Dissertations / Theses on the topic 'Electrocardiogram'
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Aston, R., and Jean Croce Hemphill. "The Electrocardiogram." Digital Commons @ East Tennessee State University, 1994. https://dc.etsu.edu/etsu-works/7572.
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Mahrousa, Zakria Zaki. "Computerised electrocardiogram classification." Thesis, Cardiff University, 2004. http://orca.cf.ac.uk/55932/.
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Advances in computing have resulted in many engineering processes being automated. Electrocardiogram (ECG) classification is one such process. The analysis of ECGs can benefit from the wide availability and power of modern computers. This study presents the usage of computer technology in the field of computerised ECG classification. Computerised electrocardiogram classification can help to reduce healthcare costs by enabling suitably equipped general practitioners to refer to hospital only those people with serious heart problems. Computerised ECG classification can also be very useful in shortening hospital waiting lists and saving life by discovering heart diseases early. The thesis investigates the automatic classification of ECGs into different disease categories using Artificial Intelligence (AI) techniques. A comparison of the use of different feature sets and AI classifiers is presented. The feature sets include conventional cardiological features, as well as features taken directly from time domain samples of an ECG. The benchmark AI classifiers tested include those based on neural network, k-Nearest Neighbour and inductive learning techniques. The research proposes two modifications to the learning vector quantisation (LVQ) neural network, namely the All Weights Updating-LVQ (AWU-LVQ) algorithm and the Neighbouring Weights Updating-LVQ (NWU-LVQ) algorithm, yielding an "intelligent" diagnostic heart system with higher accuracy and reduced training time compared to existing AI techniques.
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Marques, Jefferson Luiz Brum. "High-resolution electrocardiogram analysis." Thesis, University of Sheffield, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263558.
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Mihailovici, Manuela. "Statistical analysis of electrocardiogram data." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22860.
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An overview of the statistical procedures used in the analysis of electrocardiogram traces is presented in this thesis.The purpose of these procedures is twofold: (i) they may suggest underlying mechanisms that influence heart rate (ii) they may be used as a means of classifying one or more patients into disease categories, by using objective criteria rather than the subjective approaches prevalent in current practice.
In an attempt to apply the methods discussed in this thesis, a selected group of patients was analyzed using spectral analysis.
Lack of information and of control of the patients' activities while they were being monitored precluded the possibility of obtaining definitive results.
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Abrishami, Hedayat. "Deep Learning Based Electrocardiogram Delineation." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563525992210273.
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Jiménez-Pérez, Guillermo. "Deep learning and unsupervised machine learning for the quantification and interpretation of electrocardiographic signals." Doctoral thesis, Universitat Pompeu Fabra, 2022. http://hdl.handle.net/10803/673555.
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Las señales electrocardiográficas, ya sea adquiridas en la piel del paciente (electrocardiogamas
de superficie, ECG) o de forma invasiva mediante cateterismo (electrocardiogramas
intracavitarios, iECG) ayudan a explorar la condición y función
cardíacas del paciente, dada su capacidad para representar la actividad eléctrica del
corazón. Sin embargo, la interpretación de las señales de ECG e iECG es una tarea
difícil que requiere años de experiencia, con criterios diagnósticos complejos para
personal clínico no especialista, que en muchos casos deben ser interpretados durante
situaciones de gran estrés o carga de trabajo como en la unidad de cuidados
intensivos, o durante procedimientos de ablación por radiofrecuencia (ARF) donde
el cardiólogo tiene que interpretar cientos o miles de señales individuales. Desde
el punto de vista computacional, el desarrollo de herramientas de alto rendimiento
mediante técnicas de análisis basadas en datos adolece de la falta de bases de datos
anotadas a gran escala y de la naturaleza de “caja negra” que están asociados con
los algoritmos considerados estado del arte en la actualidad. Esta tesis trata sobre
el entrenamiento de algoritmos de aprendizaje automático que ayuden al personal
clínico en la interpretación automática de ECG e iECG. Esta tesis tiene cuatro
contribuciones principales. En primer lugar, se ha desarrollado una herramienta de
delineación del ECG para la predicción de los inicios y finales de las principales
ondas cardíacas (ondas P, QRS y T) en registros compuestos de cualquier configuración
de derivaciones. En segundo lugar, se ha desarrollado un algoritmo de
generación de datos sintéticos que es capaz de paliar el impacto del reducido tamaño
de las bases de datos existentes para el desarrollo de algoritmos de delineación.
En tercer lugar, la metodología de análisis de datos de ECG se aplicó a datos similares,
en registros electrocardiográficos intracavitarios, con el mismo objetivo de
marcar inicios y finales de activaciones locales y de campo lejano para facilitar la
localización de sitios de ablación adecuados en procedimientos de ARF. Para este
propósito, el algoritmo de delineación del ECG de superficie desarrollado previamente
fue empleado para preprocesar los datos y marcar la detección del complejo
QRS. En cuarto y último lugar, el algoritmo de delineación de ECG de superficie
fue empleado, junto con un algoritmo de reducción de dimensionalidad, Multiple
Kernel Learning, para agregar la información del ECG de 12 derivaciones y lograr
la identificación de marcadores que permitan la estratificación del riesgo de muerte
súbita cardíaca en pacientes con cardiomiopatía hipertrófica.Electrocardiographic signals, either acquired on the patient’s skin (surface electrocardiogam, ECG) or invasively through catheterization (intracavitary electrocardiogram, iECG) offer a rich insight into the patient’s cardiac condition and function given their ability to represent the electrical activity of the heart. However, the interpretation of ECG and iECG signals is a complex task that requires years of experience, difficulting the correct diagnosis for non-specialists, during stress-related situations such as in the intensive care unit, or in radiofrequency ablation (RFA) procedures where the physician has to interpret hundreds or thousands of individual signals. From the computational point of view, the development of high-performing pipelines from data analysis suffer from lack of large-scale annotated databases and from the “black-box” nature of state-of-the-art analysis approaches. This thesis attempts at developing machine learning-based algorithms that aid physicians in the task of automatic ECG and iECG interpretation. The contributions of this thesis are fourfold. Firstly, an ECG delineation tool has been developed for the markup of the onsets and offsets of the main cardiac waves (P, QRS and T waves) in recordings comprising any configuration of leads. Secondly, a novel synthetic data augmentation algorithm has been developed for palliating the impact of small-scale datasets in the development of robust delineation algorithms. Thirdly, this methodology was applied to similar data, intracavitary electrocardiographic recordings, with the objective of marking the onsets and offsets of events for facilitating the localization of suitable ablation sites. For this purpose, the ECG delineation algorithm previously developed was employed to pre-process the data and mark the QRS detection fiducials. Finally, the ECG delineation approach was employed alongside a dimensionality reduction algorithm, Multiple Kernel Learning, for aggregating the information of 12-lead ECGs with the objective of developing a pipeline for risk stratification of sudden cardiac death in patients with hypertrophic cardiomyopathy.
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Dong, Cheng. "Electrocardiogram parameter extract and analysis system." Thesis, University of Macau, 2011. http://umaclib3.umac.mo/record=b2549897.
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Crittenden, Mark E. "Real-time intrapartum fetal electrocardiogram analysis." Thesis, University of Nottingham, 1997. http://eprints.nottingham.ac.uk/27969/.
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The research within this thesis concerns the monitoring of the fetus during labour, using the fetal electrocardiogram (FECG). A versatile FECG analysis system was developed for the Microsoft Windows environment, to allow various FECG parameters to be extracted. Algorithms, currently used in other FECG analysis systems, were implemented using Object Oriented Programming, thus allowing new algorithms to be easily added at a later stage. Although these current algorithms have been demonstrated by several authors, it was felt that they had been used with only partial investigation of their limitations, and with failure to fully determine their accuracy in controlled conditions. These factors are fully addressed within this thesis. By developing a FECG simulator, in which heart-rate, morphology, and noise levels could be varied, the ability of the analysis algorithms to extract the parameters, and the accuracy of these parameters under different noise conditions, were thoroughly checked. Both ability and accuracy were shown to be very good in ideal noiseless conditions; but, with the addition of noise, there exists a compromise between parameter accuracy when the morphology is static, and parameter accuracy when the morphology is changing. The accuracies of the most common indices in this field (the Conduction Index, and the T/QRS ratio) were determined for different levels of simulated noise, and their values demonstrated for data previously recorded from the fetal scalp. Errors as large as 0.3 in the CI and 0.05 in the T/QRS suggested that in the clinical environment, an indication of the accuracy of each index ought to be displayed, and this may be estimated from the measured level of noise. Furthermore, this analysis system allows the direct comparison of both indices. Finally, in order to design a more effective front-end filter, it is important to be aware of the frequency content of the underlying FECG. The Integral Pulse Frequency Modulation (IPFM) model, combined with Pulse Amplitude Modulation (PAM), was used to estimate realistic frequency components within the FECG signal. The effects of filtering could then easily be modelled to show the distortion of both the FECG and any parameters taken from it. For a FECG frontend filter, distortion was found to be insignificant provided that, above 1 Hz, both the gain remained constant and there was no phase-distortion.
