes

Proceedings of the 4th International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME2018)

The Mechatronics Department (Accredited by National Board of Accreditation, New Delhi, India) of the G H Patel College of Engineering and Technology, Gujarat, India arranged the 4th International Conference on Innovations in Automation and Mechatronics Engineering 2018, (ICIAME 2018) on 2-3 February 2018. The papers presented during the conference were based on Automation, Optimization, Computer Aided Design and Manufacturing, Nanotechnology, Solar Energy etc and are featured in this book

Emotions and cognitive workload in economic decision processes - A NeuroIS Approach

The influence of cognitive and emotions on decision processes have been recently highlighted. Emotions interplay with the process of cognition, and determine decision processes. In this work, the role of external and internal influences on economic decision processes are studied. A NeuroIS method is applied for measuring emotions and cognitive workload. The lack of a suitable experimental platform for performing NeuroIS studies was recognized and the platform Brownie was developed and evaluated

Blind Source Separation for the Processing of Contact-Less Biosignals

(Spatio-temporale) Blind Source Separation (BSS) eignet sich für die Verarbeitung von Multikanal-Messungen im Bereich der kontaktlosen Biosignalerfassung. Ziel der BSS ist dabei die Trennung von (z.B. kardialen) Nutzsignalen und Störsignalen typisch für die kontaktlosen Messtechniken. Das Potential der BSS kann praktisch nur ausgeschöpft werden, wenn (1) ein geeignetes BSS-Modell verwendet wird, welches der Komplexität der Multikanal-Messung gerecht wird und (2) die unbestimmte Permutation unter den BSS-Ausgangssignalen gelöst wird, d.h. das Nutzsignal praktisch automatisiert identifiziert werden kann. Die vorliegende Arbeit entwirft ein Framework, mit dessen Hilfe die Effizienz von BSS-Algorithmen im Kontext des kamera-basierten Photoplethysmogramms bewertet werden kann. Empfehlungen zur Auswahl bestimmter Algorithmen im Zusammenhang mit spezifischen Signal-Charakteristiken werden abgeleitet. Außerdem werden im Rahmen der Arbeit Konzepte für die automatisierte Kanalauswahl nach BSS im Bereich der kontaktlosen Messung des Elektrokardiogramms entwickelt und bewertet. Neuartige Algorithmen basierend auf Sparse Coding erwiesen sich dabei als besonders effizient im Vergleich zu Standard-Methoden.(Spatio-temporal) Blind Source Separation (BSS) provides a large potential to process distorted multichannel biosignal measurements in the context of novel contact-less recording techniques for separating distortions from the cardiac signal of interest. This potential can only be practically utilized (1) if a BSS model is applied that matches the complexity of the measurement, i.e. the signal mixture and (2) if permutation indeterminacy is solved among the BSS output components, i.e the component of interest can be practically selected. The present work, first, designs a framework to assess the efficacy of BSS algorithms in the context of the camera-based photoplethysmogram (cbPPG) and characterizes multiple BSS algorithms, accordingly. Algorithm selection recommendations for certain mixture characteristics are derived. Second, the present work develops and evaluates concepts to solve permutation indeterminacy for BSS outputs of contact-less electrocardiogram (ECG) recordings. The novel approach based on sparse coding is shown to outperform the existing concepts of higher order moments and frequency-domain features