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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.

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The thesis is composed of tow topics: a high performance current-balancing instrumentation amplifier (IA) for ECG (Electrocardiogram) monitoring systems and an IA with CMRR (Common-Mode Rejection Ratio) self-calibration. In the first topic, a high common mode rejection ratio (CMRR) and a low input referred noise instrumentation amplifier (IA) is presented for ECG applications. A high pass filter (HPF) with a small-Gm OTA using a current division technique is employed to attain small transconductance, which needs only a small capacitor in the HPF such that the integration on silicon is highly feasible. The proposed design is carried out by TSMC standard 0.18 £gm CMOS technology. CMRR is found to be 127 dB and the voltage gain is 45 dB according to the simulation results. The second topic discloses an instrumentation amplifier with CMRR self-calibration capability. The propose design is also carried out by TSMC standard 0.18 £gm CMOS technology. To achieve a CMRR of more than 80 dB, a calibration resistance string and a detection circuit have been utilized. The DC gain of the proposed design is 60 dB and the frequency bandwidth is bound in 10 KHz, which is adaptable for biomedical signal acquisition applications.

Η παρούσα εργασία πραγματεύεται την αποτελεσματικότητα του αλγορίθμου Matching Pursuit στον τομέα της επεξεργασίας σήματος. Περιγράφονται οι γενικότερες δυνατότητες του στην επεξεργασία βασικών βιοϊατρικών σημάτων και ειδικότερα εξετάζονται τα πλεονεκτήματά του στην επεξεργασία του ηλεκτροκαρδιογράφηματος, μιας και η εργασία αποτελεί μέρος του προγράμματος τηλεκαρδιολογίας e-Herofilus. Αρχικά λοιπόν γίνεται μία αναλυτική περιγραφή του τρόπου λήψης και καταγραφής του καρδιογραφικού σήματος. Στη συνέχεια γίνεται μία περιγραφή του αλγορίθμου Matching Pursuit και των αποτελεσμάτων που είχε η εφαρμογή του σε γνωστά βιοσήματα και κυρίως σε εγκεφαλογραφήματα και καρδιογραφήματα, από όπου προκύπτουν τα πλεονεκτήματα της συγκεκριμένης μεθόδου έναντι των άλλων μεθόδων που έχουν χρησιμοποιηθεί στο παρελθόν για τη μεταφορά και επεξεργασία σημάτων στο πεδίο χρόνου- συχνότητας. Τέλος ακολουθεί η υλοποιήση του αλγορίθμου του Matching Pursuit και η εφαρμογή του σε πραγματικά ηλεκτροκαρδιογραφήματα τόσο υγιή όσο και παθολογικά, που λήφθηκαν από τη βάση Physionet του MIT. Η ανάλυση των αποτελεσμάτων της εφαρμογής αυτής οδηγεί σε συμπεράσματα σχετικά με την αξία και την αποτελεσματικότητα του αλγορίθμου στους τομείς της αποθορυβοποίησης του καρδιογραφικού σήματος και του εντοπισμού της χρήσιμης πληροφορίας που αυτό μεταφέρει. Επίσης, μέσα από τη σύγκριση υγιών και παθολογικών καρδιογραφημάτων γίνεται μία προσπάθεια αναγνώρισης στοιχείων που εμφανίζονται στο πεδίο χρόνου-συχνότητας και συσχέτισής τους με τις εκάστοτε παθολογίες, γεγονός που μελλοντικά μπορεί να αποτελέσει το εφαλτήριο για την ανάπτυξη αλγορίθμου αυτόματης διάγνωσης καρδιογραφημάτων. / The following project, which is a part of telemedicine program e-Herofilus, examines the effectiveness of Matching Pursuit method. Its ability of processing in biomedical signals is described in a general format, as well as the advantages in processing of electrocardiogram (ECG) in more details. Firstly, there is an analytical description of collection and record of ECG. Afterwards, there is a description of Matching Pursuit algorithm and its results in biosignal processing. The algorithm is applied in electrocardiogram (ECG) and electroencephalogram (EEG). The results of those applications showed the advantages of Matching Pursuit method over the other methods were used in the past in the signal processing field in the time frequency plane. Finally, the implementation of Matching Pursuit algorithm in real ECGs is following. These ECGs are taken from healthy as well as from pathological specimens. The source of these specimens is Physionet Bank of MIT. In the conclusions of this project, it is underlined the value and the efficiency of Matching Pursuit method in denoising of ECG, and in detecting the useful signal information in time frequency plane. Additionally, comparing the results of processing healthy and pathological ECGs, could lead us in the future in the development of an automated diagnosis algorithm, which can be an innovation in both Engineering and Medicine Sciences.

