72 research outputs found

    An approach to diagnose cardiac conditions from electrocardiogram signals.

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    Lu, Yan."October 2010."Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 65-68).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1. --- Introduction --- p.1Chapter 1.1 --- Electrocardiogram --- p.1Chapter 1.1.1 --- ECG Measurement --- p.2Chapter 1.1.2 --- Cardiac Conduction Pathway and ECG Morphology --- p.4Chapter 1.1.3 --- A Basic Clinical Approach to ECG Analysis --- p.6Chapter 1.2 --- Cardiovascular Disease --- p.7Chapter 1.3 --- Motivation --- p.9Chapter 1.4 --- Related Work --- p.10Chapter 1.5 --- Overview of Proposed Approach --- p.11Chapter 1.6 --- Thesis Outline --- p.13Chapter 2. --- ECG Signal Preprocessing --- p.14Chapter 2.1 --- ECG Model and Its Generalization --- p.14Chapter 2.1.1 --- ECG Dynamic Model --- p.14Chapter 2.1.2 --- Generalization of ECG Model --- p.15Chapter 2.2 --- Empirical Mode Decomposition --- p.17Chapter 2.3 --- Baseline Wander Removal --- p.20Chapter 2.3.1 --- Sources of Baseline Wander --- p.20Chapter 2.3.2 --- Baseline Wander Removal by EMD --- p.20Chapter 2.3.3 --- Experiments on Baseline Wander Removal --- p.21Chapter 2.4 --- ECG Denoising --- p.24Chapter 2.4.1 --- Introduction --- p.24Chapter 2.4.2 --- Instantaneous Frequency --- p.26Chapter 2.4.3 --- Problem of Direct ECG Denoising by EMD : --- p.28Chapter 2.4.4 --- Model-based Pre-filtering --- p.30Chapter 2.4.5 --- EMD Denoising Using Significance Test --- p.33Chapter 2.4.6 --- EMD Denoising using Instantaneous Frequency --- p.35Chapter 2.4.7 --- Experiments --- p.39Chapter 2.5 --- Chapter Summary --- p.44Chapter 3. --- ECG Classification --- p.45Chapter 3.1 --- Database --- p.45Chapter 3.2 --- Feature Extraction --- p.46Chapter 3.2.1 --- Feature Selection --- p.46Chapter 3.2.2 --- Feature Dimension Reduction by GDA --- p.48Chapter 3.3 --- Classification by Support Vector Machine --- p.50Chapter 3.4 --- Experiments --- p.53Chapter 3.4.1 --- Performance of Feature Reduction --- p.54Chapter 3.4.2 --- Performance of Classification --- p.57Chapter 3.4.3 --- Performance Comparison with Other Works --- p.60Chapter 3.5 --- Chapter Summary --- p.61Chapter 4. --- Conclusions --- p.63Reference --- p.6

    Intelligent Pattern Analysis of the Foetal Electrocardiogram

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    The aim of the project on which this thesis is based is to develop reliable techniques for foetal electrocardiogram (ECG) based monitoring, to reduce incidents of unnecessary medical intervention and foetal injury during labour. World-wide electronic foetal monitoring is based almost entirely on the cardiotocogram (CTG), which is a continuous display of the foetal heart rate (FHR) pattern together with the contraction of the womb. Despite the widespread use of the CTG, there is no significant improvement in foetal outcome. In the UK alone it is estimated that birth related negligence claims cost the health authorities over £400M per-annum. An expert system, known as INFANT, has recently been developed to assist CTG interpretation. However, the CTG alone does not always provide all the information required to improve the outcome of labour. The widespread use of ECG analysis has been hindered by the difficulties with poor signal quality and the difficulties in applying the specialised knowledge required for interpreting ECG patterns, in association with other events in labour, in an objective way. A fundamental investigation and development of optimal signal enhancement techniques that maximise the available information in the ECG signal, along with different techniques for detecting individual waveforms from poor quality signals, has been carried out. To automate the visual interpretation of the ECG waveform, novel techniques have been developed that allow reliable extraction of key features and hence allow a detailed ECG waveform analysis. Fuzzy logic is used to automatically classify the ECG waveform shape using these features by using knowledge that was elicited from expert sources and derived from example data. This allows the subtle changes in the ECG waveform to be automatically detected in relation to other events in labour, and thus improve the clinicians position for making an accurate diagnosis. To ensure the interpretation is based on reliable information and takes place in the proper context, a new and sensitive index for assessing the quality of the ECG has been developed. New techniques to capture, for the first time in machine form, the clinical expertise / guidelines for electronic foetal monitoring have been developed based on fuzzy logic and finite state machines, The software model provides a flexible framework to further develop and optimise rules for ECG pattern analysis. The signal enhancement, QRS detection and pattern recognition of important ECG waveform shapes have had extensive testing and results are presented. Results show that no significant loss of information is incurred as a result of the signal enhancement and feature extraction techniques

