yes / The aim of this book is to be able to interpret Electrocardiograms avoiding all possible errors. The accuracy of the interpretation is of great importance but a true diagnosis is far more significant. This book focuses on the recognition and interpretation of arrhythmias, one of the most important clinical tools in medicine. The greatest degree of accuracy is achieved by familiarising with the normal ECG that enables the recognition of abnormal patterns to be made immediately. Firstly, it is necessary to acquaint the function of the heart and the electrical activity in order to broaden our understanding of how the ECG detects this electrical activity. It is essential to know the characteristic patterns of a normal ECG and to categorise a wide array of morphologic patterns along with determining abnormal ECG patterns to be diagnostic of particular pathological entities. A series of practical experiments have been carried out on various subjects using the BIOPAC system to record electrical signals of the heart. Subjects were asked to perform various tasks such as lying down, sitting, deep breathing and exercising to detect electrical signals in different conditions and eventually interpret the data. The ethical issue toward each subject is also too important, so it was necessary to let the subject know about any risk factors during experiment. For this purpose, a Volunteer Information Sheet was designed during this work for each subject to read and be aware of all the ethical issues. Also, another Patient Consent Form was designed to make sure that each volunteer fully understands the procedures. Volunteer Questionnaire is necessary to make sure volunteer that there is no problem, which can affect the experimental results. Finally, ECG results were interpreted using a systematic approach and the precise findings were correlated with the pathophysiology and clinical status of the patient. This book concludes with a thorough investigation into the essential techniques and skills required to accurately interpret an ECG, eliminating as many errors as possible.

Master of Science / Department of Electrical and Computer Engineering / Steven Warren / A reference design for a wearable, wireless biomedical sensor set has been a long-term need for researchers at Kansas State University, driven by the idea that a basic set of sensor components could address the demands of multiple types of human and animal health monitoring scenarios if these components offered even basic reconfigurability. Such a reference design would also be a starting point to assess sensor performance and signal quality in the context of various biomedical research applications. This thesis describes the development of a set of wireless health monitoring sensors that can be used collectively as a data acquisition platform to provide biomedical research data and to serve as a baseline reference design for new sensor and system development. The host computer, an Intel Edison unit, offers plug-and-play usability and supports both Wi-Fi and Bluetooth wireless connectivity. The reference sensor set that accompanies the Intel Edison single-board computer includes an electrocardiograph, a pulse oximeter, and an accelerometer/gyrometer. All sensors are based on the same physical footprint and connector placement so that the sensors can be stacked to create a collection with a minimal volume and footprint. The latest hardware version is 3.1. Version 1.0 supported only a pulse oximeter, whereas version 2.0 included an electrocardiograph, pulse oximeter, and respiration belt. In version 3.0, the respiration belt was removed, and accelerometers and gyroscopes were added to the sensor set. Version 3.1 is a refined version of the latter design, where known hardware bugs were remedied. Future work includes the development of new sensors and casing designs that can hold these sensor stacks.

nÃo hà / A tarefa mais importante em processamento de sinais de eletrocardiograma (ECG) à a determinaÃÃo exata do complexo de QRS, em particular, a detecÃÃo dos picos de onda R atravÃs de sistemas e anÃlises computadorizadas. à essencial, especialmente, para uma medida correta da variabilidade do ritmo cardÃaco (HRV). Um grande obstÃculo a ser superado para uma detecÃÃo confiÃvel à a sensibilidade do eletrocardiograma a diversas fontes de distÃrbio, tais como, a interferÃncia à rede elÃtrica, os artefatos do movimento, flutuaÃÃo da linha base e o ruÃdo dos mÃsculos. Este trabalho utiliza as propriedades matemÃticas da transformaÃÃo de Hilbert sobre wavelets para desenvolver um novo algoritmo capaz de diferenciar as ondas R das demais (P, Q, S, T e U) e facilitar a detecÃÃo dos complexos QRS. Uma taxa de detecÃÃo do complexo QRS de 99,92% à alcanÃada para a base de dados de arritmias do MIT-BIH. A tolerÃncia a ruÃdo do mÃtodo proposto foi tambÃm testada usando os registros padrÃo da base de dados MIT-BIH Noise Stress Test. A taxa da detecÃÃo do detector ficou aproximadamente 99,35% mesmo para as relaÃÃes sinal-ruÃdo (SNR) tÃo baixo quanto 6dB. / The most important task in the ECG signal processing is the accurate determina-tion of QRS complex, in particular, accurate detection of the R wave peaks, is essential in computer-based ECG analysis especially for a correct measurement of Heart Rate Variability (HRV). A great hurdle to be overcome in reliable detection is the sensibility of the electrocar-diogram to several disturbance sources such as powering source interference, movement arti-facts, baseline wandering and muscle noise. This study uses the Hilbert Transform pairs of wavelet bases for QRS detection. From the properties of these mathematical tools it was pos-sible to develop an algorithm which is able to differentiate the R waves from the others (P, Q, S, T and U waves).The performance of the algorithm was verified using the records MIT-BIH arrhythmia and normal databases. A QRS detection rate of 99.92% was achieved against MIT-BIH arrhythmia database. The noise tolerance of the proposed method was also tested using standard records from the MIT-BIH Noise Stress Test Database. The detection rate of the detector remains about 99.35% even for signal-to-noise ratios (SNR) as low as 6dB.