Indiana University-Purdue University Indianapolis (IUPUI) / With passage of time and repeated usage of a system, component values that make up the system parameters change, causing errors in its functional output. In order to ensure the fidelity of the results derived from these systems it is thus very important to keep track of the system parameters while being used. This thesis introduces a method for tracking the existing system parameters while the system was being used using the inherent noise of its signal source. Kalman filter algorithm is used to track the inherent noise response to the system and use that response to estimate the system parameters. In this thesis this continuous characterization scheme has been used on a Universal Invertible Amplifier (UIA). Current biomedical research as well as diagnostic medicine depend a lot on shape profile of bio-electric signals of different sources, for example heart, muscle, nerve, brain etc. making it very important to capture the different event of these signals without the distortion usually introduced by the filtering of the amplifier system. The Universal Invertible Amplifier extracts the original signal in electrodes by inverting the filtered and compressed signal while its gain bandwidth profile allows it to capture from the entire bandwidth of bioelectric signals. For this inversion to be successful the captured compressed and filtered signals needs to be inverted with the actual system parameters that the system had during capturing the signals, not its original parameters. The continuous characterization scheme introduced in this thesis is aimed at knowing the system parameters of the UIA by tracking the response of its source noise and estimating its transfer function from that. Two types of source noises have been tried out in this method, an externally added noise that was digitally generated and a noise that inherently contaminates the signals the system is trying to capture. In our cases, the UIA was used to capture nerve activity from vagus nerve where the signal was contaminated with electrocardiogram signals providing us with a well-defined inherent noise whose response could be tracked with Kalman Filter and used to estimate the transfer function of UIA. The transfer function estimation using the externally added noise did not produce good results but could be improved by means that can be explored as future direction of this project. However continuous characterization using the inherent noise, a bioelectric signal, was successful producing transfer function estimates with minimal error. Thus this thesis was successful to introduce a novel approach for system characterization using bio-signal contamination.

3D printed on-skin electrodes are of notable interest because, unlike traditional wet silver/silver chloride (Ag/AgCl) on-skin electrodes, they can be personalized and 3D printed using a variety of materials with distinct properties such as stretchability, conformal interfaces with skin, biocompatibility, wearable comfort, and, finally, low-cost manufacturing. Dry on-skin electrodes, in particular, have the additional advantage of replacing electrolyte gel, which dehydrates and coagulates with prolonged use. However, issues arise in performance optimization with the recently discovered dry materials. These challenges become even more critical when the on-skin electrodes are scaled down to a miniaturized size, making the detection of various biosignals while keeping mechanical resilience under several conditions crucial. Thus, this thesis focuses on designing, fabricating, optimizing, and applying a personalized, fully 3D-printed permeable skin patch with miniaturized octopus-like suckers and embedded microchannels for enhanced mechanical strengths, breathability, and biosignal monitoring. The developed device showcases a rapid, cost-effective fabrication process of porous skin patches and the printing process of ink metal-based materials that expands its applications to low-resource settings and environments.

As technology has advanced, data has become more and more important. The more breakthroughs are achieved, the more data is needed to support them. As a result, more storage is required in the system's memory. Compression is therefore required. Before it can be stored, the data must be compressed. To ensure that information is not lost, efficient compression is necessary. This also makes sure that there is no redundancy in the data that is being kept and stored. Compressive sensing has emerged as a new field of compression thanks to developments in sparse optimization. Rather than relying just on compression and sensing formulations, the theory blends the two. The objective of this thesis is to analyze the concept of compressive sensing and to study several reconstruction algorithms. Additionally, a few of the algorithms were put into practice. This thesis also included a model of the ECG, which is vital in determining the health of the heart. For the most part, the ECG is utilized to diagnose heart illness, and a modified synthetic ECG can be used to mimic some of these arrhythmias.

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / The 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.

This thesis investigates the mechanisms underlying drug-induced arrhythmia and pro- poses a new approach for the automated analysis of the electrocardiogram (ECG). The current method of assessing the cardiac safety of new drugs in clinical trials is by the measurement and analysis of the QT interval. However, the sensitivity and specificity of the QT interval has been questioned and alternative biomarkers based on T-wave mor- phology have been proposed in the literature. The mechanisms underlying drug effects on T-wave morphology are not clearly understood. Therefore, a combined approach of for- ward cardiac modelling and inverse ECG analysis is adopted to investigate the effects of sotalol, a compound known to have pro-arrhythmic effects, on ventricular repolarisation. A computational model of sotalol and IKr, an ion channel that plays a critical role in ventricular repolarisation, was developed. This model was incorporated into a model of the human ventricular myocyte, and subsequently arranged in a 1-D fibre model of 200 cells. The model was used to assess the effect of sotalol on IKr, action potential duration and biomarkers of ventricular repolarisation derived from the simulated ECG. In parallel, an automated ECG analysis method based on machine learning, signal processing and time-frequency analysis is developed to identify a number of fiducial points in ECG waveforms so that timing intervals and a smooth T-wave segment can be extracted for morphology analysis. The approach is to train a hidden Markov model (HMM) using a data set of ECG waveforms and the corresponding expert annotations. The signal is first encoded using the undecimated wavelet transform (UWT). The UWT coefficients are used for R-peak detection, signal encoding for the HMM and a wavelet de-noising procedure. Using the Viterbi algorithm, the trained HMM is then applied to a subset of the ECG signal to infer the fiducial points for each heart beat. Furthermore, a method for deriving a confidence measure based on the trained HMM is implemented so that a level of confidence can be associated with the automated annotations. Finally, the T-wave segment is extracted from the de-noised ECG signal for morphology characterisation. This thesis contributes to the literature on automated characterisation of drug ef- fects on ventricular repolarisation in three different ways. Firstly, it investigates the mechanisms underlying the effects of drug inhibition of IKr on ventricular repolarisation as captured by the simulated ECG signal. Secondly, it shows how the combination of UWT encoding and HMM inference can be effectively used to segment 24-hour Holter ECG recordings. Evaluation of the segmentation algorithm on a clinical ECG data set demonstrates the ability of the algorithm to overcome problems associated with existing automated systems, and hence provide a more robust analysis of ECG signals. Finally, the thesis provides insight into the drug effects of sotalol on ventricular repolarisation as captured by biomarkers extracted from the surface ECG.