    Heartwave biometric authentication using machine learning algorithms

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    PhD ThesisThe advancement of IoT, cloud services and technologies have prompted heighten IT access security. Many products and solutions have implemented biometric solution to address the security concern. Heartwave as biometric mode offers the potential due to the inability to falsify the signal and ease of signal acquisition from fingers. However the highly variated heartrate signal, due to heartrate has imposed much headwinds in the development of heartwave based biometric authentications. The thesis first review the state-of-the-arts in the domains of heartwave segmentation and feature extraction, and identifying discriminating features and classifications. In particular this thesis proposed a methodology of Discrete Wavelet Transformation integrated with heartrate dependent parameters to extract discriminating features reliably and accurately. In addition, statistical methodology using Gaussian Mixture Model-Hidden Markov Model integrated with user specific threshold and heartrate have been proposed and developed to provide classification of individual under varying heartrates. This investigation has led to the understanding that individual discriminating feature is a variable against heartrate. Similarly, the neural network based methodology leverages on ensemble-Deep Belief Network (DBN) with stacked DBN coded using Multiview Spectral Embedding has been explored and achieved good performance in classification. Importantly, the amount of data required for training is significantly reduce

    A novel technique for guiding ablative therapy of cardiac arrhythmias

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    Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1999.Includes bibliographical references (leaves 173-180).by Antonis A. Armoundas.Ph.D

    Complex Assessment of Pilot Fatigue in Terms of Physiological Parameters

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    Únava pilotů je jedním z hlavních důvodů leteckých nehod, ke kterým došlo v důsledku pochybení lidského činitele. Z tohoto důvodu je v zájmu zachování nejvyšších standardů letové bezpečnosti ve všech fázích letu zásadní být schopen zabránit vzniku únavy nebo alespoň být schopen ji účinně detekovat a následně na tuto skutečnost upozornit posádku, aby byla schopna unaveného člena posádky odstavit. V současnosti existují studie zabývající se detekcí a sledováním únavy pilotů prostřednictvím fyziologických parametrů, jako je srdeční aktivita, pohyby očí, aktivita horních končetin apod. Ze všech dostupných fyziologických měření se pak analýza variability srdečního rytmu (HRV) jeví jako nejvhodnější metoda zkoumání únavy pilota. Ačkoli se k hodnocení únavy používá mnoho parametrů vycházejících z analýzy variability srdečního rytmu, v literatuře neexistuje shoda o tom, které z těchto parametrů variability srdeční frekvence jsou nejdůležitější pro použití při detekci únavy piloty. Na základě tohoto nedostatku informací v kontextu současného stavu poznání je cílem této práce zjistit nejvýznamnější parametry analýzy variability srdečního rytmu, které lze přímo použít při monitorování únavy pilota. Pro účely zisku dat byly provedeny 24hodinové experimenty, při nichž byla sbírána data o srdeční aktivitě 16 subjektů na Ústavu letecké dopravy, Fakulty dopravní, Českého vysokého učení technického v Praze. Údaje o srdeční aktivitě subjektu byly zaznamenány ve formě elektrokardiogramu (EKG), zatímco plnily letové úkoly. První část této práce přináší teoretické základy únavy v prostředí kokpitu a vysvětluje několik metod, které se používají pro analýzu variability srdeční frekvence zaznamenaných signálů EKG. Následující části obsahují metody statistické analýzy používané k zjištění parametrů s nejvyšší importancí. Výsledky naznačují, že parametr pVLF analýzy ve frekvenční a časově-frekvenční doméně a parametr nHF analýzy HRV ve frekvenční doméně jsou parametry s nejvyšší importancí v případě indikace únavy člena letové posádky. Klíčová slova: Únava pilota, fyziologické parametry, srdeční aktivita, variabilita srdečního rytmuPilot fatigue is one of the main reasons of aircraft accidents that were caused due to the human error factors in flight crew. Therefore, in order to maintain the highest standards of flight safety throughout all flight phases, it is crucially important to be able to prevent occurrence of fatigue or at least to be able to efficiently detect it, afterwards alert the crew to eliminate the fatigued member from flying. At present, there are many studies focusing on detection and monitoring of pilot fatigue by tracking pilot’s physiological parameters such as: cardiac activity, eye movements, upper-limb activities etc. Among all those physiological measurements available, heart rate variability analysis seems to be the most accurate method to examine pilot fatigue. Although many indices of heart rate variability analysis are used to evaluate fatigue, there is no consensus in the literature on which of those heart rate variability indices are the most important ones to utilize on determination of pilot fatigue. Based on this lack of information on the current state of the art, the purpose of this thesis is to ascertain the most significant parameters of heart rate variability analysis that can be directly used in determining pilot fatigue. For obtaining data, a 24-hours of cardiac activity measurements were conducted on 16 subjects on a flight simulator located at the Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague. The subject’s cardiac activity data were recorded in form of electrocardiogram (ECG) while they performed flying tasks. The first part of this thesis delivers a theoretical background on fatigue in the cockpit environment and explains several methods that are used for heart rate variability analysis of the recorded ECG signals. The following parts provide the statistical analysis methods used to find out the most important parameters. The results indicate that pVLF index of the frequency domain and time-frequency domain analysis and nHF parameter of frequency-domain analysis of HRV corresponds to the most important indices which indicate fatigued condition of a flight crew member. Keywords: Pilot fatigue, physiological parameters, cardiac activity, heart rate variabilit

