Design of a mobile telecardiology system using GPRS/GSM technology

This paper presents the design and development of a portable electrocardiograph to allow the on-line remote monitoring and real-time cardiac diseases diagnostics of patients from the specialist. This prototype has been satisfactory implemented finding a good balance between optimal signal processing and power consumption using a GPRS/GSM modem and a SMT low voltage microprocessor board

Caracterizacion mediante wavelets de electrocardiogramas para efectos de compresion y clasificacion de cardiopatías

Este artículo presenta un diseño para la caracterización de la señal ECG basado en “wavelets”, donde los coeficientes de caracterización no sólo posibiliten alcanzar factores considerables de compresión, sino que también se constituyan en la base para la clasificación de cardiopatías. Los datos para la evaluación del desempeño del sistema son tomados de la base de datos MIT-BIH. Se analizan los resultados del proceso de compresión a la luz de la medida de confiabilidad PRD, y de la tasa de compresión lograda; la inteligibilidad de la señal obtenida después del proceso de descompresión, se evalúa con el algoritmo de clasificación. Este diseño propone un marco de trabajo para la implementación de sistemas de telecardiología móvile

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

Sparsity-Homotopy Perturbation Inversion Method with Wavelets and Applications to Black-Scholes Model and Todaro Model

Sparsity regularization method plays an important role in reconstructing parameters. Compared with traditional regularization methods, sparsity regularization method has the ability to obtain better performance for reconstructing sparse parameters. However, sparsity regularization method does not have the ability to reconstruct smooth parameters. For overcoming this difficulty, we combine a sparsity regularization method with a wavelet method in order to transform smooth parameters into sparse parameters. We use a sparsity-homotopy perturbation inversion method to improve the accuracy and stability and apply the proposed method to reconstruct parameters for a Black-Scholes option pricing model and a Todaro model. Numerical experiments show that the proposed method is convergent and stable

Bluetooth-Based Sensor Networks for Remotely Monitoring the Physiological Signals of a Patient

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”Integrating intelligent medical microsensors into a wireless communication network makes it possible to remotely collect physiological signals of a patient, release the patient from being tethered to monitoring medical instrumentations, and facilitate the patient's early hospital discharge. This can further improve life quality by providing continuous observation without the need of disrupting the patient's normal life, thus reducing the risk of infection significantly, and decreasing the cost of the hospital and the patient. This paper discusses the implementation issues, and describes the overall system architecture of our developed Bluetooth sensor network for patient monitoring and the corresponding heart activity sensors. It also presents our approach to developing the intelligent physiological sensor nodes involving integration of Bluetooth radio technology, hardware and software organization, and our solutions for onboard signal processing.Peer reviewe

ECG Signal Transmission via Ethernet

V teoretické části diplomové práce jsou popsány metody komprese EKG záznamu určené k úpravě záznamu za účelem přenosu komunikačními kanály. Práce obsahuje úvod do sítě Ethernet a seznámení s podstatou komunikace v této síti. Podrobněji jsou zde rozebrány transportní protokoly TCP a UDP. V praktické části práce byly vytvořeny dvě samostatné aplikace. První aplikace v počítači odesílatele otevírá textový soubor s EKG signálem a načtený EKG signál pak filtruje kaskádou filtrů za účelem odstranění rušení. Výsledný signál je zobrazen. Součástí aplikace je detekce R vlny, výpočet délky RR intervalu a tepové frekvence. Aplikace dále umožňuje EKG signál komprimovat. EKG signál je po síti Ethernet odesílán prostřednictvím protokolu UDP po jednotlivých vzorcích. Aplikace v počítači příjemce přijímá vzorky signálu ze sítě, je-li přijatý signál komprimovaný, tak jej rekonstruuje. Výsledný EKG signál je zobrazen a jsou v něm opět detekovány R vlny, vypočtená délka RR intervalů a vzorkovací frekvence.The semestral thesis describes ECG signal compression methods designed to modify the data for transmission via communication channels. The thesis contains an introduction to Ethernet and explanation of communication in the network. The transport protocols TCP and UDP are discussed in more detail. In the practical part of the thesis was created two separate applications. The first application in the sender's computer opens a text file with the ECG signal. Loaded ECG signal is filtered by cascade of filters to eliminate interference. The resulting signal is displayed. A part of the application is the R wave detection, calculating the length of RR interval and heart rate. The application also allows to compress an ECG signal. ECG signal is sent via Ethernet network via UDP protocol for individual samples. Applications in the recipient's computer receives signal samples from the network. Recieved compressed data is reconstructed. The resulting ECG signal is displayed and there are again detected R waves, the length of RR intervals and sampling frequency are calculated.

