Utilizing ECG Waveform Features as New Biometric Authentication Method

In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects‟ raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%

Desarrollo de algoritmos para análisis del ECG como ayuda al diagnóstico de trastornos del sueño

A lo largo del presente documento, se describe el desarrollo de un algoritmo de análisis del ECG con el objetivo de utilizarlo como ayuda al diagnóstico de trastornos del sueño, concretamente para la identificación de apneas centrales del sueño. Se han elaborado varios algoritmos de detección de apneas centrales a partir de información extraída del ECG con unos parámetros estadísticos de precisión en torno al 90%. Además, se ha desarrollado un proceso de fusión de datos, basado en la teoría de la evidencia de Dempster-Shafer, que ha reportado unos resultados del 100% de detección para las muestras bajo estudio, con un 84% de ellas con un nivel de certeza por encima del 90%.Throughout this document, the development of an ECG analysis algorithm is described with the aim of using it as an aid to the diagnosis of sleep disorders, specifically for the identification of central sleep apneas. Several central apnea detection algorithms have been developed from information extracted from the ECG with statistical parameters of precision around 90%. In addition, a data fusion process has been developed, based on the Dempster-Shafer theory of evidence, which has reported 100% detection results for the segments under study, with 84% of them at level of certainty above 90%.Grado en Ingeniería en Tecnologías de Telecomunicació