    Thrombolysis in Acute Myocardial Infarction: An Electrocardiographic Study

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    The value of thrombolytic therapy in the treatment of acute myocardial infarction is now unchallenged following the publication of large scale clinical trials showing an impressive reduction in mortality. Intravenous administration of a thrombolytic agent in the early hours of myocardial infarction is established practice in all hospitals, from district generals to specialized cardiac centres. The aim is to obtain a patent artery, improve left ventricular function and decrease mortality. The effectiveness of intravenous therapy obviates the need for acute angiography and intracoronary administration, but a definitive statement concerning whether reperfusion has occurred cannot be made. The 12 lead ECG undergoes well recognised dynamic changes in the early phase of myocardial infarction. Successful lysis, either induced or spontaneous, will modify these changes. Whether these modifications can be quantified and used as simple non invasive tests of reperfusion, myocardial salvage and infarct size has caused much speculation. To have such a simple, widely available, reproducible and inexpensive tool would be highly desirable in a clinical setting. This thesis has addressed these questions. The first study demonstrated the rapid fall in ST segment elevation occurring in response to thrombolysis, and introduced a measurement which expresses this fall as a proportion of the admission value. This is termed the Fractional Change and can be applied to either 24 hour tape recordings or to the 12 lead ECG. A Fractional Change Value > 0.5 occurring by 2-3 hours following therapy is highly specific and sensitive for reperfusion. The next study examined whether an electrocardiographic marker of infarct size, the QRS score, was attenuated in patients achieving successful reperfusion compared with a historical cohort of patients with infarctions given no therapy other than simple analgesia. Only patients with anterior infarcts were studied, and although both groups had similar areas of myocardium at jeopardy on admission, the group of patients achieving successful reperfusion had a significant reduction in the QRS score at 48 hours compared to the control group. These initial studies showed that dynamic changes in the ECG can reflect both reperfusion and myocardial salvage, but are limited in that they were performed in relatively small numbers, and the ECG measurements were made and tabulated manually. A method for digitizing 12 lead ECG's with subsequent computer storage of data for comparative analysis has been developed, and incorporates an automatic QRS scoring system. The developmental work involved in setting this system up and its subsequent validation with inter- and intra-observer variation studies is presented in Chapter 5. This system was then used to follow the sequential ECG changes in a prospective angiographically controlled, double blind randomised trial of 128 patients comparing anistreplase with streptokinase. The 90 minute patency rates for both drugs were found to be the same (anistreplase 55%, streptokinase 53%) . Coronary angiography performed at 90 minutes post therapy allowed a detailed correlation between ECG changes on admission and acute coronary anatomy. The findings of this particular study showed that the height of ST segment elevation does not bear any relation to the age of the infarct, that there is a high incidence of reciprocal change early in the course of infarction, and that this is not related to coexisting disease or remote ischaemia, but is likely to be an electrocardiographic mirror phenomenon. Examining the resolution of ST segment elevation and depression showed that it was the rate of fall which discriminates patent from non patent arteries, and that using a Fractional Change Value of 0. 5 to detect reperfusion, calculated at 2 hours post treatment from a single lead showing maximal ST segment elevation, gave the best sensitivity (81%) and specificity (60%), when compared with a number of different parameters. In addition, it appears that the presence of collaterals supplying the infarct area could result in a high Fractional Change Value despite no antegrade perfusion. This study also confirmed that achievement of a patent artery early (i.e. before 90 minutes) significantly attenuated Q wave development, R wave loss and the QRS score in anterior infarction, but did not affect electrocardiographic markers of infarct size when applied to inferior infarcts. In summary, this thesis provides a detailed study of the electrocardiographic changes taking place in acute myocardial infarction, especially as a consequence of treating with thrombolysis, quantitates these changes and shows where they may be used in a clinical setting as non-invasive tests to aid patient management

