Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas

The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.El desarrollo de sistemas de  monitoreo  ambulatorio  y  sus  técnicas  de  procesamiento  de  la  señal  electrocardiográfica (ECG) se han convertido en un importante campo de investigación, debido a su relevancia en la detección temprana de enfermedades cardiovasculares, tales como arritmias. La tendencia actual de esta tecnología está orientada al uso de equipos portátiles y dispositivos móviles como los Smartphones, que han sido ampliamente aceptados debido a sus características técnicas y a su integración, cada vez más común, en la vida diaria. Una característica fundamental de estos sistemas es su capacidad de reducir los tipos más comunes de ruido mediante técnicas de procesamiento de señales digitales. Entre las técnicas más utilizadas se encuentran los filtros adaptativos y la Transformada Discreta Wavelet (DWT, por sus siglas en inglés), los cuales han sido implementados exitosamente en diversos estudios. Así mismo, se reportan sistemas que integran etapas de clasificación basadas en inteligencia artificial, con lo cual se aumenta el rendimiento en el proceso de detección de arritmias. En este sentido, estas técnicas no solo son evaluadas por su funcionalidad, sino por su costo computacional, debido a que deben ser utilizadas en aplicaciones en tiempo real, e implementadas en sistemas embebidos. Este documento presenta una revisión del estado del arte de cada una de las etapas en la construcción de un sistema de monitoreo ambulatorio estándar, para la contextualización del lector en este tipo de tecnologías

Driver drowsiness detection using different classification algorithms

Capability of electrocardiogram (ECG) signal in contributing to the daily application keeps developing days by days. As technology advances, ECG marks the possibility as a potential mechanism towards the drowsiness detection system. Driver drowsiness is a state between sleeping and being awake due to body fatigue while driving. This condition has become a common issue that leads to road accidents and death. It is proven in previous studies that biological signals are closely related to a person's reaction. Electrocardiogram (ECG) is an electrical indicator of the heart, provides such criteria as it reflects the heart activity that can detect changes in human response which relates to our emotions and reactions. Thus, this study proposed a non-intrusive detector to detect driver drowsiness by using the ECG. This study obtained ECG data from the ULg multimodality drowsiness database to simulate the different stages of sleep, which are PVT1 as early sleep while PVT2 as deep sleep. The signals are later processed in MATLAB using Savitzky-Golay filter to remove artifacts in the signal. Then, QRS complexes are extracted from the acquired ECG signal. The process was followed by classifying the ECG signal using Machine Learning (ML) tools. The classification techniques that include Multilayer Perceptron (MLP), k-Nearest Neighbour (IBk) and Bayes Network (BN) algorithms proved to support the argument made in both PVT1 and PVT2 to measure the accuracy of the data acquired. As a result, PVT1 and PVT2 are correctly classified as the result shown with higher percentage accuracy on each PVTs. Hence, this paper present and prove the reliability of ECG signal for drowsiness detection in classifying high accuracy ECG data using different classification algorithms

A Novel Low Complexity On body CVD Classifier ASIC Design Methodology

Due to increasing rate of cardiac disorders in developed and developing countries Continuous on body monitoring of ECG signal using the concept of IoT and Body Sensor Network has become the necessity. In this work we are proposing a novel low complex, low power algorithm and architecturefor E.C.G. classification which can be incorporated in present era of IOT and Body Sensor Network. Rather than going for Artificial Intelligence based pattern matching and complex DSP algorithm we have used the simplicity of Hurst exponent and Haar wavelet for filtering out anomalous E.C.G. signals and normal ones