Electrocardiogram (ECG) records the electrical impulses from myocardium, reflects the underlying dynamics of the heart and has been widely exploited to detect and identify cardiac arrhythmias. This dissertation examines a resource-saving cloud based long-term ECG (CLT-ECG) monitoring system which consists of an ECG raw data acquisition system, a mobile device and a serve. Three issues that are critically pertaining to the effectiveness and efficiency of the monitoring system are studied: the detection of life-threatening arrhythmias, the discrimination of normal and abnormal heartbeats to facilitate the resource-saving operation and the multi-class heartbeat classification algorithm for non-life-threatening arrhythmias. The detection algorithm for life-threatening ventricular arrhythmias, which is critical to saving patients’ lives, is investigated by exploiting personalized features. Two new personalized features, namely, aveCC and medianCC, are extracted based on the correlation coefficients between a patient-specific regular QRS-complex template and his/her real-time ECG data, characterizing subtle differences in the QRS complexes among different people. A small set of the most effective features is selected for efficient performance and real-time operation using Support Vector Machines (SVMs). The effectiveness of the proposed algorithm is validated in enhancing the performance under both the record-based and database-based data divisions. The classification algorithm achieves results outperforming the existing classification performances using top-two or top-three features. A novel patient-specific arrhythmia detection algorithm, which discriminates the normal and abnormal heartbeats, is proposed using One-Class SVMs. Conventionally, CLT-ECG systems are used to solve problems such as the portable problem and the difficulty of capturing the intermittent arrhythmias. However, CLT-ECG systems are subject to several practical limitations: battery power restriction, network congestion and heavily redundant ECG data. To overcome these problems, a resource-saving CLT-ECG system is studied, in which a novel arrhythmia detection algorithm closely related to the resource-saving rate is proposed and examined in detail. The proposed arrhythmia detection algorithm explores two types of variations: waveform change indicator (WCI), which reflects a change within one heartbeat; modified RR interval ratio (modRRIR), which characterizes the successive heartbeat interval variation. The overall classification result is obtained from combining the results separately adopting WCI and modRRIR. The proposed algorithm is validated using the public ECG database with a result outperforming others in the literature, as well as using the data collected from the ECG platform HeartCarer built in our research group. Considering the multi-class classification in the cloud server, a patient-specific single-lead ECG heartbeat classification strategy is proposed to discriminate ventricular ectopic beats (VEBs) and Supraventricular Ectopic Beats (SVEBs). Two types of features are extracted: Intra-beat features characterize the distortion of the waveform within one heartbeat, while inter-beat features reflect the variation between successive heartbeats. A novel fusion strategy consisting of a global classifier and a local classifier is presented. The local classifier is obtained using the high-confidence heartbeats extracted from the first 5-minute data of a specific patient, while the global classifier is trained by the public training data. The advantage of the developed strategy is that fully automatic classification is realized without the intervention of physicians. Finally, simulation results show that comparable or even better classification performance is achieved, which validates the effectiveness of the proposed strategy. / Graduate / 2019-03-19

abstract: Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications. / Dissertation/Thesis / M.S. Electrical Engineering 2013

