Determination of the Frequency Response of an End Tidal CO2 Analyser

In a health care environment, mechanical ventilation is used to support patients during acute illness and surgery. Mechanical ventilation is the process by which the patient’s respiratory function is artificially supported using a respirator. During mechanical ventilation, it is necessary to ensure the adequacy of ventilation. The patient is thus monitored for several ventilatory parameters. The end-tidal CO2 (EtCO2) is one of the primary monitoring parameters. EtCO2 is the partial pressure or maximal concentration of carbon dioxide (CO2) at the end of an exhaled breath, which is expressed as a percentage of CO2 or mmHg . Capnography is the technique that graphs out expired CO2 as a function of time and measures EtCO2. The measuring device is called a capnometer and the waveform displayed by the capnograph is called capnogram

The application of digital accelerometers for wired and non-wired Mechanomyography

The objective of this paper is to consider the use of digital accelerometers for Mechanomyographic applications. The digital outputs of the accelerometer require the consideration of additional interfacing hardware for any commercial data acquisition systems being considered. The Arduino open-source platform is shown to meet this requirement. This platform also provides access to set the data registers on the accelerometer to output data at the resolution, speed and format required. Results show that digital accelerometers provide an accurate representation of the MMG signal. The second objective of this work was to extend this digital platform to design a wireless MMG sensor. This has been completed using open-source components and libraries. The wireless sensor can provide an inexpensive accurate representation of the MMG response for distances in excess of 30 meters

Assessing the time synchronisation of EEG systems

This study compared the synchronisation of a medical grade Electroencephalography (EEG) system, the g.Tec, and a consumer grade EEG system, the Emotiv. Data was collected from both systems using the lab streaming layer (LSL). Both EEG systems recorded an electric signal from the surface of a customised gel phantom. The electric signal was generated using a solar cell which was illuminated by a monitor presenting a sequence of black and white images. Test results show that the g.Tec had a mean delay of 51.22 ms from the stimulus onset and the Emotiv had a mean delay of 162.69 ms from the stimulus onset. The result should be taken into account with future ERP studies which will use either the EEG system and the lab streaming layer. The design of this experiment provides a smart way to evaluate the temporal accuracy of other EEG systems

Real Time Detection and Analysis of Facial Features to Measure Student Engagement with Learning Objects

This paper describes a software application that records student engagement in an on-screen task. The application records in real time the on-screen activity and simultaneously estimates the emotional state and head pose of the learner. The head pose is used to detect when the screen is being viewed and the emotional state provides feedback on the form of engagement. The application works without recording images of the learner. On completing the task, the percentage of time spent viewing the screen and statistics on emotional state (neutral, happy, sad) are produced. A graph depicting the learner’s engagement and emotional state synchronised with the screen captured video is also produced. It is envisaged that the tool will find application in learning activity and learning object design

Application of the Hough Transform to Aid Raised Pavement Marker Detection on Marked Roadways

A machine vision system is proposed that will identify and locate GPS co-ordinates of defective Raised Pavement Markers along road lane markings. This system will comprise of a mobile data acquisition system, and a separate ofjline image analysis system. In this paper we present a method for road lane marking identification, using a Hough Transform based technique. This paper describes the implementation of the Hough Transform for line detection. Using the Hough Transform algorithm road lane markings are automatically identified, given a scene acquired from a digital camera system. This knowledge is intended to be used to aid current research in the area of defective Raised Pavement Marker detection at 1TB. Results of a sample dataset are presented and discusse

Examination of Driver Visual and Cognitive Responses to Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential

Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway. However, the effect of billboards on driver distraction, eye gaze, and cognition has not been fully investigated. This study utilises a customised driving simulator and synchronised electroencephalography (EEG) and eye tracking system to investigate the cognitive processes relating to the processing of driver visual information. A distinction is made between eye gaze fixations relating to stimuli that assist driving and others that may be a source of distraction. The study compares the driver’s cognitive responses to fixations on billboards with fixations on the vehicle dashboard. The measured eye-fixation related potential (EFRP) shows that the P1 components are similar; however, the subsequent N1 and P2 components differ. In addition, an EEG motor response is observed when the driver makes an adjustment of driving speed when prompted by speed limit signs. The experimental results demonstrate that the proposed measurement system is a valid tool in assessing driver cognition and suggests the cognitive level of engagement to the billboard is likely to be a precursor to driver distraction. The experimental results are compared with the human information processing model found in the literature

Measurement of the Frequency Response of Clinical Gas Analysers

A technique for the time and frequency response measurement of clinical CO2 analysers has been established. The time and frequency response of several analyser systems has been determined. This paper presents the case for the routine measurement of the dynamic performance of such systems in the context of their application in high-frequency ventilation schemes. The importance of these measurements has been demonstrated in the comparison of older and newer systems in the sense that older systems demonstrate significant deterioration in peiformance. In the context of the use of capnographs in life-support systems, it is essential to measure and monitor the dynamic peiformance of such systems to ensure the appropriate monitoring of ventilation schemes. All of the units so far analysed cannot be used for high-frequency and/or paediatric ventilation schemes of over 80 breaths per minute

Analysis of glottal source parameters in Parkinsonian speech

Diagnosis and monitoring of Parkinson\u27s disease has a number of challenges as there is no definitive biomarker despite the broad range of symptoms. Research is ongoing to produce objective measures that can either diagnose Parkinson\u27s or act as an objective decision support tool. Recent research on speech based measures have demonstrated promising results. This study aims to investigate the characteristics of the glottal source signal in Parkinsonian speech. An experiment is conducted in which a selection of glottal parameters are tested for their ability to discriminate between healthy and Parkinsonian speech. Results for each glottal parameter are presented for a database of 50 healthy speakers and a database of 16 speakers with Parkinsonian speech symptoms. Receiver operating characteristic (ROC) curves were employed to analyse the results and the area under the ROC curve (AUC) values were used to quantify the performance of each glottal parameter. The results indicate that glottal parameters can be used to discriminate between healthy and Parkinsonian speech, although results varied for each parameter tested. For the task of separating healthy and Parkinsonian speech, 2 out of the 7 glottal parameters tested produced AUC values of over 0.9

Micro electro mechanical systems based sensor for mechanomyography

The purpose of this work was to design a micro electrical mechanical sensor (MEMS) based system to measure the mechanomyogram of electrically stimulated muscle. The measuring device for the mechanomyography (MMG) system consisted of a dual axis accelerometer and a signal conditioning circuit designed specifically to enhance raw MMG signals . Currently electromyography (EMG) is the standard tool for measuring muscle contraction. During electrical stimulation however, EMG measurements are corrupted with a large stimulus artefact. This obscures any contributions from the much smaller electrical activity of the muscle tissue itself for the first 10ms to 12ms, before disappearing. MMG, being based on kinetic measurements, offers an alternative in such cases. We illustrate this with a dual modality EMG/MMG simultaneous acquisition for a Hoffman-reflex study