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Peasgood, William. "Enhancement of the abdominal fetal electrocardiogram." Thesis, University of Nottingham, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335851.
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Murray, Henry G. "Evaluation of the fetal electrocardiogram (ECG)." Thesis, University of Nottingham, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297895.
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Santos, Vanessa Cordeiro. "Equine exercise electrocardiogram : standardized lungeing test." Master's thesis, Universidade de Lisboa, Faculdade de Medicina Veterinária, 2016. http://hdl.handle.net/10400.5/11922.
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Dissertação de Mestrado Integrado em Medicina VeterináriaCardiovascular lesions are common in horses, but these lesion are often minor and well tolerated by the horses. However, some cardiovascular lesions can become clinically significant, manifesting as poor performance or even exercise intolerance, arrhythmia, weakness, systemic infection, congestive heart failure, or sudden death. (Bonagura & Reef, 2004) Arrhythmias may cause uncertainty to the equine practitioner especially when diagnosed in resting animals. They must be distinguished as being normal due to variations in autonomic tone (‘physiological’ arrhythmias), or as being caused by cardiac abnormalities. Although rest examination is critical to diagnose many clinical problems, exercise tests can help assess dynamic cardiac problems. Exercise testing can be conducted in a treadmill laboratory or in the field. There are advantages and disadvantages for both types of exercise test. However, both protocols might be difficult to implement, and their availability can be limited. This study is focused on evaluating a protocol for standardized lungeing exercise test which was included in the protocol for cardiac examination, used to evaluate cardiac arrhythmias. Lungeing usually is part of most horse’s training, and they can be walked, trotted, and galloped, and induce high levels of exertion, so performing it as an exercise test doesn’t need sophisticated instruments nor specialized people, allowing it to be implemented with less limitations, as long as an arena and the wireless ECG recording equipment (telemetry or Holter monitor) are available. Therefore, this study includes a sample of 10 diverse cases of clinically apparent healthy horses and horses with cardiac disease, in which a standardized lungeing exercise test was performed as part of a protocol for cardiac examination. The protocol for standardized lungeing test consisted of 5 minutes walking, 10 minutes trotting, 4 minutes galloping, 1 minute fast galloping, and 5 minutes walking.
RESUMO - As lesões cardiovasculares são comuns em cavalos, mas estas lesões são muitas vezes pouco significativas, e bem toleradas. No entanto, algumas lesões cardiovasculares podem tornar-se clinicamente significativas, manifestando-se em diminuição da performance ou mesmo intolerância ao exercício, arritmias, fraqueza, infeção sistémica, insuficiência cardíaca congestiva ou morte súbita. (Bonagura & Reef, 2004) As arritmias representam incerteza para os clínicos de equinos, especialmente quando diagnosticadas em animais em repouso. Devem ser distinguidas como sendo normais devido a variações no tónus vagal (arritmia 'fisiológica'), ou como sendo causadas por lesões cardíacas. Embora o exame em repouso seja fundamental para diagnosticar muitos problemas clínicos, os testes de exercício ajudam a avaliar problemas cardíacos dinâmicos. A prova de esforço pode ser realizada em passadeira elétrica ou no campo. Existem vantagens e desvantagens para ambos os tipos de prova de esforço. No entanto, ambos os protocolos podem ser difíceis de implementar, e sua disponibilidade pode ser limitada. Este estudo tem como objetivo estudar um protocolo de eletrocardiograma de esforço durante o trabalho à guia padronizado e inserido num protocolo de exame cardíaco, para a avaliação das arritmias cardíacas. O trabalho à guia geralmente faz parte do treino da maioria dos cavalos, sendo que estes podem ser conduzido a passo, trote, e galope, e originar elevados níveis de esforço. A sua realização enquanto prova de esforço, não torna necessária a utilização de instrumentos sofisticados e pessoas especializadas, dando a possibilidade de ser implementado com menores limitações, sempre que um picadeiro e um equipamento de ECG sem fios (Holter ou telemetria) estejam disponíveis. Este estudo inclui uma amostra de 10 casos clínicos diferentes, que incluem cavalos aparentemente clinicamente saudáveis e cavalos com doença cardíaca, aos quais foi realizado eletrocardiograma de esforço durante o trabalho à guia padronizado e inserido num protocolo de exame cardíaco. Este protocolo consistiu em 5 minutos de passo 10 minutos de trote, 4 minutos de galope, 1 minuto de galope rápido, e 5 minutos de passo.
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Burkhardt, Brian. "THE FUTURE OF ELECTROCARDIOGRAPH TELEMETRY SYSTEMS." International Foundation for Telemetering, 2004. http://hdl.handle.net/10150/605770.
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International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, CaliforniaThe Electrocardiograph (EKG or ECG) measures electrical changes of tissue surrounding the heart to create a time-based representation of the physical operation of the heart. The purpose of this paper is to explore the future of ECG telemetry systems and how they are used in health care. The initial goal is to develop an inexpensive, efficient, and robust real-time ECG telemetry system. The future goal is to create a wireless network of miniature body sensors capable of measuring ECG data and other vital signs.
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Tanriverdi, Volkan. "Removal Of Baseline Wandering From The Electrocardiogram." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/12607687/index.pdf.
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ECG measures electrical potentials on the body surface via contact electrodes. Conditions such as movement of the patient, breathing, and interaction between the electrodes and skin cause baseline wandering of the ECG signal. Baseline wandering noise can mask some important features of the ECG signalhence it is desirable to remove this noise for proper analysis of the ECG signal. This study includes an implementation and evaluation of methods to remove this noise, such as finite impulse response filters, infinite impulse response filters, interpolation filters and adaptive filters. These filters are first applied offline to simulated ECG data. The filter outputs and their frequency spectra are compared to the pure ECG signal and its frequency spectrum using visual inspection and numerical evaluation criteria such as root mean squared error and percentage root relative squared error. The best filters are then selected and applied online to the same simulated data. Finally, these best methods are used to suppress the baseline wandering noise in real ECG recordings using both offline and online filtering. In the offline application, windowing type filters were found to be the most successful filters among the implemented filters. However, a high filter order should be used to produce such good results, which increases the computation time, thus it may not be the best method for online filtering, in which fast computation is essential. Butterworth bidirectional type is preferred for online filtering since it has lower computational complexity, and it produces acceptable results.
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Outram, Nicholas John. "Intelligent pattern analysis of the foetal electrocardiogram." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/2601.
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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.
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Toll, Maria. "Wireless electrocardiogram based on ultra-wideband communications." Thesis, Uppsala universitet, Signaler och System, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-382015.
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The goal for this master thesis is to develop a prototype that uses ultra-wideband (UWB) communications to wirelessly transfer electrocardiogram (ECG) data from an ECG measurement unit to an Android device (smartphone or similar) which is used to process and display the ECG signals. The prototype should consist of two hardware nodes; (1) Node one having a ECG measurement unit (an AD8232 single lead heart rate monitor), an UWB communication module (a Decawave DWM1000 module) and a microcontroller (an Arduino DUE); and (2) Node two having an Android device (an Android smartphone), an UWB communication module (a Decawave DWM1000 module) and a microcontroller (an Arduino DUE). On Node one the AD8232 monitor for ECG measurements is connected to an analog input (with an analog to digital converter (ADC)) on the Arduino and the DWM1000 module is connected to the Arduino via serial peripheral interface (SPI). On Node two the DWM1000 is connected to the Arduino via SPI to receive ECG data from Node one, and the Arduino is connected to the smartphone through a serial USB cable with an USB on-the-go adapter to send the ECG data to the smartphone, where it is filtered and displayed with an Android application. The application has the potential to add, for example, ECG analysis for diagnosing heart activities with artificial intelligence (AI) and further transmit the ECG data for remote medical care. The Arduino is programmed in Arduino IDE (integrated development environment) to handle ECG measurements and UWB communications (transmitting and receiving ECG data), which is limited to a single UWB channel because of limitations of the DWM1000 module. The Android application is created using Android studio, and it can process (with a notch filter) and display 1-12 channel ECG. The prototype has been built and tested. The results show that a single lead ECG measurement can be sent via UWB communication to a smartphone to display in real time. Multiple data channels (1-12 analog inputs on the Arduino) can be multiplexed, transmitted and displayed in real time. This thesis concludes that UWB has huge development potential, and will likely be used for various wireless devices in the future.
