The main goal of this thesis is to test the feasibility of human identification using the Electrocardiogram (ECG). Such biomedical signal has several key advantages including its intrinsic nature and liveness indicator which makes it more secure compared to some of the existing conventional and traditional biometric modalities. In compliance with the terms and regulations of McMaster University, this work has been assembled into a sandwich thesis format which consist of three journal papers. The main idea of this work is to identify individuals using distance measurement techniques and ECG feature matching. In addition, we gradually developed the content of the three papers. In the first paper, we started with the general criteria for developing ECG based biometric systems. To explain, we proposed both fiducial and non-fiducial approaches to extract the ECG features followed by providing comparative study on the performance of both approaches. Next, we applied non-overlapped data windows to extract the ECG morphological and spectral features. The former set of features include the amplitude and slope differences between the Q, R and S peaks. The later features include extracting magnitudes of the ECG frequency components using short time Fourier Transform (STFT). In addition, we proposed a methodology for QRS detection and segmentation using STFT and binary classification of ECG fiducial features. In the second paper, we proposed a technique for choosing overlapped data windows to extract the abovementioned features. Namely, the dynamic change in the ECG features from heart beats to heartbeat is utilized for identification purposes. To improve the performance of the proposed techniques we developed Frechet-mean based classifier for this application. These classifiers exploit correlation matrix structure that is not accounted for in classical Euclidean techniques. In addition to considering the center of the cluster, the Frechet-mean based techniques account for the shape of the cluster as well. In the third paper, the thesis is extended to address the variability of ECG features over multiple records. Specifically, we developed a multi-level wavelet-based filtering system which utilizes features for multiple ECGs for human identification purposes. In addition, we proposed a soft decision-making technique to combine information collected from multi-level identification channels to reach a common final class. Lastly, we evaluated the robustness of all our proposed methods over several random experiments by changing the testing data and we achieved excellent results. The results of this thesis show that the ECG is a promising biometric modality. We evaluated the performance of the proposed methods on the public ECG ID database because it was originally recorded for biometric purposes. In addition, to make performance evaluation more realistic we used two recordings of the same person obtained under possibly different conditions. Furthermore, we randomly changed both the training and testing data which are obtained from the full ECG records for performance evaluation purposes. However, it is worth mentioning that in all parts of the thesis, various parameters settings are presented to support the main ideas and it is subject to change according to human activity and application requirements. Finally, the thesis concludes with a comparison between all the proposed methods, and it provides suggestions on few open problems that can be considered for future research as extension to the work that has been done in this thesis. Generally, these problems are associated with the constraints on computational time, data volume and ECG clustering. / Thesis / Doctor of Philosophy (PhD)

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

The rise of machine learning has seen an increase in digital methods for use within health care. Arrythmia detection is one of the areas where this increase is obvious. However, many machine learning methods for arrythmia detection utilise models that are computationally expensive, such as convolutional neural networks, CNNs. This thesis examines whether it is viable to use the Fast Fourier Transform to transform an Electrocardiogram, ECG, signal into its frequency components before training a neural network, NN, on the data. This could allow for a lower computational cost and wider availability of arrythmia detection technology. The results from the model were compared to that of a CNN trained on time domain data. The results show that the CCN model outperforms the NN trained on FFT transformed data but the performance of the model still indicates that valuable information about heart arrythmias does exist within the frequency space. This suggests a potential for future work on the subject.

This master‘s thesis deals with the use of higher order cumulants for classification of cardiac cycles. Second-, third-, and fourth-order cumulants were calculated from ECG recorded in isolated rabbit hearts during experiments with repeated ischemia. Cumulants properties useful for the subsequent classification were verified on ECG segments from control and ischemic group. The results were statistically analyzed. Cumulants are then used as feature vectors for classification of ECG segments by means of artificial neural network.

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This thesis deals with classification of different types of time courses of ECG signals. Main objective was to recognize the normal cycles and several forms of arrhythmia and to classify the exact types of them. Classification has been done with usage of algorithms of Neural Networks in Matlab program, with its add-on (Neural Network Toolbox). The result of this thesis is application, which makes possible to load an ECG signal, pre-process it and classify its each cycle into five classes. Percentage results of this classification are in the conclusion of this thesis.

Cardiovascular diseases are the global leading cause of death. Automated electrocardiogram (ECG) analysis can support clinicians to identify abnormal excitation of the heart and prevent premature cardiovascular death. An explainable classification is particularly important for support systems. Our contribution to the PhysioNet/CinC Challenge 2021 (team name: ibmtPeakyFinders) therefore pursues an approach that is based on interpretable features to be as explainable as possible. To meet the challenge goal of developing an algorithm that works for both 12-lead and reduced lead ECGs, we processed each lead separately. We focused on signal processing techniques based on template delineation that yield the template's fiducial points to take the ECG waveform morphology into account. In addition to beat intervals and amplitudes obtained from the template, various heart rate variability and QT interval variability features were extracted and supplemented by signal quality indices. Our classification approach utilized a decision tree ensemble in a one-vs-rest approach. The model parameters were determined using an extensive grid search. Our approach achieved challenge scores of 0.47, 0.47, 0.34, 0.40, and 0.41 on hidden 12-, 6-, 4-, 3-, and 2-lead test sets, respectively, which corresponds to the ranks 12, 10, 23, 18, and 16 out of 39 teams.