FPGA based reconfigurable body area network using Nios II and uClinux

This research is focused on identifying an appropriate design for a reconfigurable Body Area Network (BAN). In order to investigate the benefits and drawbacks of the proposed design, a BAN system prototype was built. This system consists of two distinct node types: a slave node and a master node. These nodes communicate using ZigBee radio transceivers. The microcontroller-based slave node acquires sensor data and transmits digitized samples to the master node. The master node is FPGA-based and runs uClinux on a soft-core microcontroller. The purpose of the master node is to receive, process and store digitized sensor data. In order to verify the operation of the BAN system prototype and demonstrate reconfigurability, a specific application was required. Pattern recognition in electrocardiogram (ECG) data was the application used in this work and the MIT-BIH Arrhythmia Database was used as the known data source for verification. A custom test platform was designed and built for the purpose of injecting data from the MIT-BIH Arrhythmia Database into the BAN system. The BAN system designed and built in this work demonstrates the ability to record raw ECG data, detect R-peaks, calculate and record R-R intervals, detect premature ventricular and atrial contractions. As this thesis will identify, many aspects of this BAN system were designed to be highly reconfigurable allowing it to be used for a wide range of BAN applications, in addition to pattern recognition of ECG data

Selected Computing Research Papers Volume 4 June 2015

A Critical Study of Current Natural Language Processing Methods for the Semantic Web (Lee Bodak) .................................................................................................................. 1 A Critical Evaluation on Current Wireless Wearable Sensors in Monitoring of Patients (Mmoloki Gogontle Gontse) ................................................................................... 7 Evaluation on Research targeted towards Worm Viruses and Detection Methods (Adam Keith) ...................................................................................................................... 13 Evaluation of Security Techniques in Cloud Storage Systems (Aone Maenge) ................ 21 An Evaluation of Current Power Management Techniques Used In Mobile Devices (Gabriel Tshepho Masabata) ............................................................................................... 27 An Evaluation Of Current Wide Area Network Cyber Attack Detection Methods Aimed At Improving Computer Security (Hayley Roberts) ............................................... 35 Current EMG Pattern Recognition Research Aimed At Improving Upper Limb Prosthesis Control (Molly Sturman) ................................................................................... 41 Positive and Negative: Effects Video Game Use Can Have on Personality Development (Shaun Watson) ............................................................................................ 4

A real-time cardiac arrhythmia classification system with wearable electrocardiogram

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for the prevention on the heart attack and other high risk diseases. Most of the existing devices provide ECG recording in a hospital setting or off-line ECG diagnosis. The design of a real-time wearable ECG monitoring device with cardiac arrhythmia classification system is discussed in this paper. In this system, the wearable sensor node monitors the patient's ECG and motion signal in an unobstructive way that the patient's daily life will not be affected. ECG analog front-end and on-node processing are designed to remove most of the noise and bias, which guarantees an clean and reliable ECG waveform. The ECG waveform is digitalized by an analog-to-digital convertor and transmitted to a smart phone via bluetooth. On the smartphone, the ECG waveform is visualized and a novel layered hidden Markov model is implemented to classify multiple cardiac arrhythmias in real time. This paper evaluates the performance of the hardware design and the classification algorithm

A real-time cardiac arrhythmia classification system with wearable electrocardiogram

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. A design of a real-time wearable ECG monitoring system with cardiac arrhythmia classification is proposed in this paper. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Last, human activities by an accelerometer can be identified to reduce the chance of false alarm in classification due to the motion artifacts. © 2011 IEEE

A real-time cardiac arrhythmia classification system with wearable electrocardiogram

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for the prevention on the heart attack and other high risk diseases. Most of the existing devices provide ECG recording in a hospital setting or off-line ECG diagnosis. The design of a real-time wearable ECG monitoring device with cardiac arrhythmia classification system is discussed in this paper. In this system, the wearable sensor node monitors the patient\u27s ECG and motion signal in an unobstructive way that the patient\u27s daily life will not be affected. ECG analog front-end and on-node processing are designed to remove most of the noise and bias, which guarantees an clean and reliable ECG waveform. The ECG waveform is digitalized by an analog-to-digital convertor and transmitted to a smart phone via bluetooth. On the smartphone, the ECG waveform is visualized and a novel layered hidden Markov model is implemented to classify multiple cardiac arrhythmias in real time. This paper evaluates the performance of the hardware design and the classification algorithm. © 2011 IEEE