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Oh, Sungho. "A new quality measure in electrocardiogram signal." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0007281.
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Guerreiro, Carlota Graça Valente Torres. "Electrocardiografia clínica em canídeos : estudo retrospectivo de 118 casos." Bachelor's thesis, Universidade Técnica de Lisboa. Faculdade de Medicina Veterinária, 2009. http://hdl.handle.net/10400.5/1244.
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Dissertação de Mestrado Integrado em Medicina VeterináriaCom base no estágio curricular realizado no Instituto Veterinário do Parque, apresenta-se esta Dissertação de Mestrado Integrado em Medicina Veterinária, que tem como tema a Electrocardiografia Clínica em Canídeos. Por ter sido possível, durante o referido estágio, acompanhar uma elevada casuística da especialidade de Cardiologia e, pelo facto de as alterações electrocardiográficas e do ritmo cardíaco constituírem condições de extrema importância, muitas vezes subvalorizadas, decidiu-se abordar este tema de uma forma mais pormenorizada. Para o reconhecimento da etiologia das arritmias, a consequente instituição terapêutica e o estabelecimento do prognóstico, tomam-se como procedimentos o entendimento da história clínica do canídeo, o exame físico minucioso e o recurso a outros exames complementares, como a ecocardiografia. Ainda assim, o diagnóstico definitivo das arritmias encontra no registo electrocardiográfico o seu principal recurso. Ao longo de todo o estágio realizado, 118 canídeos foram submetidos a um exame electrocardiográfico. Mais de metade destes cães pertenciam ao sexo masculino, com uma idade média de, aproximadamente, 10 anos, apresentando como justificações mais frequentes para avaliação electrocardiográfica, a presença de tosse, ascite, episódios de síncope e situações de avaliação pré anestésica. As doenças cardíacas encontradas com maior prevalência incluíram a endocardiose da valva mitral e a cardiomiopatia dilatada, tendo sido diagnosticadas nas raças mais predispostas para estas doenças cardíacas, igualmente propensas ao desenvolvimento de arritmias. As arritmias mais frequentes foram a taquicardia sinusal, a arritmia sinusal respiratória, os complexos supraventriculares prematuros e a fibrilhação atrial. A arritmia sinusal respiratória prevaleceu nas raças braquicefálicas, assim como a fibrilhação atrial nas raças de maior porte, estando esta última intimamente relacionada com a dilatação atrial. O ECG é um exame acessível, de fácil interpretação e complementa a informação da ecocardiografia. A ecocardiografia é hoje considerada determinante e de especial importância no diagnóstico de patologia cardíaca, mas é o ECG que nos revela informação específica acerca da actividade eléctrica cardíaca, sendo determinante na apreciação da arritmia.
ABSTRACT -- Clinic Electrocardiography in Canids: Retrospective Study of 118 cases -- As the outcome of the internship in the Instituto Veterinário do Parque, it is introduced here this Master’s Dissertation on Veterinary Medicine, having as the main theme the Clinical Electrocardiogram in Canids. Due to the fact that it was possible to follow a high number of Cardiology cases, and being the electrocardiographic abnormalities and cardiac rhythm disorders, of the utmost importance, commonly underestimated, it was decided to approach the subject exhaustively. In order to identify the aetiology of the arrhythmia, to trace the therapeutical approach and to settle the prognosis, it is usually analysed the subject’s clinical history, and carry out a detailed physical examination and other complementary exams, such as the echocardiogram. However, the definite diagnosis is only fully achieved through the electrocardiogram. During the internship time, 118 canids took an electrocardiogram. More than half of these dogs were male, with an average age of 10 years, approximately, whose main causes for taking the exam were the presence of cough, ascites, episodes of syncope and situations of pre-anaesthesia. The most common cardiac diseases were the mitral valve endocardiosis and dilated cardiomyopathy, mostly diagnosed in breeds naturally predisposed to them, diseases that increase the formation of arrhythmia. The most common types of arrhythmia were the sinus tachycardia, respiratory sinus arrhythmia, supraventricular premature complexes and atrial fibrillation. The respiratory sinus arrhythmia was the most common among the brachicephalic breeds, as the atrial fibrillation prevailed among the bigger breeds, being connected with the atrial dilatation. The ECG is an accessible exam, easy to interpret and complements the echocardiographic data. The echocardiogram is considered to be deciding and of high importance in the diagnosis of cardiac pathologies. It is, however, the ECG that shows the specific facts about the cardiac electrical impulse, being crucial in the identification and judgement of the arrhythmia.
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Weir, D. K. "Pattern recognition of electrocardiograms." Thesis, University of Ulster, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.355922.
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Junttila, J. (Juhani). "Characteristics of subjects with Brugada syndrome type electrocardiogram." Doctoral thesis, University of Oulu, 2008. http://urn.fi/urn:isbn:9789514287701.
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Brugada syndrome is an inherited arrhythmia disorder that predisposes to sudden cardiac death. It is characterized by its distinct ECG pattern. The purpose of this thesis was to study the phenotype and genotype characteristics of subjects with Brugada syndrome type ECG.
The first study population consisted of 2479 young male Air Force applicants and 542 healthy middle-aged subjects. The 12-lead ECG was analyzed to assess the prevalence and prognosis of Brugada pattern in Finnish population. The second population consisted of 168 patients with AF. The ECGs of the patients with family history of lone AF were analysed in order to characterize the ECG features of familial AF. The third population consisted of 200 patients with Brugada syndrome and their ECGs were analyzed for detection of distinct ECG characteristics. In a substudy, the H558R variant was genotyped and the clinical presentation of this variant was evaluated. The clinical characteristics were collected of 47 patients with induced Brugada ECG during fever or medication.
The prevalence of type 2 or 3 Brugada ECG was 0.61% in the young population and 0.55% in the middle-aged Finnish population. In a retrospective analysis, none of the Brugada ECG carriers had died. In the AF study, the prevalence of type 2 or 3 Brugada ECG was significantly higher among the subjects with lone AF compared to the healthy controls (p < 0.001). Many of the Brugada ECG carriers had a family history (> 30% of first-degree relatives) of AF. In patients with Brugada syndrome, the prolonged QRS duration was associated with previous symptoms. The R allele carriers in H558R variant had a trend towards less symptoms (p = 0.067) and had less conduction disturbances in 12-lead ECG than the HH genotype carriers (p < 0.05 in all ECG analysis). Among the subjects with induced Brugada ECG, 51% exhibited arrhythmic symptoms during the medical condition that had provoked the ECG pattern.
In conclusion, type 2 and 3 Brugada ECGs were found to be benign in the Finnish population since no mortality occurred during an extensive follow-up period. On the other hand, these ECG abnormalities seem to be a marker of familial AF. Among patients with the Brugada syndrome, a prolongation of QRS is associated with prior symptoms. The variant H558R R allele seems to be a protecting genetic modulator. Induced Brugada ECG is a medical emergency since the patients are at high risk of sudden cardiac death.
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Boothroyd, Lucy Jane. "The prehospital electrocardiogram in suspected acute coronary syndrome." Thesis, McGill University, 2010. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=94932.
