Electrocardiogram is a pictorial representation of the electrical activity of heart beats. Because of the direct relationship between the ECG waveform and interval of the heart beats, it is possible that doctor can diagnose cardiac disease and monitor patient conditions from the unusual ECG waveforms. Based on the wavelet transform, this work introduces an algorithm to detect QRS complex. In particular, the quadratic spline wavelet has been adopted. The thesis first reviews wavelet transform briefly, then develops a QRS detention algorithm, which is then tested by using the MIT-BIH arrhythmia database. It is hoped that the proposed QRS detection algorithm can be a useful tool for medical personnel who are interested in using QRS information to explore their research work.

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Electrocardiography (ECG) is the most important noninvasive tool used for diagnosing heart diseases. An ECG interpretation program can help the physician state the diagnosis correctly and take the corrective action. Detection of the QRS complexes from the ECG signal is usually the first step for an interpretation tool. The main goal in this thesis was to develop robust and high performance QRS detection algorithms, and using the results of the QRS detection step, to classify these beats according to their different pathologies. In order to evaluate the performances, these algorithms were tested and compared in Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database, which was developed for research in cardiac electrophysiology. In this thesis, four promising QRS detection methods were taken from literature and implemented: a derivative based method (Method I), a digital filter based method (Method II), Tompkin&rsquo / s method that utilizes the morphological features of the ECG signal (Method III) and a neural network based QRS detection method (Method IV). Overall sensitivity and positive predictivity values above 99% are achieved with each method, which are compatible with the results reported in literature. Method III has the best overall performance among the others with a sensitivity of 99.93% and a positive predictivity of 100.00%. Based on the detected QRS complexes, some features were extracted and classification of some beat types were performed. In order to classify the detected beats, three methods were taken from literature and implemented in this thesis: a Kth nearest neighbor rule based method (Method I), a neural network based method (Method II) and a rule based method (Method III). Overall results of Method I and Method II have sensitivity values above 92.96%. These findings are also compatible with those reported in the related literature. The classification made by the rule based approach, Method III, did not coincide well with the annotations provided in the MIT-BIH database. The best results were achieved by Method II with the overall sensitivity value of 95.24%.

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In many developing countries health care conditions are poor and there is a lack of healthcare professionals and diagnostics tools. Cheap and easy-to-use diagnostics tools have been developed to make practicing medicine easier under these conditions. However, signal monitors can be many and spread out, making it hard for the limited number of medical workers to handle. The monitors are also stationary, making mobile supervision impossible. In this thesis a solution is suggested, made of a hardware setup consisting of an Arduino UNO and Bluetooth module paired with an application, capable of analog to digital conversion, wireless transfer and display of medical signals. Furthermore, two different QRS detection algorithms are tested, a larger and accurate model called Pan-Tompkins and a smaller and faster, moving average based filtering system. The transmission circuit as well as the signal displayed showed promise. However, the analog to digital conversion was noisy due to the power source. The tested algorithms showed that speed and low computational requirements are traded for precision.

This master’s thesis deals with the detection of QRS complex detection using zero crossing counts. QRS detection is an important part of the analysis of ECG signal. From the point of determining the R wave detection is based on the other waves and intervals necessary for the diagnosis of heart. This method is very effective even for very noisy signals. Implementation of the method in Matlab, and the success of detection is tested on the CSE and MIT-BIH database. The optimization algorithm is an optional value for the detector.

The Hilbert transform is a widely used transform in signal processing. In this thesis we explore its use for three different applications: electrocardiography, the Hilbert-Huang transform and modulation. For electrocardiography, we examine how and why the Hilbert transform can be used for QRS complex detection. Also, what are the advantages and limitations of this method? The Hilbert-Huang transform is a very popular method for spectral analysis for nonlinear and/or nonstationary processes. We examine its connection with the Hilbert transform and show limitations of the method. Lastly, the connection between the Hilbert transform and single-sideband modulation is investigated.

