An investigation of bio potentials for the pre-diagnosis of heart dysfunction using a novel portable high resolution electronic analyzer and software

An electrocardiogram (ECG) is a bioelectrical signal which records the heart‘s electrical activity versus time. It is an important diagnostic tool for assessing heart functions. The interpretation of ECG signal is an application of pattern recognition. The techniques used in this pattern recognition comprise: signal pre-processing, QRS detection, creation of variables and signal classification. In this research, signal processing and programs implementation are based in Matlab environment. The processed simulated signal source came from the SIMULAIDS® interactive ECG simulator™ device and the actual heart signals came from actual patients that suffer from various heart disorders, as well as healthy persons that hadn‘t recorded any form of heart condition in the past. For the creation of the database in this research, 5 types of ECG waveform were selected from the ECG simulator device. These are normal sinus rhythm (NSR), ventricular tachycardia (VT poly), ventricular fibrillation (VF), Atrial fibrillation (A FIB) and supra ventricular tachycardia (SVT). An essential part of this research was the development of a portable high resolution ECG device, capable of connecting with, either an ECG simulator device, or recording real human data. This device is able to produce higher resolution than normal ECG devices and high values of Signal to Noise Ratio (SNR). Matlab was used to develop a program that could further examine, analyze and study the ECG samples. Since the heart waveform can be simulated by cubic spline interpolation, this feature was used by the implemented Matlab program. The ECG samples were normalized and processed to produce 4 specific coefficients. These 4 coefficients of cubic spline were used in the applied methodology in order to evaluate and separate the various heart disorders with mathematical terms and equations. The database created was compared with the real human samples that were taken and passed through the same data process. Through this step, the entire data process and implementation was not only confirmed, but also proved that the capability to diagnose heart disorders was possible. Based on the results of the applied methodology, the categorization of heart disorders without actual clinical examination is possible. Further analysis of each group of results, can lead to heart disorder prediction. Also given are further suggestions to plan experiments for future work

Development of a computerized ECG analysis model using the cubic spline interpolation method

An electrocardiogram (ECG) is a bioelectrical signal which records the heart's electrical activity versus time. It is an important diagnostic tool for assessing heart functions. The interpretation of ECG signal is an application of pattern recognition. The techniques used in this pattern recognition comprise: signal preprocessing, QRS detection, creation of variables and signal classification. In this method, signal processing and programs implementation are based in Matlab environment. Matlab was used to develop a program that could further examine, analyze and study the ECG samples. Since the heart waveform can be simulated by cubic spline interpolation, this feature was used by the implemented Matlab program. The ECG samples were normalized and processed to produce 4 specific coefficients. These 4 coefficients of cubic spline were used in the applied methodology in order to evaluate and separate the various heart disorders with mathematical terms and equations. Based on the results of the applied methodology, the categorization of heart disorders without actual clinical examination is possible

Development of a novel system to analyse and detect small changes in ECG signals that indicate cardiac disorders

The analysis of the electrocardiogram as a diagnostic tool is a relatively old field and it is therefore often assumed that the ECG is a simple signal that has been fully explored. However, there remain difficult problems in this field that are being incrementally solved with advances in techniques from the fields of filtering, pattern recognition, and classification, together with the leaps in computational power and memory capacity that have occurred over the last couple of decades. While the ECG is routinely used to diagnose arrhythmias, it reflects an integrated signal and cannot provide information on the micro-spatial scales of cells and ionic channels. For this reason, the field of computational cardiac modeling and simulation has grown over the last decade. The aim of this paper is by using a novel system to develop methods to analyze and detect small changes in ECG waves and complexes that indicate cardiac diseases and disorder

An integrated development hardware design for an advanced wireless Ag/AgCl sensor to acquiring biosignals form ornamental plants

Electrical signals (biosignals) in higher plants could be the information carriers in intracellular and intercellular communication during environmental changes and pathogens attack. Biosignals are currently acquiring by Ag/AgCl electrode attached on plants' shoots and leaves. These plants are usually placed in Faraday's cage (FC) to eliminate the signal-noise. In this paper, we present an integrated hardware design for the development of an advanced wireless Ag/AgCl sensor for plants' biosignals measurements. In order to avoid white Gaussian noise, 50 Hz power line noise as well as the noise of the operating electrical devices, microcontroller embedded systems and IEEE 802.15.4 communication protocol was used for the system performance. The electrical potential was measured on leaves of Chrysanthemum (Chrysanthemum moriflorum) plants using Ag/AgCl electrodes and recorded in data logger: i) placed inside the FC, ii) in a distance of 15 meter away from FC, connected by interface circuit via wire and iii) in a distance of 60 meter away from FC using a wireless embedded system for data transfer. The wireless connection between embedded systems of Ag/AgCl electrodes did not show signals errors. The wire transmission of biosignal showed harmonics distortions in spectral analysis and the amplitude of biosignal showed modification at 50%. The same result was showed under the effect of a low power RF signal (1- 3,3 Mhz), which transmitted near the location of FC