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Acute coronary syndrome (unstable angina and myocardial infarction) is associated most often with a narrowed coronary artery, leading to inadequate blood perfusion (ischemia) of cardiac muscle. Timely diagnosis and treatment is important. For acute myocardial infarction with an elevated ST-segment (STEMI) on an electrocardiogram (ECG), rapid reperfusion therapy is critical for survival. Prehospital ECG strategies are being implemented across Quebec to reduce STEMI treatment delay, but ambulance personnel in this province are not permitted to interpret ECGs. The objectives of this thesis were (1) to examine the diagnostic performance of computerized prehospital ECG interpretation; (2) to estimate the additional time spent on scene to acquire prehospital ECGs; and (3) to examine the similarities and differences in information provided by pairs of prehospital and initial in-hospital ECGs. The thesis used data on 1560 patients served by the Urgences-santé ambulance operator in 2005-2006 in metropolitan Montreal-Laval. Using a Bayesian latent class model, the statistical analysis was unique in the literature in considering data from three tests simultaneously (ECG reading by computer and by cardiologists, and hospital diagnosis) and assuming all were imperfect. Sensitivity and specificity of the computer for detection of true ST-segment elevation on the prehospital ECG were estimated as 78.8% (95% credible interval: 68.6-87.3%) and 98.9% (98.2-99.4%), respectively. Sensitivity and specificity for detection of true STEMI were estimated as 69.2% (59.1-78.2%) and 98.9% (98.1-99.4%), respectively; estimated prevalence of STEMI was 9.0% (7.0-11.4%). Positive and negative predictive values for STEMI were estimated as 85% (76-91%) and 97% (96-98%), respectively. In multivariate regression analysis, younger age was the only patient factor associated with higher sensitivity. Broadening the ECG positive criteria to increase the computer's sensitivity correspondingly deLe syndrome coronarien aigu (angor instable et infarctus du myocarde) est le plus souvent associé à un rétrécissement d'une artère coronaire, entraînant une irrigation sanguine insuffisante (ischémie) du muscle cardiaque. Il importe de le diagnostiquer et de le traiter promptement. Dans les cas d'infarctus aigu du myocarde avec élévation du segment ST (IAMEST) à l'électrocardiogramme (ECG), l'administration rapide d'un traitement de reperfusion est essentielle à la survie. Des stratégies d'ECG préhospitaliers sont implantées à travers le Québec afin de réduire le délai de traitement de l'IAMEST, mais, dans cette province, les techniciens ambulanciers ne sont pas autorisés à interpréter les ECG. Les objectifs de cette thèse étaient (1) d'examiner la validité diagnostique de l'interprétation informatisée de l'ECG préhospitalier; (2) d'estimer le délai additionnel passé « sur place » par le personnel ambulancier pour réaliser les ECG préhospitaliers; et (3) d'examiner les similarités et les différences observées dans l'information fournie par des paires d'ECG préhospitalier et intrahospitalier initial. À ces fins, nous avons utilisé les données sur 1560 patients ayant eu recours aux services de l'entreprise d'ambulances Urgences-santé au cours de la période 2005-2006 dans la région métropolitaine de Montréal et de Laval. Grâce à un modèle bayésien de classe latente, nous avons procédé à une analyse statistique inédite dans la littérature en incorporant trois tests simultanément (interprétation des ECG informatisée et par des cardiologues, et diagnostic hospitalier) et en assumant que ceux-ci n'étaient pas parfaitement justes. La sensibilité et la spécificité de l'ordinateur pour la détection d'une « vrai » élévation du segment ST sur l'ECG préhospitalier ont été estimées à 78,8 % (intervalle de crédibilité à 95 % : 68,6-87,3 %) et 98,9 % (98,2-99,4 %), respectivement. La sensibilité et la sp
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Delano, Margaret K. "A long term wearable electrocardiogram (ECG) measurement system." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76811.
Full textAbstract:
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 101-102).
In this thesis, a low-power, wearable monitoring system was developed from discrete electronic components and custom PCBs. The device was designed to maximize comfort and minimize the footprint on the user. A single lead, clinical grade electrocardiogram (ECG), along with 3 axes of acceleration are recorded while a user wearing the device carries out his/her daily activities. The monitor itself consists of a central PCB that contains an electrode and the majority of the electronics, along with either one or two additional electrodes that are connected to smaller electrode PCBs. The monitor can be configured such that any given electrode can act as an input or an output. The system can record ECG and acceleration for over 1 week and consumes 7.3 mW. To confirm the quality of the ECG recorded by the device, a clinical test was performed. Individuals wore both the device discussed in this thesis and a clinical ECG recorder while engaging in physical activities such as sitting, standing, and running. Four participants have been tested at this time. QRS sensitivity and QRS positive predictability were determined for each ECG trace. The cardiac monitor outperformed the clinical recorder in all interventions. It should be a viable alternative to
by Margaret (Maggie) K. Delano.
M.Eng.
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Flink, Oskar. "Wireless electrocardiogram transmission based on ultra wideband radio." Thesis, Uppsala universitet, Signaler och System, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-352454.
Full textAbstract:
Ultra wideband (UWB) communications has been a subject of much discussion over the last decade. The method of UWB has had a hard time to establish itself among other methods such as Bluetooth and WiFi but as internet of things (IoT) gains a foothold in our daily lives, UWB has presented some new application areas. These application areas are, among other things, self driving cars, energy efficient data transfer, health care applications, sensor networks and real time location systems. This project aims to use UWB communications to real-world applications, specifically, electrocardiography (ECG) - an application in health care in this project, and develop a prototype for the application. The prototype consists of two Android smart phones and two UWB modules (EVK1000 evaluation kits from DecaWave, Inc). Each smartphone connects a UWB module so that the two smart phones, one as sender and the other as receiver, can communicate directly through UWB radios. This is intended to serve as a proof-of-concept that UWB devices are well suited for short range data transfer applications. The result achieved by the project is an android application along with sending and receiving programs for the development boards by Decawave and additional information regarding UWB and its uses. The results also include a comparison of UWB, Bluetooth and WiFi as of todays standard. The goal of the project is to learn how android applications are programmed, how UWB is used in todays technology and how to program and use development boards presented by companies. The prototype has been built and shown that 12-lead simulated ECGsignals from the sender can be transfered to the receiver through the UWB communications. The work includes programming (in C) the two UWB modules for UWB communications, and Java for the android applications (programs) on the smart phones. The Android application is handling transmission of ECG signals to the sending UWB module and then receiving them from the receiving UWB module as well as displaying them on the receiver. The future work to continue the present project is to replace the sending smart phone by an ECG device that sends real ECG signals.
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Gonçalves, Bernardo Nunes. "An ontological theory of the electrocardiogram with applications." Universidade Federal do Espírito Santo, 2009. http://repositorio.ufes.br/handle/10/6409.
Full textAbstract:
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Previous issue date: 2009-05-13Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
The fields of Medical- and Bio-informatics are bearing witness of the application of the discipline of Formal Ontology to the representation of biomedical entities and (re-)organization of medical terminologies also in view of advancing electronic health records (EHR). In this context, the electrocardiogram (ECG) defines one of the prominent kinds of biomedical data. As a vital sign, it is an important piece in the composition of the EHR of today, as likely in the EHR of the future. This thesis introduces an ontological analysis of the ECG grounded in the Unified Foundational Ontology (UFO) and axiomatized in First-Order Logic (FOL). With the goal of investigating the phenomena underlying this cardiological exam, we deal with the sub-domains of human heart electrophysiology and anatomy. We then outline an ECG ontology meant to represent what the ECG is on both sides of the patient and of the physician. The ontology is implemented in the semantic web technology OWL with its SWRL extension. The ECG Ontology makes use of basic relations standardized in the OBO Relation Ontology for the biomedical domain. In addition, it takes inspiration in the Foundational Model of Anatomy (FMA) and applies the Ontology of Functions (OF). Besides the ECG ontological theory itself, two applications of the ECG Ontology are also presented here. The first one is concerned with the off-line integration of ECG data standards, a relevant endeavor for the progress of Medical Informatics. The second one in turn comprises a reasoning-based web system that can be used to offer support for interactive learning in electrocardiography / heart electrophysiology. Overall, we also reflect on the ECG Ontology as well as on its two applications to provide evidence for benefits achieved with the employment of methodological principles - in terms of both ontological foundations and ontology engineering - in building a domain ontology
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Eilevstjønn, Joar. "Removal of Cardiopulmonary Resuscitation Artifacts in the Human Electrocardiogram." Doctoral thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications, 2004. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-314.
Full textAbstract:
Death from heart diseases is the most common type of mortality in western countries and the survival rate of cardiac arrest is dismally low. In the treatment of cardiac arrest, two therapeutic methods are most important: cardiopulmonary resuscitation (CPR; chest compressions and ventilations) and defibrillation (electrical shocks to restart a fibrillating heart).
An automated external defibrillator is commonly used for such shocks, and records and performs signal analysis on the electrocardiogram(ECG) in order to advice when to shock the patient. However, the mechanical activity during CPR introduces artifact components in the ECG. To perform reliable ECG signal analysis, CPR is therefore discontinued for a substantial time before the potential delivery of a shock. This wastes valuable therapy time, and if this hands-off time could be reduced or eliminated by removing these artifacts, it should improve the chance of return of spontaneous circulation.
We propose a method for removing CPR artifacts using a novel multichannel adaptive filter, the computationally efficient and numerically robust MultiChannel Recursive Adaptive Matching Pursuit(MC-RAMP) filter. Using the most realistic data set to date, human out-of-hospital cardiac arrest data of both shockable and non-shockable rhythms, we test MC-RAMP and evaluate the feasibility of ECG analysis during CPR. In our experiments we use a shock advice algorithm and individual ECG signal features to reach the conclusion that after CPR artifact filtering, ECG rhythm analysis during ongoing CPR is feasible.
