Feature extraction of electrocardiogram signal using machine learning classification

In this article, we'll introduce ways to build virtual worlds through different computer programs. We will show the method of rectangles for analyzing data obtained from the electroencephalogram. We will demonstrate basic mathematical models for movement prediction in a system of virtual reality. Using this data, the main transformations are possible-change of position and rotation (change of orientation)

Information Theoretic Methods For Biometrics, Clustering, And Stemmatology

This thesis consists of four parts, three of which study issues related to theories and applications of biometric systems, and one which focuses on clustering. We establish an information theoretic framework and the fundamental trade-off between utility of biometric systems and security of biometric systems. The utility includes person identification and secret binding, while template protection, privacy, and secrecy leakage are security issues addressed. A general model of biometric systems is proposed, in which secret binding and the use of passwords are incorporated. The system model captures major biometric system designs including biometric cryptosystems, cancelable biometrics, secret binding and secret generating systems, and salt biometric systems. In addition to attacks at the database, information leakage from communication links between sensor modules and databases is considered. A general information theoretic rate outer bound is derived for characterizing and comparing the fundamental capacity, and security risks and benefits of different system designs. We establish connections between linear codes to biometric systems, so that one can directly use a vast literature of coding theories of various noise and source random processes to achieve good performance in biometric systems. We develop two biometrics based on laser Doppler vibrometry: LDV) signals and electrocardiogram: ECG) signals. For both cases, changes in statistics of biometric traits of the same individual is the major challenge which obstructs many methods from producing satisfactory results. We propose a ii robust feature selection method that specifically accounts for changes in statistics. The method yields the best results both in LDV and ECG biometrics in terms of equal error rates in authentication scenarios. Finally, we address a different kind of learning problem from data called clustering. Instead of having a set of training data with true labels known as in identification problems, we study the problem of grouping data points without labels given, and its application to computational stemmatology. Since the problem itself has no true answer, the problem is in general ill-posed unless some regularization or norm is set to define the quality of a partition. We propose the use of minimum description length: MDL) principle for graphical based clustering. In the MDL framework, each data partitioning is viewed as a description of the data points, and the description that minimizes the total amount of bits to describe the data points and the model itself is considered the best model. We show that in synthesized data the MDL clustering works well and fits natural intuition of how data should be clustered. Furthermore, we developed a computational stemmatology method based on MDL, which achieves the best performance level in a large dataset

Robust ECG based person identification system

Identity theft is a burgeoning issue. Gaining unauthorized access to computer network tends to compromise the system which could potentially cause undetected fatal destruction and disastrous consequences for individuals and the nation. It is to the extent of taking down communication networks, paralyzing transportation systems and crippling power grids. If security system are burdensome, people may avoid using them, preferring functionality and convenience. For these reasons, an effective security mechanism needs to be deployed in combating identity crimes. Therefore, this thesis proposes of implementing biometric technology as a viable solution for the aforementioned problems. In the recent years, the electrocardiogram (ECG) signal was introduced as a potential biometric modality to overcome issues of currently available biometric attributes which could be falsified by gummy fingerprints, static iris and face images, voice mimics and fake signatures. When a person is having a heartbeat, automatically it proclaims that the person exist and is alive. Thus, the advantage as a life indicator mechanism verifies the presence of a person during the time of recognition. For the past decade, preliminary investigations on the validity of using ECG based biometric have been manifested with different person recognition methods to support its usability in security and privacy applications. Even though, ECG based biometric has set its ground in recognizing people, however, the underlying issues that governs a practical biometric system have not been properly addressed. Basic problems which require further attention are fundamental issues which touch the aspects of reliability and robustness of an ECG based biometric system in a real life scenario. Thus, in this thesis, we have identified four main research problems which are essentially important to increase user acceptability of ECG based biometric recognition covering different aspects of a practical biometric system such as distinctiveness, permanence, collectability and performance. The research issues being posed in this thesis are the selection of extracted biometric features, subject recognition with different pathological and physiological conditions, performing biometric with low sampling frequency signals and applying ECG based biometric in mobile surroundings. This thesis suggests of solving ECG based biometric recognition raised problems in a holistic perspective which does not limit the implementations to certain groups of users but looking at the issue as a whole and in a boarder avenue so that it could be applicable to almost all walks of life. A single optimum biometric system that supersedes others does not exist as each biometric modality is based on the nature of the implementation and application. Nevertheless, ECG based biometric features give a strong indication that it would be well accepted by users in the future due to the automatic liveness detection factor which is available in every human being that further expands to people with disabilities such as amputees and those who are visually impaired. Therefore, this thesis is substantial and vital as to assist and provide alternative person identification mechanism to present security and privacy applications in the quest to combat identity crimes

