6 research outputs found

    Wavelet Based Feature Extraction and Dimension Reduction for the Classification of Human Cardiac Electrogram Depolarization Waveforms

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    An essential task for a pacemaker or implantable defibrillator is the accurate identification of rhythm categories so that the correct electrotherapy can be administered. Because some rhythms cause a rapid dangerous drop in cardiac output, it is necessary to categorize depolarization waveforms on a beat-to-beat basis to accomplish rhythm classification as rapidly as possible. In this thesis, a depolarization waveform classifier based on the Lifting Line Wavelet Transform is described. It overcomes problems in existing rate-based event classifiers; namely, (1) they are insensitive to the conduction path of the heart rhythm and (2) they are not robust to pseudo-events. The performance of the Lifting Line Wavelet Transform based classifier is illustrated with representative examples. Although rate based methods of event categorization have served well in implanted devices, these methods suffer in sensitivity and specificity when atrial, and ventricular rates are similar. Human experts differentiate rhythms by morphological features of strip chart electrocardiograms. The wavelet transform is a simple approximation of this human expert analysis function because it correlates distinct morphological features at multiple scales. The accuracy of implanted rhythm determination can then be improved by using human-appreciable time domain features enhanced by time scale decomposition of depolarization waveforms. The purpose of the present work was to determine the feasibility of implementing such a system on a limited-resolution platform. 78 patient recordings were split into equal segments of reference, confirmation, and evaluation sets. Each recording had a sampling rate of 512Hz, and a significant change in rhythm in the recording. The wavelet feature generator implemented in Matlab performs anti-alias pre-filtering, quantization, and threshold-based event detection, to produce indications of events to submit to wavelet transformation. The receiver operating characteristic curve was used to rank the discriminating power of the feature accomplishing dimension reduction. Accuracy was used to confirm the feature choice. Evaluation accuracy was greater than or equal to 95% over the IEGM recordings

    P Wave Detection in Pathological ECG Signals

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    Důležitou součástí hodnocení elektrokardiogramu (EKG) a následné detekce srdečních patologií, zejména v dlouhodobém monitorování, je detekce vln P. Výsledky detekce vln P umožňují získat ze záznamu EKG více informací o srdeční činnosti. Podle správně detekovaných pozic vln P je možné detekovat a odlišit patologie, které současné programy používané v medicínské praxi identifikovat neumožňují (např. atrioventrikulární blok 1., 2. a 3. stupně, cestující pacemaker, Wolffův-Parkinsonův-Whiteův syndrom). Tato dizertační práce představuje novou metodu detekce vln P v záznamech EKG během fyziologické a zejména patologické srdeční činnosti. Metoda je založena na fázorové transformaci, inovativních pravidlech detekce a identifikaci možných patologií zpřesňující detekci vln P. Dalším důležitým výsledkem práce je vytvoření dvou veřejně dostupných databází záznamů EKG s obsahem patologií a anotovanými vlnami P. Dizertační práce je rozdělena na teoretickou část a soubor publikací představující příspěvek autora v oblasti detekce vlny P.Accurate software for the P wave detection, mainly in long-term monitoring, is an important part of electrocardiogram (ECG) evaluation and subsequent cardiac pathological events detection. The results of P wave detection allow us to obtain more information from the ECG records. According to the correct P wave detection, it is possible to detect and distinguish cardiac pathologies which are nowadays automatically undetectable by commonly used software in medical practice (events e.g. atrioventricular block 1st, 2nd and 3rd degree, WPW syndrome, wandering pacemaker, etc.). This thesis introduces a new method for P wave detection in ECG signals during both physiological and pathological heart function. This novel method is based on a phasor transform, innovative rules, and identification of possible pathologies that improve P wave detection. An equally important part of the work is the creation of two publicly available databases of physiological and pathological ECG records with annotated P waves. The dissertation is divided into theoretical analysis and a set of publications representing the contribution of the author in the area of P wave detection.

    A machine learning framework for automatic human activity classification from wearable sensors

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    Wearable sensors are becoming increasingly common and they permit the capture of physiological data during exercise, recuperation and everyday activities. This work investigated and advanced the current state-of-the-art in machine learning technology for the automatic classification of captured physiological data from wearable sensors. The overall goal of the work presented here is to research and investigate every aspect of the technology and methods involved in this field and to create a framework of technology that can be utilised on low-cost platforms across a wide range of activities. Both rudimentary and advanced techniques were compared, including those that allowed for both real-time processing on an android platform and highly accurate postprocessing on a desktop computer. State-of-the-art feature extraction methods such as Fourier and Wavelet analysis were also researched to ascertain how well they could extract discriminative physiological information. Various classifiers were investigated in terms of their ability to work with different feature extraction methods. Consequently, complex classification fusion models were created to increase the overall accuracy of the activity recognition process. Genetic algorithms were also employed to optimise classifier parameter selection in the multidimensional search space. Large annotated sporting activity datasets were created for a range of sports that allowed different classification models to be compared. This allowed for a machine learning framework to be constructed that could potentially create accurate models when applied to any unknown dataset. This framework was also successfully applied to medical and everyday-activity datasets confirming that the approach could be deployed in different application settings

    Telemedicine

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    Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

    Recent Application in Biometrics

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    In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers
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