Finally, we analyze and quantify the time intervals without blood flow (no flow time(NFT)) during external automatic defibrillation in cardiac arrest patients and show that these patients were not perfused around half of the time. We propose methods using CPR artifact filtering to reduce the NFT, and show their significant and promising potential. By introducing the proposed methods into defibrillators, the NFT would be significantly reduced, hopefully increasing the survival.
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Parra, Luis 1972. "Design of an electrocardiogram machine using a personal computer." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47506.
Full text
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Doerschuk, Peter Charles. "A Markov chain approach to electrocardiogram modeling and analysis." Thesis, Massachusetts Institute of Technology, 1985. http://hdl.handle.net/1721.1/15224.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1985.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING.
Bibliography: leaves 393-401.
by Peter Charles Doerschuk.
Ph.D.
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Woodcock, Dan. "A novel entropy measure for analysis of the electrocardiogram." Thesis, Aston University, 2007. http://publications.aston.ac.uk/10681/.
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There has been much recent research into extracting useful diagnostic features from the electrocardiogram with numerous studies claiming impressive results. However, the robustness and consistency of the methods employed in these studies is rarely, if ever, mentioned. Hence, we propose two new methods; a biologically motivated time series derived from consecutive P-wave durations, and a mathematically motivated regularity measure. We investigate the robustness of these two methods when compared with current corresponding methods. We find that the new time series performs admirably as a compliment to the current method and the new regularity measure consistently outperforms the current measure in numerous tests on real and synthetic data.
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Wu, Chien-Wei, and 吳健瑋. "Adaptive Maternal Electrocardiogram Suppression for Fetal Electrocardiogram Extraction." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/30941193901912471556.
Full textAbstract:
碩士國立臺灣大學
生醫電子與資訊學研究所
98
Representing the electrical activity of the cardiac, electrocardiogram (ECG) is a common non-invasive technique for diagnosing heart diseases in a clinical environment. By way of analyzing ECG the patient’s physiological and psychological information can be obtained and offered to medical personnel. Similarly, the health condition of the fetus can be acquired as well through analyzing fetal electrocardiogram (FECG) because the characterization of the FECG is the same as that of the adult ECG. The FECG is derived from cutaneous electrodes placed on the pregnant women’s abdomen. Fetal health information can be acquired through calculating the fetal heart rate variability (fHRV), which be regulated by the autonomic nerve system (ANS) and indicates the fetus’ physiological and psychological conditions, and observing the morphology of ectopic heart beats indicating physiological functions. Unfortunately, abdominal electrodes located on the mother’s abdomen record not only the FECG but undesired signals including the maternal ECG (MECG), other bioelectric source interferences and outer noise. Because the magnitude of the FECG is far smaller than that of the MECG and the FECG can be influenced by noise, it is arduous to extract the FECG from abdominal signals recorded from abdomen electrodes. Previous studies have reported that using the single template PQRST theory suppresses the maternal component to acquire the FECG. However, it may be seen that there is a discrepancy in each maternal beat compared with other maternal beats as abdominal signals are filtered noise and maternal beats do not be entirely or partially overlapped by fetal beats. It can be certain that after subtraction some residual components remain using the single template algorithm. In the thesis, a new algorithm is advanced to extract the FECG. The algorithm, which can be steadily executed in early pregnancy for monitoring the FECG, aims at the shape of each maternal beat to generate an adaptive waveform to which that beat is corresponding and then by which that beat is subtracted. First of all, interferences and noises are preliminarily removed. Secondly, each R wave of the abdominal MECG is orientated. Next, through orientation points each maternal beat is extracted from the abdominal signal. Fourthly, these beats are then calculated by the singular value decomposition (SVD) to extract most dominant bases. Adaptive waveforms are formed with these bases and each maternal beat itself. Subsequently, the corresponding adaptive waveforms are used to suppress maternal beats in the abdominal signal. The last band-pass filter then eliminates the noise generated during subtraction. Finally, the FECG signal can be acquired. Of the new algorithm compared with the result of the single template algorithm, the result demonstrates that using the new algorithm the FECG can be more visible and that the signal-to-noise (SNR) can be further enhanced. The result also illustrates that the FECG can be extracted for long-time monitoring. The new algorithm can successfully process experimental data between 22 and 40 weeks of gestation. The experimental data used in the thesis were downloaded from the “Non-Invasive Fetal Electrocardiogram Database” of “PhysioBank” on the internet. Although the SNR of the result at 22 weeks of pregnancy is not high enough, it is effortless to identify the fetal QRS complex. The low SNR is because the length of all of 22 weeks data range from two to five minutes and the number of abdomen electrodes is three. Short length of data enables the algorithm to hardly extract most dominant bases, and three electrodes compared with four have the lower SNR during executing the SVD to separate information and noise. Nevertheless, in different weeks of gestation the FECG can be further analyzed to obtain the fHRV information. It enables medical personnel to know at early stages the health condition of the fetus on which the mother and the fetus can rely to receive proper treatment.
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Lan, Ting Yu, and 藍婷昱. "Digitization of paper electrocardiogram." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/u26xkv.
Full textAbstract:
碩士中原大學
生物醫學工程研究所
105
Most modern electrocardiographs can store electrocardiograms in memory and printing them out in paper. However, many electrocardiograms are still in paper format kept in old patient records. These electrocardiograms are valuable information for medical research and training because they already have been diagnosed and marked by specialists. Many of these old electrocardiogram were drawn on thermal paper and seriously fading. They are very difficult to be digitized automatically using image processing technique. In this study, a procedure was taken to trace the electrocardiogram by hand before the computerized waveform digitization. The distortion of digitization was analyzed by calculating the percent root-mean-square difference and correlation coefficient of repeated quantization. The results show that the correlation coefficient is generally over 0.97 and the PRD value is around 15%. Automatic detection of position and amplitude of R-wave in electrocardiogram signals was also used to verify the consistence. This study shows that the tracing drawing of signal waveform is highly consistent with the original signal.
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Lin, Shiuh-Jong, and 林旭中. "ECG (ElectroCardioGram) arrhythmia diagnostic system." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/32383028618279334831.
Full text
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Panda, Rashmi. "Removal of artifacts from electrocardiogram." Thesis, 2012. http://ethesis.nitrkl.ac.in/4122/1/Rashmi_Thesis.pdf.
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The electrocardiogram is the recording of the electrical potential of heart versus time. The analysis of ECG signal has great importance in the detection of cardiac abnormalities. The electrocardiographic signals are often contaminated by noise from diverse sources. Noises that commonly disturb the basic electrocardiogram are power line interference, instrumentation noise, external electromagnetic field interference, noise due to random body movements and respirational movements. These noises can be classified according to their frequency content. It is essential to reduce these disturbances in ECG signal to improve accuracy and reliability. Different types of adaptive and non-adaptive digital filters have been proposed to remove these noises. In this thesis, window based FIR filters, adaptive filters and wavelet filter bank are applied to remove the noises. Performances of the filters are compared based on the PSNR values. It is difficult to apply filters with fixed filter coefficients to reduce the instrumentation noise, because the time varying behaviour of this noise is not exactly known. Adaptive filter technique is required to overcome this problem, as the filter coefficients can be varied to track the dynamic variations of the signals. In wavelet transform, a signal is analyzed and expressed as a linear combination of the summation of the product of the wavelet coefficients and mother wavelet. The wavelet decomposition offers an excellent resolution both in time and frequency domain. Better estimation of the amplitudes is also obtained in wavelet based denoising.
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Raj, Jyotiranjan, and Ajit Kumar Sahu. "Fetal electrocardiogram extraction and analysis." Thesis, 2014. http://ethesis.nitrkl.ac.in/6490/1/110EC0180-8.pdf.
Full textAbstract:
Diagnosis of mother’s and child’s heart beat is very necessary during pregnancy and hence we use Fetal electrocardiogram (FECG) extraction for the same. The signal contains precise informations that can help doctors during pregnancy and labor. In this thesis, an easy-to use method has been implemented using adaptive noise canceller (ANC). Using the ANC, an effective algorithm has been proposed. The algorithm uses ANC, Least Mean Square (LMS) method and a Simulink model for the extraction of FECG. The FECG extraction method has been implemented using an algorithm implemented on MATLAB using Simulink models. The extracted FECG signal is a noise free signal. The QRS complex has been detected using another algorithm that counts the R-R peaks. The simulation result shows that heart rate of the FECG signal can be counted using the detection algorithm. This project evokes a complete model of the FECG extraction with the implementation of effective algorithms and adaptive filters and finally gives the heart rate of the FECG signal.