Compression of electrocardiograms using wavelets

Orientador: Ivanil Sebastião BonattiDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: A principal contribuição desta dissertação é a proposta de dois métodos de compressão de eletrocardiogramas (ECGs). O primeiro método, chamado Run Length Encoding Adaptativo (RLEA), é baseado nas transformadas wavelet e consiste basicamente em utilizar uma função wavelet, obtida pela resolução de um problema de otimização, que se ajuste ao sinal a ser comprimido. O problema de otimização torna-se irrestrito com a parametrização dos coeficientes do filtro escala, que definem unicamente uma função wavelet. Após a resolução do problema de otimização é aplicado o procedimento de decomposição wavelet no sinal e os coeficientes de representação mais significativos são retidos, sendo que o número de coeficientes retidos é determinado de forma a satisfazer uma medida de distorção pré-especificada. Os coeficientes retidos são então quantizados e compactados, assim como o bitmap que indica as posições dos coeficientes retidos. A quantização é feita de forma adaptativa, utilizando diferentes números de bits de quantização para os diferentes subespaços de decomposição considerados. Tanto os valores dos coeficientes retidos quanto o bitmap são codificados utilizando uma variante do método Run Length Encoding. O segundo método proposto nesta dissertação, chamado Zero Padding Singular Values Decomposition (ZPSVD), consiste em primeiramente detectar os batimentos, equalizá-los pela inserção de zeros (zero padding) e então aplicar a decomposição SVD para obter tanto a base quanto os coeficientes de representação dos batimentos. Alguns componentes da base são retidos e então comprimidos utilizando os mesmos procedimentos aplicados aos coeficientes de decomposição do ECG no método RLEA, enquanto que os coeficientes de projeção dos batimentos nessa base são quantizados utilizando um procedimento de quantização adaptativa. Os dois métodos de compressão propostos são comparados com diversos outros métodos existentes na literatura por meio de experimentos numéricosAbstract: The main contribution of the present thesis is the proposition of two electrocardiogram (ECG) compression methods. The first method, called Run Length Encoding Adaptativo (RLEA), is based on wavelet transforms and consists of using a wavelet function, obtained by the resolution of an optimization problem, which fits to the signal to be compressed. The optimization problem becomes unconstrained with the parametrization of the coefficients of the scaling filter, that define uniquely a wavelet function. After the resolution of the optimization problem, the wavelet decomposition procedure is applied to the signal and the most significant coefficients of representation are retained, being the number of retained coefficients determined in order to satisfty a pre-specified distortion measure. The retained coefficients are quantized and compressed, likewise the bitmap that informs the positions of the retained coefficients. The quantization is performed in an adaptive way, using different numbers of bits for the different decomposition subspaces considered. Both the values of the retained coefficients and the bitmap are encoded using a modi- fied version of the Run Length Encoding technique. The second method proposed in this dissertation, called Zero Padding Singular Values Decomposition (ZPSVD), consists of detecting the beat pulses of the ECG, equalizing the pulses by inserting zeros (zero padding), and finally applying the SVD to obtain both the basis and the coefficients of representation of the beat pulses. Some components of the basis are retained and then compressed using the same procedures applied to the coefficients of decomposition of the ECG in the RLEA method, while the coefficients of projection of the beat pulses in the basis are quantized using an adaptive quantization procedure. Both proposed compression methods are compared to other techniques by means of numerical experimentsMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétric