Determination and Detection and of Heart Rate at the Heart's Electrical Signal for Telemedicine Applications

This research offers a novel approach for automatic heart rate detection using electrocardiogram(ECG) signal processing, which leads to be used in telemedicine applications. The electrocardiogram is a diagram of the electrical potential changes of the heart muscle that can be recorded at various points in the body. The recorded signals of the chest are widely used in telemedicine applications because of their ease of recording. The P, QRS, and T waves and also ST fragment are the main parts of this signal. The QRS wave represents the heart beat and has a special shape, which is different in each breast lead. This wave is made of three main points;Q, R, and S. The range of R and S points vary from one lead to another. This can facilitate the automatic determination of these points. Determining each of these points is as determining each of the heart beat bits. A number of studies have been undertaken in this context, most of which are based on digital signal processing and signal shape. One of the important methods in this field, is the use of wavelet coefficients. The previous research have often used the reported usual values for the domain, determined by QRS waveguide. Some new methods, that have used the artificial neural network, have their own complexity. The previous research have also used a variety of data, most of which are from the Physionet.org site databases(PTBDB). The purpose of this research, is to offer a new method for the simpler signal processing in accordance to the shape of the employed lead signal. The proposed algorithm has some general preprocessing parts including import, reset, noise reduction, points and threshold limit setting of the signal whose main processing includes intersection line formation, location accurate of QRS and heart rate determination. The algorithm was implemented for 93 signals from the PTB Diagnostic ECG Database of the Physionet.org site, which eventually yielded Positive Predictively (P+)and sensitivity (Se)values of100% and99.95% respectively. These values show that the offered algorithm has been more accurate, than those of the existing ones. Finally, the proposed algorithm is implemented on the Android.