nÃo hà / Sistemas automÃticos de auxÃlio ao diagnÃstico visam à extraÃÃo de mÃtricas especÃficas, podendo ser por algoritmos computacionais, de forma a subsidiar a anÃlise por parte do especialista de condiÃÃes orgÃnicas e fisiolÃgicas do paciente. No contexto da cardiologia, referidos sistemas sÃo particularmente importantes quando aplicados no processamento de sinais de longa duraÃÃo, como o eletrocardiograma (ECG) de 24 horas. As tÃcnicas para segmentaÃÃo e extraÃÃo automÃtica de parÃmetros do sinal ECG propostas nesta tese abrangem diversos campos de pesquisa. Inicialmente, o sistema realiza a detecÃÃo e a segmentaÃÃo do complexo QRS, relacionado à despolarizaÃÃo ventricular. Como metodologia, utiliza-se a combinaÃÃo das tÃcnicas do limiar adaptativo, das transformadas de Hilbert e Wavelet e do filtro derivativo com uma nova abordagem de reduÃÃo de prÃ-processamento e de seleÃÃo do fator de escala da Wavelet. Ao final desta etapa, obtÃm-se a sÃrie de intervalos RR, a sÃrie de duraÃÃes de cada complexo QRS e de suas amplitudes. No segundo momento, tem-se a detecÃÃo e a segmentaÃÃo da onda T, relacionada à repolarizaÃÃo ventricular. PropÃe-se um novo modelo matemÃtico do comportamento morfolÃgico da onda T baseado na funÃÃo Gaussiana, modificada por um procedimento matemÃtico de inserÃÃo de assimetria. Uma vez obtidos os parÃmetros de modelagem para uma dada morfologia predominante de onda T, a funÃÃo de correlaÃÃo cruzada à utilizada para a detecÃÃo do pico e uma tÃcnica baseada no cÃlculo da Ãrea de trapÃzios à utilizada para a localizaÃÃo do final da forma de onda. Dentre as mÃtricas derivadas das informaÃÃes extraÃdas, destaca-se a sÃrie de intervalos QT, segmento que vai do inÃcio de cada complexo QRS ao final de cada onda T. Finalizado o processo de segmentaÃÃo, dois estudos de caso sÃo realizados: subtraÃÃo da atividade ventricular em sinais eletrogramas atriais de pacientes com fibrilaÃÃo atrial (FA) e anÃlise de sÃries de variabilidade da frequÃncia cardÃaca (VFC) de um conjunto de pacientes idosos selecionados pelo AmbulatÃrio de Geriatria do Hospital UniversitÃrio WÃlter CantÃdio. A partir de experimentos de validaÃÃo em bases de dados diversas com anotaÃÃes manuais dos batimentos, obtÃm-se as seguintes taxas de detecÃÃo e erros de delineamento para o complexo QRS: sensibilidade de 99,51%, preditividade positiva de 99,44%, erro mÃdio de inÃcio (QRS onset) de 2,85  9,90 ms e erro mÃdio de final (QRS offset) de 2,83  12,26 ms. Com relaÃÃo à detecÃÃo e segmentaÃÃo da onda T, obtÃm-se os seguintes resultados: sensibilidade de 99,48%, preditividade positiva de 99,53%, erro mÃdio de localizaÃÃo de pico de 0,51  8,06 ms e erro mÃdio de localizaÃÃo de final da forma de onda de 0,11  11,73 ms. Quanto ao primeiro estudo de caso de uso dos pontos fiduciais detectados, a potÃncia mÃdia dos sinais eletrogramas atriais, apÃs a subtraÃÃo da atividade ventricular, à significativamente reduzida para frequÃncias acima de 10 Hz, predominantemente associadas ao complexo QRS, bem como para frequÃncias na faixa de 3 a 5 Hz, relacionadas à atividade elÃtrica de repolarizaÃÃo ventricular. Para o segundo estudo, a anÃlise do comportamento de mÃtricas no domÃnio da frequÃncia associadas à atividade do sistema nervoso simpÃtico permite o reconhecimento de tendÃncias prÃprias e caracterÃsticas, no que tange a aspectos de funcionamento/disautonomia do sistema nervoso autonÃmico, de cada classe prÃ-determinada de idosos segundo os conceitos de fenÃtipo de fragilidade: idosos frÃgeis, prÃ-frÃgeis e robustos. Os resultados obtidos sugerem que o conjunto de metodologias desenvolvidas para a segmentaÃÃo do sinal ECG apresenta altas taxas de precisÃo, repetibilidade e robustez a uma ampla gama de morfologias, podendo ser aplicado em diversos contextos de auxÃlio ao diagnÃstico. Dadas as mÃtricas e sÃries temporais que podem ser extraÃdas, os referidos mÃtodos tambÃm podem dar suporte a processos de investigaÃÃo clÃnica e desenvolvimento de marcadores/indicadores de eventos cardiovasculares adversos.

