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El presente proyecto de tesis consiste en el diseño de un equipo biomédico, el electrocardiógrafo, que le permite al especialista de la salud ver la evolución de la señal eléctrica emitida por el corazón. Esta señal es adquirida mediante un sistema de acondicionamiento de señales y procesada por un controlador digital de señales, así como también los periféricos que muestra la información procesada. Paralelamente, se puede enviar esta información a una computadora, donde un programa se encargara de visualizar esta señal y almacenarla en una base de datos.

This thesis studies the applicability of two cardiac traits, the electrocardiogram (ECG) and the phonocardiogram (PCG), as biometrics. There is strong evidence that cardiac electrical activity (ECG) embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. On the other hand, having the same origin with the ECG signal, it is believed that the PCG signal conveys distinctive information of an individual which can be deployed in biometric applications. Such recognition systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios. Moreover, the expression of the cardiac signals is subject to alternation with heart rate and noise components. Thus, the central consideration of this thesis is the design and evaluation of robust recognition approaches that can compensate for these effects. A recognition system based on each, the ECG and the PCG, is developed and evaluated. Furthermore, a fusion of the two signals in a multimodal biometric system is investigated.

Many people have abnormal heartbeats from time to time. A Holter monitor is a device used to record the electrical impulses of the heart when people do ordinary activities. Holter monitoring systems that can record heart rate and rhythm when you feel chest pain or symptoms of an irregular heartbeat (called an arrhythmia) and automatically perform electrocardiogram (ECG) signal analysis are desirable.<p>The use of the Hilbert transform (HT) in the area of electrocardiogram analysis is investigated. A property of the Hilbert transform, i.e., to form the analytic signal, was used in this thesis. Subsequently pattern recognition can be used to analyse the ECG data and lossless compression techniques can be used to reduce the ECG data for storage.<p>The thesis discusses one part of the Holter Monitoring System, Input processing.<p>Four different approaches, including the Time-Domain approach, the Frequency-Domain approach, the Boche approach and the Remez filter approach for calculating the Hilbert transform of an ECG wave are discussed in this thesis. By comparing them from the running time and the ease of software and hardware implementations, an efficient approach (the Remez approach) for use in calculating the Hilbert transform to build a Holter Monitoring System is proposed. <p>Using the Parks-McClellan algorithm, the Remez approach was present, and a digital filter was developed to filter the data sequence. <p>Accurate determination of the QRS complex, in particular, accurate detection of the wave peak, is important in ECG analysis and is another task in this thesis. A program was developed to detect the wave peak in an ECG wave.<p>The whole algorithm is implemented using Alteras Nios SOPC (system on a program chip) Builder system development tool. The performance of the algorithm was tested using the standard ECG waveform records from the MIT-BIH Arrhythmia database. The results will be used in pattern recognition to judge whether the ECG wave is normal or abnormal.

This thesis studies the applicability of two cardiac traits, the electrocardiogram (ECG) and the phonocardiogram (PCG), as biometrics. There is strong evidence that cardiac electrical activity (ECG) embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. On the other hand, having the same origin with the ECG signal, it is believed that the PCG signal conveys distinctive information of an individual which can be deployed in biometric applications. Such recognition systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios. Moreover, the expression of the cardiac signals is subject to alternation with heart rate and noise components. Thus, the central consideration of this thesis is the design and evaluation of robust recognition approaches that can compensate for these effects. A recognition system based on each, the ECG and the PCG, is developed and evaluated. Furthermore, a fusion of the two signals in a multimodal biometric system is investigated.

Many people have abnormal heartbeats from time to time. A Holter monitor is a device used to record the electrical impulses of the heart when people do ordinary activities. Holter monitoring systems that can record heart rate and rhythm when you feel chest pain or symptoms of an irregular heartbeat (called an arrhythmia) and automatically perform electrocardiogram (ECG) signal analysis are desirable.<p>The use of the Hilbert transform (HT) in the area of electrocardiogram analysis is investigated. A property of the Hilbert transform, i.e., to form the analytic signal, was used in this thesis. Subsequently pattern recognition can be used to analyse the ECG data and lossless compression techniques can be used to reduce the ECG data for storage.<p>The thesis discusses one part of the Holter Monitoring System, Input processing.<p>Four different approaches, including the Time-Domain approach, the Frequency-Domain approach, the Boche approach and the Remez filter approach for calculating the Hilbert transform of an ECG wave are discussed in this thesis. By comparing them from the running time and the ease of software and hardware implementations, an efficient approach (the Remez approach) for use in calculating the Hilbert transform to build a Holter Monitoring System is proposed. <p>Using the Parks-McClellan algorithm, the Remez approach was present, and a digital filter was developed to filter the data sequence. <p>Accurate determination of the QRS complex, in particular, accurate detection of the wave peak, is important in ECG analysis and is another task in this thesis. A program was developed to detect the wave peak in an ECG wave.<p>The whole algorithm is implemented using Alteras Nios SOPC (system on a program chip) Builder system development tool. The performance of the algorithm was tested using the standard ECG waveform records from the MIT-BIH Arrhythmia database. The results will be used in pattern recognition to judge whether the ECG wave is normal or abnormal.

SAS has become an increasingly important public-health problem in recent years. It can adversely affect neurocognitive, cardiovascular, respiratory diseases and can also cause behavior disorder. Moreover, up to 90% of these cases are obstructive sleep apnea (OSA). Therefore, the study of how to diagnose, detect and treat OSA is becoming a significant issue, both academically and medically. Polysomnography can monitor the OSA with relatively fewer invasive techniques. However, polysomnography-based sleep studies are expensive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel. Therefore, to improve such inconveniences, one needs to develop a simplified method to diagnose the OSA, so that the OSA can be detected with less time and reduced financial costs. Since currently there seems to be no OSA detection technique available in Taiwan, the goal of this work is to develop a reliable OSA diagnostic algorithm. In particular, via signal processing, feature extraction and artificial intelligence, this thesis describes an on-line ECG-based OSA diagnostic system. It is hoped that with such a system the OSA can be detected efficiently and accurately.

Sleep medicine becomes more increasing attention in recent years, especially for the SAS. The primary health implications of SAS are its impact on the cardiovascular system . Generally speaking, it is necessary to stay in the sleep examination room overnight or several nights and need plenty of clinical data to diagnose, which is high-priced and time consuming. This study is based on ECG and EEG signals, moreover the features are obtained from them to observe the interaction, therefore we can estimate correlation coefficient within ECG, EEG., and RDI. Furthermore we also show that higher correlation is found by adjusting the most appropriate bands than ordinary ones[AASM,1996]. Moreover, experimental data are broken into male and female groups and the female patients exhibit lower correlation than male ones.

This paper proposes a device capable of acquiring an electrocardiogram (ECG, EKG) signal to be hosted by a typical Android smartphone. Bluetooth is used as the data connection. Once acquired, the signal is graphed on the display of the smartphone. A basis of physiology behind the ECG is presented. The data acquisition system and the performance of the ECG amplification and supporting circuits are analyzed. / text