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Chaturvedi, Jalaj. "Electrocardiogram Signal Compression and Decompression." Thesis, 2015. http://ethesis.nitrkl.ac.in/7340/1/2015_Electrocardiogram_Chaturvedi.pdf.
Full textAbstract:
Electrocardiogram is a method of recording the heartbeat of a patient electronically. Quicker transmission of electrocardiogram signals through channels like across wireless networks, telephone networks, hospital networks is not possible without compressing them. Here I implement an algorithm for ECG (electrocardiogram) signal compression based on Delta Coding and LZW (Lempel–Ziv–Welch) encoding. To carry out compression, real time ECG data has been recorded through EKG sensors and Labview. Since the recorded signal was analogue it was first converted to digital by applying Savitzky Golay Filter, uniform sampler and quantizer sequentially. Then the signal was compressed using delta coding and LZW coding. Then the compressed signal was decompressed and then further converted to analogue. Finally the reconstructed signal was compared with the original signal. This was done for six different analogue samples. The compression ratios and the reconstruction errors for all were calculated. The calculated compression ratios range from 1:10.51 to 1:12.56 and PRDs range from 3.33% to 4.79%. Mean compression ratio came out to be 1:11.36 and mean PRD came out to be 4.18%. The number of quantization levels was kept sufficiently large in order to reduce the quantization error zero levels. In the pre-processing stage the signal was reduced to about one-ninth of the original size while in the encoding stage it was reduced to one third. When applied sequentially, the compression ratios got multiplied.
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Agrafioti, Foteini. "Robust Subject Recognition Using the Electrocardiogram." Thesis, 2008. http://hdl.handle.net/1807/11131.
Full textAbstract:
This thesis studies the applicability of the electrocardiogram signal (ECG) as a biometric. There is strong evidence that heart's electrical activity embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. Such systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios.
As in most pattern recognition problems, the probability of mis-classification error decreases as more learning information becomes available. Thus, a central consideration is the design and evaluation of algorithms which exploit the added information provided by the 12 lead standard ECG recording system. Feature and decision level fusion techniques described in thesis, offer enhanced security levels.
The main novelty of the proposed approach, lies in the design of an identification system robust to cardiac arrhythmias. Criteria concerning the power distribution and information theoretic complexity of electrocardiogram windows are defined to signify abnormal ECG recordings, not suitable for recognition. Experimental results indicate high recognition rates and highlight identification based on ECG signals as very promising.
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Guo, Chin-yuan, and 郭晉源. "Studies in the electrocardiogram monitoring indices." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/11424197380605357957.
Full textAbstract:
碩士國立中山大學
應用數學系研究所
92
An recent finding shows that heart rate data possess self-similar property, which is characterized by a parameter H, as well as a long range dependent parameter d. We estimate H by the EBP(Embedded Branching Process) method to derive the fractional parameter d in the first part. The heart rate and R-R interval data are found to have high differencing parameter(d=0.8 ~0.9) and against the normality assumption. Thus the heart rate and R-R interval data are first fractionally differenced of order 0.5 to achieve stationarity. In the second part, we analyze the RR-interval data on the physionet and obtain the long range parameters. After fractionally differencing 0.5 order, the EBP method is adapted to estimate the long range parameter d. The EWMA and EWRMS control charts of the I(d) processes are constructed to monitor the heart rate mean level and variability, respectively for the 18 RR-interval data sets from the physionet. For the EWMA control chart the out of control percentages are chosen to the nominal probability. However, the out of control percentages are affected by the skewness and kurtosis of the process distribution for the EWRMS control carts. Generally speaking, the I(d)-EWMA and I(d)-EWRMS control charts provide a proper monitor system for heart rate mean level and variability.
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"Wireless electrode for electrocardiogram (ECG) signal." 1999. http://library.cuhk.edu.hk/record=b5890078.
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by Leung Sze-wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.
Includes bibliographical references (leaves 79-84).
Abstracts in English and Chinese.
ACKNOWLEDGEMENT --- p.II
ABSTRACT --- p.III
摘要 --- p.V
CONTENTS --- p.VI
Chapter CHAPTER 1 --- INTRODUCTION --- p.1
Chapter 1.1 --- Objectives --- p.1
Chapter 1.2 --- Prevalence of Heart Diseases --- p.1
Chapter 1.3 --- Importance of ECG Monitoring --- p.2
Chapter 1.4 --- Wireless Electrode --- p.2
Chapter 1.5 --- Analogue-to-Digital Converters --- p.3
Chapter 1.6 --- Organization of Thesis --- p.4
Chapter CHAPTER 2 --- LITERATURE REVIEW --- p.5
Chapter 2.1 --- Telemetry --- p.5
Chapter 2.1.1 --- "Definitions of ""Telemetry “" --- p.5
Chapter 2.1.2 --- Advantages of Telemetry --- p.6
Chapter 2.1.3 --- History of Telemetry --- p.7
Chapter 2.1.4 --- Special Considerations on Telemetry System --- p.10
Chapter 2.2 --- Sigma-Delta Converter --- p.12
Chapter 2.2.1 --- Conventional Digitizing Circuitry --- p.12
Chapter 2.2.2 --- "Single, Dual-Slope A/D Converters" --- p.13
Single-Slope A/D Converter --- p.13
Dual-Slope Converter --- p.75
Chapter 2.2.3 --- Successive Approximation (SAR) --- p.17
Chapter 2.2.4 --- Flash Converters --- p.18
Chapter 2.2.5 --- Sigma-Delta Converter --- p.18
Chapter 2.3 --- Conclusion --- p.20
Chapter CHAPTER 3 --- WIRELESS ELECTRODE --- p.21
Chapter 3.1 --- """Single Electrode"" Measurement" --- p.21
Chapter 3.2 --- VSE (Virtual Single Electrode) --- p.21
Concentric Electrode --- p.21
Chapter 3.3 --- WE (Wireless Electrode) --- p.24
Chapter 3.4 --- Discussion --- p.29
Chapter CHAPTER 4 --- SIGMA-DELTA CONVERTER FOR ECG SIGNALS --- p.30
Chapter 4.1 --- Motivations --- p.30
Chapter 4.2 --- Baseband Application --- p.31
Chapter 4.2.1 --- Simulation Results --- p.31
Chapter 4.2.2 --- Experimental Results --- p.48
Chapter 4.3 --- Wireless Application --- p.58
Chapter 4.3.1 --- General Description --- p.58
Chapter 4.3.2 --- Simulation Results --- p.59
Chapter 4.3.3 --- Scenario 1 (Analogue Decoding) --- p.70
Chapter 4.3.4 --- Scenario II (Digital Decoding) --- p.73
Chapter 4.4 --- Discussion and Conclusion --- p.76
Chapter CHAPTER 5 --- CONCLUSION AND FUTURE WORK --- p.77
Chapter 5.1 --- General Conclus ion --- p.77
Chapter 5.2 --- Future Work --- p.78
BIBLIOGRAPHY --- p.79
LIST OF ABBREVIATIONS --- p.85
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Dai, Yu-Jhih, and 戴育志. "An Intelligent Electrocardiogram Remote Monitoring System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/59438267610304224186.
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碩士國立中央大學
電機工程研究所
98
According to statistical report from Department of Health in Taiwan , heart disease has been ranked as the 2nd leading cause of death. Cardiac arrest in out-of-hospital patients usually causes a worse survival rate (2-8%) due to its unpredictability and inappropriate first aid. Furthermore, studies have shown that rescuers often make a mistake when checking the carotid pulse in an emergency, whether they are healthcare professionals or lay persons.Therefore, a personal-care system for real-time ECG monitoring and immediately communication with hospital in emergent situation is crucial. In this thesis, we have developed a mobile phone based ECG monitoring system. User’s ECG signals are analyzed by a microprocessor and abnormalities in ECG are detected. Once heart failure, including cardiac arrest and etc., has been detected, user’s position (localized by a global positioning system, GPS) as well as his/her abnormal ECG epoch will be modulated and transmitted by means of frequency shift keying (FSK) technique via voice channel to remote health care centers for assistances. Remote health care centers receive the distress call and demodulate the signal transmitted from the user, so that aid center closest to the user can be informed to help the patient in shortest time.
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Amin, Gamal El Din Fathy. "Respiratory Information Extraction from Electrocardiogram Signals." Thesis, 2010. http://hdl.handle.net/10754/133969.
