SoC-based biomedical embedded system design of arrhythmia detector

Arrhythmia is an irregular heartbeat where the blood may not be delivered effectively throughout the body and cause sudden cardiac arrest (SCA). Immediate treatment is required to prevent SCA. However, most of the existing electrocardiogram (ECG) monitoring devices are bulky, cost expensive and lack arrhythmia detection and classification system. This paper proposes a front-end on-board graphical interface design of System-on-Chip (SoC) based arrhythmia detector which can be used as a first screening device for cardiac disease patient. The system consists of a knowledge-based arrhythmia classifier which is able to identify three types of arrhythmias which are ventricular fibrillation (VF), premature ventricular contractions (PVCs) and second-degree atrioventricular (AV) block. The system has been evaluated and benchmarked with ECG data from MIT-BIH arrhythmia database. The results show that its accuracy is up to 99.25% with a computation time of 6.385 seconds. It is highly portable and relatively inexpensive for installation in small clinics and home monitoring

A MATLAB-Based Interactive Environment for EMG Signal Decomposition Utilizing Matched Template Filters

An interactive software package for analyzing and decomposing electromyographic (EMG) signals is designed, constructed, and implemented using the MATLAB high-level programming language and its interactive environment. EMG signal analysis in the form of signal decomposition into their constituent motor unit potential trains (MUPTs) is considered as a classification task. Matched template filter methods have been employed for the classification of motor unit potentials (MUPs) in which the assignment criterion used for MUPs is based on a combination of MUP shapes and motor unit firing pattern information. The developed software package consists of several graphical user interfaces used to detect individual MUP waveforms from raw EMG signals, extract relevant features, and classify MUPs into MUPTs using matched template filter classifiers. The proposed software package is useful for enhancing the analysis quality and providing a systematic approach to the EMG signal decomposition process. It also worked as a very helpful environment for testing and evaluating algorithms developed for EMG signal decomposition research

Physiologically attentive user interface for improved robot teleoperation

User interfaces (UI) are shifting from being attention-hungry to being attentive to users’ needs upon interaction. Interfaces developed for robot teleoperation can be particularly complex, often displaying large amounts of information, which can increase the cognitive overload that prejudices the performance of the operator. This paper presents the development of a Physiologically Attentive User Interface (PAUI) prototype preliminary evaluated with six participants. A case study on Urban Search and Rescue (USAR) operations that teleoperate a robot was used although the proposed approach aims to be generic. The robot considered provides an overly complex Graphical User Interface (GUI) which does not allow access to its source code. This represents a recurring and challenging scenario when robots are still in use, but technical updates are no longer offered that usually mean their abandon. A major contribution of the approach is the possibility of recycling old systems while improving the UI made available to end users and considering as input their physiological data. The proposed PAUI analyses physiological data, facial expressions, and eye movements to classify three mental states (rest, workload, and stress). An Attentive User Interface (AUI) is then assembled by recycling a pre-existing GUI, which is dynamically modified according to the predicted mental state to improve the user's focus during mentally demanding situations. In addition to the novelty of the proposed PAUIs that take advantage of pre-existing GUIs, this work also contributes with the design of a user experiment comprising mental state induction tasks that successfully trigger high and low cognitive overload states. Results from the preliminary user evaluation revealed a tendency for improvement in the usefulness and ease of usage of the PAUI, although without statistical significance, due to the reduced number of subjects.info:eu-repo/semantics/acceptedVersio

Development of OSA Event Detection Using Threshold Based Automatic Classification

Obstructive Sleep Apnea (OSA) is a very serious sleeping disorder resulting in the temporary blockage of the airflow airway that can be deadly if left untreated. OSA is not a rare condition; in the US, from 18 to 50 million people, most of them remain undiagnosed due to cost, cumbersome and resource limitations of overnight polysomnography (PSG) at sleep labs. Instead, automated, at-home devices that patients can simply use while asleep seem to be very attractive and highly on-demand. This paper presents a method for OSA screening and user notification based on the respiratory recording and video monitoring as a secondary system during sleep in order to alert of the apnea event and help patient to recover

EMG Signal Processing in Amateur and Professional Sports with Performance Evaluation and Injury Prevention

Physical activity is a constant in life, prolonging since the primordial times until now as an intrinsic element of human condition, though his character have suffered a transmutation, going from a need, by the predatory nature of the human being, for an option in escaping sedentary habits of contemporary society. Despite the enormous benefits of sports practice, there are also some negative consequences associated, namely the emergence of muscular injuries provided by the installation of fatigue, due to an overload on time or in the intensity of training. The consequences of an injury are drastic, conditioning the quotidian of the injured and carrying high costs for the health system, establishing this problem as the starting point of the present work. Although investigations on this subject have recently appeared, yet is not common to find commercial solutions for evaluating fatigue and with the capability of warning the user about the risk of injury. In order to avoid the fatigue consequences, is proposed the implementation of a computational system for physiological signal processing - Electromyographic (EMG) and Electrocardiographic (ECG) - extracting multiple indexes with informative potential at fatigue level. There is provided an automatic evaluation of the state of fatigue assured by the definition of a Global Fatigue Index that synthesises information from distinct individual fatigue indexes and implementation of a Classification System, with the capability of giving to the user the indication if the physical activity is originating the approximation or deviation from fatigue state. The computer system was built for a future integration as a plugin on a signal acquisition software. This framework is a specialized tool for acquiring and processing of the physiological signals collected in equipments such as bitalino and biosignalsplux, being directed to the practice of indoor cycling

Towards an Intelligent Framework for Pressure-based 3D Curve Drawing

Pen pressure is an input channel typically available in tablet pen device. To date, little attention has been paid to the use of pressure in the domain of graphical interaction, its usage largely limited to drawing and painting program, typically for varying brush characteristic such as stroke width, opacity and color. In this paper, we explore the use of pressure in 3D curve drawing. The act of controlling pressure using pen, pencil and brush in real life appears effortless, but to mimic this natural ability to control pressure using a pressure sensitive pen in the realm of electronic medium is difficult. Previous pressure based interaction work have proposed various signal processing techniques to improve the accuracy in pressure control, but a one-for-all signal processing solution tend not to work for different curve types. We propose instead a framework which applies signal processing techniques tuned to individual curve type. A neural network classifier is used as a curve classifier. Based on the classification, a custom combination of signal processing techniques is then applied. Results obtained point to the feasibility and advantage of the approach.Comment: This paper was rejected from GI 2014. Comment from the chief reviewer:All reviewers noted that the ideas behind this paper were promising, but felt that research was not quite sufficiently developed...Although all agreed that this idea is insightful and has the potential to lead to a valuable contribution,... the idea is not yet sufficiently developed to warrant publicatio