Electrocardiography (ECG) has been established as one of the most useful diagnostic tools in medicine and is critical in the management of various heart conditions. Automated or semi-automated ECG analysis algorithms are expected to play an important role in the utilization of the ECG data. The correct identification of the QRS complexes is a fundamental step in every ECG analysis method. A major problem that is often encountered in automatic QRS detection is the presence of artifacts in the ECG data, which cause considerable alterations to the signal. Some common filters can smooth the effect of the artifacts, however they cannot eliminate them due to their spectral frequency overlap with the signal components. In this thesis, the objective was to develop a method, based on Time-Frequency Analysis that would be able to automatically detect and remove artifacts in order to increase the reliability of automatic QRS detection. The ECG data used for this purpose was taken from the Physionet library and more specifically from the MIMIC II database. The data in this database was acquired from ICU patients and it contains various types of rhythms as well as artifacts. First, a Graphical User Interface (GUI) was developed in order to manually annotate ECG data and was used for creating the ground truth for testing the methods developed. The Time-Frequency Analysis method used for the analysis of the ECG data, was based on a time-varying Autoregressive (AR) model whose solutions were obtained using Burg’s method. Several factors that affect the effectiveness of the method were investigated in order to optimize the algorithm experimentally. The algorithm implemented performs three main functions: “Artifact Hypothesis Testing,” “Artifact Detection and Removal,” and “QRS Complex Detection.” The first step, “Artifact Hypothesis Testing,” examines whether the signal contains any artifact or not. This is performed with a correct classification rate of 95.56%. The second step was the “Artifact Detection and Removal,” which could detect and remove the artifact area with an accuracy of 95.60% based on each signal sample identified as artifact or not. The final step, the “QRS Complex Detection,” correctly identified 92% of QRS complexes (322 out of 335 annotated QRS complexes). Finally, the proposed method was compared with one of the most commonly used methods in ECG analysis, the Wavelet Transform Analysis (WTA). The two methods were tested on exactly the same dataset. The WTA resulted in an overall score of 65.3% mainly due to the large number of false positive detections in the regions of artifact. / Το ηλεκτροκαρδιογράφημα (ΗΚΓ) έχει καθιερωθεί ως ένα από τα πιο χρήσιμα εργαλεία διάγνωσης στην ιατρική και είναι πολύ σημαντικό στη διαχείριση καρδιαγγειακών παθήσεων. Αυτοματοποιημένοι ή ημι-αυτοματοποιημένοι αλγόριθμοι ανάλυσης του ΗΚΓ αναμένεται να έχουν σημαντικό ρόλο στη χρήση των δεδομένων του ΗΚΓ. Η σωστή αναγνώριση των συμπλεγμάτων QRS είναι βασικό βήμα σε κάθε μέθοδο ανάλυσης του ΗΚΓ. Ένα σημαντικό πρόβλημα που συχνά προκύπτει σε αυτόματη ανίχνευση QRS είναι η παρουσία των τεχνητών σφαλμάτων (artifacts) στα δεδομένα ΗΚΓ, τα οποία προκαλούν σημαντικές αλλαγές στο σήμα. Κάποια κοινά φίλτρα μπορούν να εξομαλύνουν τις επιπτώσεις των τεχνητών σφαλμάτων, ωστόσο δεν μπορούν να τα εξαλείψουν λόγω της μεγάλης επικάλυψης του φάσματος συχνοτήτων τους με αυτού των στοιχείων του σήματος. Στην παρούσα εργασία στόχος ήταν η ανάπτυξη μιας μεθόδου, βασισμένης στην Ανάλυση Χρόνου-Συχνότητας, που θα είναι σε θέση να εντοπίσει αυτόματα και να αφαιρεί τα τεχνητά σφάλματα, ώστε να έχουμε μια πιο αξιόπιστη μέθοδο αυτόματης ανίχνευσης των QRS. Τα δεδομένα ΗΚΓ που χρησιμοποιήθηκαν για το σκοπό αυτό λήφθηκαν από τη βιβλιοθήκη Physionet και πιο συγκεκριμένα από τη βάση δεδομένων MIMIC II. Τα δεδομένα σε αυτή τη βάση δεδομένων προέρχονται από ασθενείς της Μονάδας Εντατικής Θεραπείας, και ως εκ τούτου, περιέχουν διάφορα είδη ρυθμών αλλά και τεχνητών σφαλμάτων. Αρχικά, ένα Γραφικό Περιβάλλον Χρήστη (GUI), σχεδιάστηκε για τη χειροκίνητη σηματοδότηση των διάφορων περιοχών ΗΚΓ σημάτων και χρησιμοποιήθηκε για τη δημιουργία των αληθών αποτελεσμάτων για δοκιμή της μεθόδου. H Ανάλυση Χρόνου-Συχνότητας έγινε με τη χρήση ενός χρονικά μεταβαλλόμενου Αυτοπαλινδρομικού (AR) μοντέλου οι λύσεις του οποίου βρέθηκαν με τη μέθοδο Burg. Ακολούθησε η διερεύνηση διαφόρων παραγόντων που επηρεάζουν την αποτελεσματικότητα της μεθόδου, προκειμένου να βελτιστοποιηθεί πειραματικά η μέθοδος. Ο αλγόριθμος που υλοποιήθηκε εκτελεί τρεις βασικές λειτουργίες: “Artifact Hypothesis Testing,” “Artifact Detection and Removal” και “QRS Complex Detection.” Κατ’ αρχήν, το βήμα "Artifact Hypothesis Testing" εξετάζει αν το σήμα περιέχει τεχνητό σφάλμα ή όχι, με το ποσοστό σωστής ταξινόμησης να ανέρχεται στο 95.56%. Το δεύτερο βήμα, η ανίχνευση και αφαίρεση της περιοχής του τεχνητού σφάλματος, έγινε με ακρίβεια 95.60% με βάση το πόσα σημεία του σήματος αναγνωρίστηκαν ως τεχνητό σφάλμα ή όχι. Τέλος, το συνολικό ποσοστό ορθής ανίχνευσης των συμπλεγμάτων QRS ήταν 92% (322 από τα 335 QRS που επισημάνθηκαν χειροκίνητα). Τέλος, έγινε μια σύγκριση μεταξύ της προτεινόμενης μεθόδου και μιας μεθόδου ανάλυσης ΗΚΓ που χρησιμοποιείται πολύ συχνά, της ανάλυσης με Μετασχηματισμό Wavelet (WTA). Οι δύο μέθοδοι δοκιμάστηκαν στα ίδια ακριβώς δεδομένα. Η ορθή ανίχνευση των συμπλεγμάτων QRS με τη μέθοδο WTA ήταν 65.3% κυρίως λόγω του μεγάλου αριθμού ψευδώς θετικών αποτελεσμάτων στις περιοχές των τεχνητών σφαλμάτων.