Full textAbstract:
The Electrocardiogram (ECG) is a tool measuring the electrical activity of the heart, and it is extensively used for diagnosis and monitoring of heart diseases. The ECG signal reflects not only the heart activity but also many other physiological processes. The respiratory activity is a prominent process that affects the ECG signal due to the close proximity of the heart and the lungs. In this thesis, several methods for the extraction of respiratory process information from the ECG signal are presented. These methods allow an estimation of the lung volume and the lung pressure from the ECG signal. The potential benefit of this is to eliminate the corresponding sensors used to measure the respiration activity. A reduction of the number of sensors connected to patients will increase patients’ comfort and reduce the costs associated with healthcare. As a further result, the efficiency of diagnosing respirational disorders will increase since the respiration activity can be monitored with a common, widely available method. The developed methods can also improve the detection of respirational disorders that occur while patients are sleeping. Such disorders are commonly diagnosed in sleeping laboratories where the patients are connected to a number of different sensors. Any reduction of these sensors will result in a more natural sleeping environment for the patients and hence a higher sensitivity of the diagnosis.
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Shih, Meng-Yu, and 示孟玉. "Emotion Recognition Using Electrocardiogram and Photoplethysmorgraphy." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/51618578625321951636.
Full textAbstract:
碩士國立中正大學
電機工程研究所
101
In this thesis, we proposed an emotion recognition system based on physiological signals. Electrocardiogram (ECG) and photoplethysmorgraphy (PPG) were used to recognize seven kinds of emotions, including neutral (non-stimulated state), happy, stress, sad, disgust, anger, and surprise. The participants consist of 10 male and 10 female students who watched video programs of two to four minutes in length to stimulate distinct emotions.A new index called the pulse transit time (PTT) has recently emerged and used in research. This signal can be calculated from ECG and PPG signals. We expect to see the accumulate effects of combining ECG, PPG, and PTT features in emotion recognition. Four feature selectors, including fisher discriminant analysis (FDA), sequential backward selection (SBS), sequential forward selection (SFS), and genetic algorithms (GA), were employed to select useful features and reduce feature dimensions, in this study support vector machine was employed as the classifier. Leave-one-out methods were applied to analyze the data recorded from10 male and 10 female participants in cross validation recognizing seven kinds of emotions.Comparing the performance of different combinations of the three categories of physiological signal features, feature selector, and the classifier, we found the combination of the GA feature selector and the SVM classifier achieved the best result. This combination elevated the recognition accuracies in the experimental settings of the leave-one-out cross-validation of 10 males, 10 females, all of the participants and the half-half random selection of all of the participants. All the results demonstrated the combination of the two or three categories of physiological signal features can achieve better recognition rates than that using only one category of physiological signal features. Combination of three categories of features outperforms the combination of two categories of features in emotion recognition, and GA selector plays the major role in promoting the performance.
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Mohapatra, Swaraj. "Classification of Electrocardiogram Waveforms Using PNN." Thesis, 2010. http://ethesis.nitrkl.ac.in/2079/1/SWARAJ.pdf.
Full textAbstract:
With increasing computational power, sophisticated algorithms have been proposed to improve the prediction accuracy of ECG waveform classification systems. One such approach is application of PCA on the features extracted using curve fitting tool in MATLAB and application of PNN to get the classification desired for various purposes. The present work reviews the methods of feature extraction and then the application of PNN to get the classification. It also reveals an approach that can be taken for ECG waveform classification based works
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ZHONG, LIN-YOU, and 鍾林佑. "Predicting the Blood Pressure via Electrocardiogram Signal." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/ene4qj.
Full textAbstract:
碩士亞洲大學
生物資訊與醫學工程學系
105
With the development of technology, the function of the portable devices are gradually increased. The emergence of these devices including many physiological data monitoring function. After combined with the mobile phone, these device can be used to collect the health status-related data. So that the national health awareness gradually. In this study, we focus on cardiovascular disease which is significant increasing risk with hypertension. Through the measurement of ECG signal and blood pressure, we intended to explore the relationship between ECG signal and blood pressure. Finally, the establishment of relationship model for blood pressure prediction was set up. In the future, the predicted model can be used in portable devices.
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Chiu, Yen-Ming, and 邱彥銘. "Acquisition and Identification of Chaotic Electrocardiogram Signals." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/69275943653765954044.
Full textAbstract:
碩士國立中興大學
電機工程學系所
98
Electrocardiography (ECG) is a noninvasive recording featured by electrocardiographic devices which is a transthoracic interpretation of the electrical activity of the human’s heart over time captured. From the data base of medical ECG signals for people, ECG seems to be irregular, random, and changing from person to person. Because of high randomness and complexity of ECG in human beings, its feature is extremely hard and is likely impossible to be duplicated artificially. However, it has recently been shown in the literature that the kind of signals is, in fact, chaotic. Because people’s ECGs are extremely hard to be artificially duplicated, thus intends to investigate the way to extract the specific biometric features of ECG signals for possible use in the biometric personnel identification. The root mean square of the ECG in the time domain is viewed as one of the characteristic values. The signal is converted into the phase plane and ECG chaos extractor is applied to capture the major indices of ECG chaos, i.e. Lyapunov exponents spectrum and correlation dimension. The above mentioned root mean square, Lyapunov exponent and correlation dimension are used as the key input variables for neural networking training and used in the identification scheme.
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翁嘉懋. "Analysis of Electrocardiogram Based on ARMAX Model." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/41884886521984609296.
Full textAbstract:
碩士中華大學
電機工程學系(所)
97
The auto-regressive exogenous input moving average (ARMAX) model is proposed to approximate and to predict the normal electrocardiogram (ECG) in the thesis. The most important X part of the ARMAX is consisted of three major components, such as the right tricuspid valve, the interventricular septum, and the aortic valve. Both minimum square error (MSE) criterion and Generic Algorithm (GA) have been implemented to estimate the coefficients of the ARMAX. The major difference is that the predicted results of MSE criterion usually are local optimal, and the results of GA is superior to that of MSE criterion. Nevertheless, the J junction can be clearly observed from the results of the MSE criterion. Effects by varying the X part are also demonstrated. We hope that the proposed ARMAX model can offer a new approach to diagnose the potential heart defects in the future.
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Liu, Wei-min, and 劉維旻. "Design of wireless electrocardiogram system andits applications." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/96377899034510225117.
Full textAbstract:
碩士國立中央大學
電機工程研究所
96
According to the survey taken by the Department of Healthy in Taiwan, the cardiovascular disease has been noticed because of its second rank in the top ten causes of death. Since dangerous heart attack is always coming in unexpected situation, one low-cost system which is suitable for monitoring a patient’s heart status in his daily life is needed. This research aims to design a wireless electrocardiogram (ECG) recording system for continuously monitoring and tracking the heart status of a patient. The wireless ECG contains multiple circuit stages, including instrument amplifier, bandpass filter and transmission-reception stages. The main contribution of the proposed study is that we have developed a high-sensitive ECG system and construct its wireless communication via Bluetooth channel. The technique, Bluetooth, has been created as a single digital wireless protocol, capable of connecting multiple devices and overcoming issues arising from synchronization of these devices. Bluetooth provides a way to connect and exchange information between universal devices. The bluetooth communication is controlled by a dsPIC microprocessor. ECG signals are digitized, encoded and transmitted in 2.4 GHz by Bluetooth chipset. The data are received by a corresponding receiving Bluetooth chipset and ECG data are reconstructed by another dsPIC microprocessor. The currently developed system can achieve real-time ECG signals transmission above 200 Hz sampling rate. The reconstructed ECG signals were validated by the comparison of the original ECG signals and the reconstructed one. Future work will implement the proposed system in monitoring other bio-electrical signals, such as electroculargram (EOG), electromyogram (EMG) and electroencephalogram (EEG).
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Liu, Chang-Wen, and 劉昌紋. "Mental Stress Analysis System Based on Electrocardiogram." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/56326228364117698999.