Heartwave biometric authentication using machine learning algorithms

PhD ThesisThe advancement of IoT, cloud services and technologies have prompted heighten IT access security. Many products and solutions have implemented biometric solution to address the security concern. Heartwave as biometric mode offers the potential due to the inability to falsify the signal and ease of signal acquisition from fingers. However the highly variated heartrate signal, due to heartrate has imposed much headwinds in the development of heartwave based biometric authentications. The thesis first review the state-of-the-arts in the domains of heartwave segmentation and feature extraction, and identifying discriminating features and classifications. In particular this thesis proposed a methodology of Discrete Wavelet Transformation integrated with heartrate dependent parameters to extract discriminating features reliably and accurately. In addition, statistical methodology using Gaussian Mixture Model-Hidden Markov Model integrated with user specific threshold and heartrate have been proposed and developed to provide classification of individual under varying heartrates. This investigation has led to the understanding that individual discriminating feature is a variable against heartrate. Similarly, the neural network based methodology leverages on ensemble-Deep Belief Network (DBN) with stacked DBN coded using Multiview Spectral Embedding has been explored and achieved good performance in classification. Importantly, the amount of data required for training is significantly reduce

Polynomial distance measurement for ECG based biometric authentication

Existing electrocardiography (ECG) based biometric systems are constantly being challenged by higher misclassification error, longer acquisition time, larger template size, slower processing time and pertinence of abnormal beats within the biometric template. These challenges are the prime hindrance for ECG based biometric being commercialized as a pervasive authentication mechanism. At least,ECGbased biometric can provide a secured mechanism for cardiac patients being monitored over telephony network. In this paper, we present a polynomial distance measurement (PDM) method for ECG based biometric authentication for the very first time, according to the literature and to the best of our knowledge. The proposed PDM method is up to 12 times faster than existing algorithms, requires up to 6.5 times less template storage, needs only 2.49 (average) acquisition time with the highest accuracy rate (up to 100 per cent) when experimented on a population size of 15. Moreover, this proposed ECG based biometric system was deployed on a mobile phone based telemonitoring scenario with multilayer authentication mechanism upholding its applicability

Polynomial distance measurement for ECG based biometric authentication

Existing electrocardiography (ECG) based biometric systems are constantly being challenged by higher misclassification error, longer acquisition time, larger template size, slower processing time and pertinence of abnormal beats within the biometric template. These challenges are the prime hindrance for ECG based biometric being commercialized as a pervasive authentication mechanism. At least,ECGbased biometric can provide a secured mechanism for cardiac patients being monitored over telephony network. In this paper, we present a polynomial distance measurement (PDM) method for ECG based biometric authentication for the very first time, according to the literature and to the best of our knowledge. The proposed PDM method is up to 12 times faster than existing algorithms, requires up to 6.5 times less template storage, needs only 2.49 (average) acquisition time with the highest accuracy rate (up to 100 per cent) when experimented on a population size of 15. Moreover, this proposed ECG based biometric system was deployed on a mobile phone based telemonitoring scenario with multilayer authentication mechanism upholding its applicability

Identifying Humans by the Shape of Their Heartbeats and Materials by Their X-Ray Scattering Profiles