    A survey of the application of soft computing to investment and financial trading

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    On the Recognition of Emotion from Physiological Data

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    This work encompasses several objectives, but is primarily concerned with an experiment where 33 participants were shown 32 slides in order to create ‗weakly induced emotions‘. Recordings of the participants‘ physiological state were taken as well as a self report of their emotional state. We then used an assortment of classifiers to predict emotional state from the recorded physiological signals, a process known as Physiological Pattern Recognition (PPR). We investigated techniques for recording, processing and extracting features from six different physiological signals: Electrocardiogram (ECG), Blood Volume Pulse (BVP), Galvanic Skin Response (GSR), Electromyography (EMG), for the corrugator muscle, skin temperature for the finger and respiratory rate. Improvements to the state of PPR emotion detection were made by allowing for 9 different weakly induced emotional states to be detected at nearly 65% accuracy. This is an improvement in the number of states readily detectable. The work presents many investigations into numerical feature extraction from physiological signals and has a chapter dedicated to collating and trialing facial electromyography techniques. There is also a hardware device we created to collect participant self reported emotional states which showed several improvements to experimental procedure

    Relationship between body surface potential maps and atrial electrograms in patients with atrial fibrillation

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    PhD ThesisAtrial fibrillation (AF) is the most common cardiac arrhythmia. It is distinguished by fibrillating or trembling of the atrial muscle instead of normal contraction. Patients in AF have a much higher risk of stroke. AF is often driven by the left atrium (LA) and the diagnosis of AF is normally made from lead V1 in a 12-lead electrocardiogram (ECG). However, lead V1 is dominated by right atrial activity due to its proximal location to the right atrium (RA). Consequently it is not well understood how electrical activity from the LA contributes to the ECG. Studies of the AF mechanisms from the LA are typically based on invasive recording techniques. From a clinical point of view it is highly desirable to have an alternative, non-invasive characterisation of AF. The aim of this study was to investigate how the LA electrical activity was expressed on the body surface, and if it could be observed preferentially in different sites on the body surface. For this purpose, electrical activity of the heart from 20 patients in AF were recorded simultaneously using 64-lead body surface potential mapping (BSPM) and bipolar 10-electrode catheters located in the LA and coronary sinus (CS). Established AF characteristics such as amplitude, dominant frequency (DF) and spectral concentration (SC) were estimated and analysed. Furthermore, two novel AF characteristics (intracardiac DF power distribution, and body surface spectral peak type) were proposed to investigate the relationship between the BSPM and electrogram (EGM) recordings. The results showed that although in individual patients there were body surface sites that preferentially represented the AF characteristics estimated from the LA, those sites were not consistent across all patients. It was found that the left atrial activity could be detected in all body surface sites such that all sites had a dominant or non-dominant spectral peak corresponding to EGM DF. However, overall the results suggested that body surface site 22 (close to lead V1) was more closely representative of the CS activity, and site 49 (close to the posterior lower central right) was more closely representative of the left atrial activity. There was evidence of more accurate estimation of AF characteristics using additional electrodes to lead V1
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