Full textAbstract:
碩士國立臺灣海洋大學
電機工程學系
102
Along with the mental stress of modern is higher than the previous, it is difficult to count while we face the problem of living surroundings. Althoug appropriate stress allows people to maintain good physical and mental condition, but the stress generated by the environment often are not temporary. If stress is not adequately expressed or problems not resolved, it will be accumulated continually ,then lead to physical and psychological symptoms and diseases. The stress of long-term accumulation will affect the autonomic nervous system, autonomic nervous system disorders caused, and produce a variety diseases. Stress is inevitable, to understand how to manage stress in today's society is a very important issue. Regarding the stress analysis in the past commonly used to assess by the Perceived Stress Scale. This thesis proposes an emotional stress analysis system is based on electrocardiogram (ECG). Using spectral analysis of heart rate variability（HRV）to assess the regulation of autonomic nervous system. It could reveal the correlation between the physiological response and autonomic nervous system via the energy change in different frequency bands,because people at different levels of emotional stress affects the sympathetic nerve and parasympathetic nerve activity strength.This study is using Power spectral density (PSD) to analyze HRV and observe changes in their autonomic nervous system . The results show that mental stress is applied, the high frequency power proportion (HF%) of HRV were decreased, the low frequency power proportion (LF%) and the low to high frequency power ratios（LF/HF）were increased significantly. The LF/HF ratios were decreased significantly during mental stress is removed. When reducing mental stress, low-frequency power proportion (LF%) compared and the low to high frequency power ratios（LF/HF） are significantly decreased, the high-frequency power proportion (HF%) will be significantly increased. These changes can be used as indicators to assess the status of mental stress, and thus as a reference for work shift arrangements, even helpful to assess detection and diagnosis of clinically relevant .
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Lin, Weijen, and 林偉仁. "Emotion Recognition Based on Electroencephalogram and Electrocardiogram." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/24096118291773195424.
Full textAbstract:
碩士國立中正大學
電機工程研究所
99
In this thesis, we proposed an emotion recognition system based on physiological signals. Electroencephalogram (EEG) and electrocardiogram (ECG) were used to recognize seven kinds of emotions, including normal (non-stimulated state), happy, stress, sad, disgust, anger, and surprise. The emotion recognition system was user-independent, and was divided into five parts, including physiological signal acquisition, signal segmentation, feature extraction, feature selection, and classification. In the physiological signal acquisition part, we acquired data from 10 participants who watched video programs of two to four minutes in length to stimulate distinct emotions in a quiet room. We then used technologies based on discrete wavelet transform and mutual information to find emotion-related EEG segments. Six categories of features were extracted from the EEG segment, including time-domain features, frequency-domain features, features of difference between response and baseline, differential channel features, high-order-statistic features, and nonlinear features. Seven categories of features were extracted from the ECG segment, including time-domain features, HRV features, Poincare plot features, baseline features, nonlinear features, frequency-domain features, and waveform features. Four feature selectors, including fisher discriminant analysis (FDA), sequential backward selection (SBS), sequential forward selection (SFS), and genetic algorithms (GA), proceeded to select useful features and reduce feature dimensions. Finally, leave-one-out cross-validation was performed in combination with either the k-nearest neighbor (KNN) classifier or the support vector machine (SVM) classifier to recognize seven kinds of emotions. The results demonstrated that the average classification accuracies base on EEG and ECG were 87.14% and 92.86% respectively when using the GA feature selector and the SVM classifier. The average classification accuracy increased to 97.14% when the features calculated from EEG and ECG signals were combined to work with the GA feature selector and the SVM classifier.
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Tsai, Hsin Chen, and 蔡信宸. "A System Modeling Approach to Electrocardiogram Analysis." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/30405405991676875951.
Full textAbstract:
碩士長庚大學
資訊工程學系
98
In this thesis, we propose a novel means that uses system modeling approach to extract fixed-length parameters from single-lead ECG data, such that Premature Ventricular Contraction (PVC) can be detected in real time. Our proposed method consists of two pipelined parts: the first part receives an ECG signal and identifies the corresponding AR (Auto Regressive) system by the recursive least squares method; the second part feeds the system parameters into a neural network for symptom analysis. Experimental results obtained from the MIT-BIH database, including 15 patients with PVC symptoms, verified the sensitivity, specificity and accuracy of our method; that were 87.27%, 93.35%, 91.92 % and 88.50%, 95.16%, 93.82% on patient-dependent and patient-independent cases respectively. This thesis analyzes ECG signals using system modeling rather than traditional data comparison approaches, and thus reduces the time complexity of the detection algorithm into linear. Featured by its low computing resource, linear time complexity and real-time symptom detection, our method could be implemented on embedded systems to form a portable ECG monitor, that will be helpful to the patients having cardiovascular disease history. The patients with frequent PVC records will be reminded immediately, and thus they can have medical treatment in time.
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Chou, Yi-Wen, and 周逸雯. "The Analysis of Electrocardiogram Feature in Disease." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/03214271949525441106.
Full textAbstract:
碩士元智大學
資訊管理學系
97
Abstract Heart attack is one of the major diseases leading to death based on recent reports. Therefore, it is crucial to develop a computer-assisted algorithm to improve conventional diagnoses of heart diseases. 12-lead ECG is a frequently-used diagnostic tool with the advantages of non-invasive measurement and convenient acquisition. Objective : The major objective of this study is to develop a hidden markov model (HMM) that can recognize the ECG features of 12-lead ECG. The HMM model then can be used to identify myocardial infarction that is a life-threaten disease and commonly-seen in clinical practice. Method : The 12-lead ECGs confirmed as myocardial infarction were acquired from clinically-used Philips XML-ECG. The waveforms in collected XML-ECG files were extracted and then processed to get clean ECG waveform data. The waveform data in various leads were used to build a HMM model. The HMM model using mixed Gaussian functions to model waveform pattern in time domain. Result : By using Maximum Likelihood Estimation in the process of HMM training, the optimal HMM model representing anterior myocardial infarction was evaluated. Results indicated that anterior myocardial infarction can be represented by two Gaussian mixture and twelve states in HMM. Conclusion : In sum, a computer-assisted myocardial infarction detector can be facilitated with the use of HMM. Keywords: Hidden Markov Model, 12-lead ECG, Myocardial Infarction
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Chen, Ying-Hsiang, and 陳穎祥. "Subband Decomposition Methods for Electrocardiogram Beat Discrimination." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/13403671395600740821.
Full textAbstract:
博士國立中正大學
電機工程所
97
Electrocardiogram (ECG) beat discrimination plays an important role in the clinical diagnosis of heart diseases. Although many ECG beat classification methods have been provided in the literature, there still leave room for improvement in view of different issues. Several significant issues, including recognition rates, noise-resistibility, and feature dimension, are considered in the dissertation for the development of an effective and efficient ECG beat classifier. In Chapter II, the discrete wavelet transformation is employed to decompose the ECG signals into different subband components. Statistical and morphological features are extracted to characterize the ECG signals. A probabilistic neural network (PNN) proceeds to discriminate different pathological heartbeat types. The results demonstrate that it provides a promising accuracy of 99.65%, with equally well recognition rates of over 99% throughout all heartbeat types in this study. In Chapter III, higher order statistics is recruited to accompany with the discrete wavelet decomposition to characterize the ECG signals as an attempt to elevate the noise-resistibility of the heartbeat discrimination. A feed-forward back-propagation neural network (FFBNN) is employed as classifier. More than 97.5% discrimination rate is achieved with a more complicated experimental profile in which multiple beat types are selected from each of the records for study. In Chapter IV, four nonlinear feature selection methods including Relief-F, two nonlinear correlation based filters (NCBFs), and symmetrical uncertainty feature-class only (SUFCO), are utilized to reduce the dimension of features mentioned in Chapter III. The results demonstrate that two NCBFs based on both feature-feature and feature-class correlation measures outperform the other methods. As high as 96.34% accuracies can be retained even with only eight features. At last, comparison between the proposed methods in Chapter II with another ECG beat discrimination based on independent component analysis and support vector machine (ICA-SVM) method is demonstrated in Chapter V. The results show that both ECG beat classification methods are insensitive to the stationary artifacts including white Gaussian noise and power line interference. The proposed method is especially tolerant to non-stationary artifacts baseline wander and muscle artifacts when compared to ICA-SVM. More than 90% accuracy can be retained with the proposed method even when the SNR is decreases to 10 dB.
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Shu, Horng-yih, and 許鴻義. "Design and Analysis of Electrocardiogram Measuring System." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/07080890915716071929.
Full textAbstract:
碩士國立雲林科技大學
機械工程系碩士班
91
A measuring system for weak electrical signals is built and analyzed in the thesis. Its performance is tested on the recording of electrocardiogram (ECG). The measurement system is divided into two parts. The first one is an amplifying circuit that solves the recording problems associated with electrodes. The second part includes a low-pass filter and a notch filter that attenuate the signal-contaminating noises. The filtering circuits are analyzed both using computer simulation and real-time testing. To examine the performance of the measurement signals, we first test it on a set of artificial ECG signals out of a data acquisition board generated by computer programs. The system is shown to be capable of eliminating offset voltage of the electrodes, 60Hz power-line noise, and signals of frequency higher than 2kHz. The system is then used to detect human ECG. From the analysis of the signals it can be confirmed that the measured waveforms are indeed wanted ECG signals.