Automatic detection and delineation of ECG characteristic points is a basic procedure of any analyze of ECG using computer. This detection is a necessary step to simplify the work of cardiologists to evaluate long ECG records. In this thesis is proposed and evaluate a method of detection and delineation in a single-lead ECG using dyadic wavelet transform followed by correction in pseudo-orthogonal lead system taken from standard 12-lead system. The method uses information about position of positive maximum – negative minimum pair to detect ECG characteristic waves. At first the QRS complex is detected and than its morphology (waves Q and S) and the onset and end of the complex. After that the T-wave is detected and delineated within a searching window dependent on QRS position. And last the P-wave is detected and delineated. There are used two types of wavelets in developed method, “haar” and “quadratic spline”. The developed method was evaluated on CSE database. When haar wavelet was used the QRS detector sensitivity was 99.14%. In the work is also evaluated the accuracy of delineation characteristic points. As the P-wave and QRS complex delineation produced quite good results the T-wave end delineator produced relatively big deviations. All deviations are presented in histograms.

The aim of diploma thesis named “QRS detection using wavelet transform” is to simplify and accelerate the work of doctors. This can be achieved by using application for QRS detection, which can use one of four proposed algorithms. Application shows basic informations about inserted electrocardiogram. Part of this program is a graphical window with displayed record and with coloured marks on detected QRS complexes. By another algorythm are marks color-coded due to accurancy percentil of every detected complex. This program is designed for a several hours record from Holter ECG monitoring.