Security needs at access control points presents itself in the form of human identification and/or material identification. The field of Biometrics deals with the problem of identifying individuals based on the signal measured from them. One approach to material identification involves matching their x-ray scattering profiles with a database of known materials. Classical biometric traits such as fingerprints, facial images, speech, iris and retinal scans are plagued by potential circumvention they could be copied and later used by an impostor. To address this problem, other bodily traits such as the electrical signal acquired from the brain (electroencephalogram) or the heart (electrocardiogram) and the mechanical signals acquired from the heart (heart sound, laser Doppler vibrometry measures of the carotid pulse) have been investigated. These signals depend on the physiology of the body, and require the individual to be alive and present during acquisition, potentially overcoming circumvention. We investigate the use of the electrocardiogram (ECG) and carotid laser Doppler vibrometry (LDV) signal, both individually and in unison, for biometric identity recognition. A parametric modeling approach to system design is employed, where the system parameters are estimated from training data. The estimated model is then validated using testing data. A typical identity recognition system can operate in either the authentication (verification) or identification mode. The performance of the biometric identity recognition systems is evaluated using receiver operating characteristic (ROC) or detection error tradeoff (DET) curves, in the authentication mode, and cumulative match characteristic (CMC) curves, in the identification mode. The performance of the ECG- and LDV-based identity recognition systems is comparable, but is worse than those of classical biometric systems. Authentication performance below 1% equal error rate (EER) can be attained when the training and testing data are obtained from a single measurement session. When the training and testing data are obtained from different measurement sessions, allowing for a potential short-term or long-term change in the physiology, the authentication EER performance degrades to about 6 to 7%. Leveraging both the electrical (ECG) and mechanical (LDV) aspects of the heart, we obtain a performance gain of over 50%, relative to each individual ECG-based or LDV-based identity recognition system, bringing us closer to the performance of classical biometrics, with the added advantage of anti-circumvention. We consider the problem of designing combined x-ray attenuation and scatter systems and the algorithms to reconstruct images from the systems. As is the case within a computational imaging framework, we tackle the problem by taking a joint system and algorithm design approach. Accurate modeling of the attenuation of incident and scattered photons within a scatter imaging setup will ultimately lead to more accurate estimates of the scatter densities of an illuminated object. Such scattering densities can then be used in material classification. In x-ray scatter imaging, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume. A mask placed between the object and the detector array provides information about scatter angles. An efficient computational implementation of the forward and backward model facilitates iterative algorithms based upon a Poisson log-likelihood. The design of the scatter imaging system influences the algorithmic choices we make. In turn, the need for efficient algorithms guides the system design. We begin by analyzing an x-ray scatter system fitted with a fanbeam source distribution and flat-panel energy-integrating detectors. Efficient algorithms for reconstructing object scatter densities from scatter measurements made on this system are developed. Building on the fanbeam source, energy-integrating at-panel detection model, we develop a pencil beam model and an energy-sensitive detection model. The scatter forward models and reconstruction algorithms are validated on simulated, Monte Carlo, and real data. We describe a prototype x-ray attenuation scanner, co-registered with the scatter system, which was built to provide complementary attenuation information to the scatter reconstruction and present results of applying alternating minimization reconstruction algorithms on measurements from the scanner

Efficient and secured wireless monitoring systems for detection of cardiovascular diseases

Cardiovascular Disease (CVD) is the number one killer for modern era. Majority of the deaths associated with CVD can entirely be prevented if the CVD struck person is treated with urgency. This thesis is our effort in minimizing the delay associated with existing tele-cardiology application. We harnessed the computational power of modern day mobile phones to detect abnormality in Electrocardiogram (ECG). If abnormality is detected, our innovative ECG compression algorithm running on the patient's mobile phone compresses and encrypts the ECG signal and then performs efficient transmission towards the doctors or hospital services. According to the literature, we have achieved the highest possible compression ratio of 20.06 (95% compression) on ECG signal, without any loss of information. Our 3 layer permutation cipher based ECG encoding mechanism can raise the security strength substantially higher than conventional AES or DES algorithms. If in near future, a grid of supercomputers can compare a trillion trillion trillion (1036) combinations of one ECG segment (comprising 500 ECG samples) per second for ECG morphology matching, it will take approximately 9.333 X 10970 years to enumerate all the combinations. After receiving the compressed ECG packets the doctor's mobile phone or the hospital server authenticates the patient using our proposed set of ECG biometric based authentication mechanisms. Once authenticated, the patients are diagnosed with our faster ECG diagnosis algorithms. In a nutshell, this thesis contains a set of algorithms that can save a CVD affected patient's life by harnessing the power of mobile computation and wireless communication