The electro-optic technique of measuring the cardiovascular pulse wave known as photoplethysmography (PPG) is clinically utilised for noninvasive characterisation of physiological components by dynamic monitoring of tissue optical absorption. There has been a resurgence of interest in this technique in recent years, driven by the demand for a low cost, compact, simple and portable technology for primary care and community-based clinical settings, and the advancement of computer-based pulse wave analysis techniques. PPG signal provides a means of determining cardiovascular properties during the cardiac cycle and changes with ageing and disease. This thesis focuses on the photoplethysmographic signal for cardiovascular assessment. The contour of the PPG pulse wave is influenced by vascular ageing. Contour analysis of the PPG pulse wave provides a rapid means of assessing vascular tone and arterial stiffness. In this thesis, the parameters extracted from the PPG pulse wave are examined in young adults. The results indicate that the contour parameters of the PPG pulse wave could provide a simple and noninvasive means to study the characteristic change relating to arterial stiffness. The pulsatile component of the PPG signal is due to the pumping action of the heart, and thus could reveal the circulation changes of a specific vascular bed. Heart rate variability (HRV) represents one of the most promising quantitative markers of cardiovascular control. Calculation of HRV from the peripheral pulse wave using PPG, called pulse rate variability (PRV), is investigated. The current work has confirmed that the PPG signal could provide basic information about heart rate (HR) and its variability, and highly suggests a good alternative to understanding dynamics pertaining to the autonomic nervous system (ANS) without the use of an electrocardiogram (ECG) device. Hence, PPG measurement has the potential to be readily accepted in ambulatory cardiac monitoring due to its simplicity and comfort. Noncontact PPG (NPPG) is introduced to overcome the current limitations of contact PPG. As a contactless device, NPPG is especially attractive for physiological monitoring in ambulatory units, NICUs, or trauma centres, where attaching electrodes is either inconvenient or unfeasible. In this research, a prototype for noncontact reflection PPG (NRPPG) with a vertical cavity surface emitting laser (VCSEL) as a light source and a high-speed PiN photodiode as a photodetector is developed. The results from physiological experiments suggest that NRPPG is reliable to extract clinically useful information about cardiac condition and function. In summary, recent evidence demonstrates that PPG as a simple noninvasive measurement offers a fruitful avenue for noninvasive cardiovascular monitoring. Key words: Photoplethysmography (PPG), Cardiovascular assessment, Pulse wave contour analysis, Arterial stiffness, Heart rate (HR), Heart rate variability (HRV), Pulse rate variability (PRV), Autonomic nervous system (ANS), Electrocardiogram (ECG).

This master’s thesis deals with methods for detection of myocardial ischemia in the ECG signal. There is explained the principle of spreading of electrical activity through the heart muscle and its manifestations on the ECG. There are also mentioned the causes of myocardial ischemia and various methods of its detection in the ECG signal. In great detail there is explained the process of implementation of the two selected detection methods of myocardial ischemia in MATLAB. These methods are tested on the data from The PTB Diagnostic ECG Database. Finally, there is the presentation of detection results on used data and overall assessment of created algorithms.

In today’s research and market, IT applications for health-care are gaining huge interest of both IT and medical researchers. Cardiovascular diseases (CVDs) are considered the largest cause of death for both men and women regardless of ethnic backgrounds. More efficient treatments and most importantly efficient methods of cardiac diagnosis that examine heart diseases are desired. Electrocardiography (ECG) is an essential method used to diagnose heart diseases. However, diagnosing any cardiovascular disease based on the 12-lead ECG printout from an ECG machine using human eye might seriously impair analysis accuracy. To meet this challenge of today’s ECG analysis methodology, a more reliable solution that can analyze huge amount of patient’s data in real-time is desired. The software solution presented in this article is aimed to reduce the risk while diagnosing cardiovascular diseases (CVDs) by human eye, computation of large-scale patient’s data in real-time at the patient’s location and sending the required results or summary to the doctor/nurse. Keeping in mind the importance of real-time analysis of patient’s data, the software system has built upon small individual algorithms/modules designed for multi-core architecture, where each module is supposed to be processed by an individual core/processor in parallel. All the input and output processes to the analysis system are made automated, which reduces operator’s interaction to the system and thus reducing the cost. The outputs/results of the processing are summarized to smaller files in both ASCII and binary formats to meet the requirement of exchanging the data over Voice and Data Networks.

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