No abstract

The QRS complex of ECG signal represents the depolarization and repolarization activities in the cells of ventricle. Accurate informations of   and   are needed for automatic analysis of ECG waves. In this study, using the amount of change in the QRS complex voltage values and the distance from the   , we determined the junction point from Q-wave to R-wave and the junction point from R-wave to S-wave. In the next step, using the integral calculation based on the connection point, we detected   and   . We use the PhysioNet QT database to evaluate the performances of the algorithm, and calculate the mean and standard deviation of the differences between onsets or offsets manually marked by cardiologists and those detected by the proposed algorithm. The experiment results show that standard deviations are under the tolerances accepted by expert physicians, and outperform the results obtained by the other algorithms.

The accurate detection of R-wave is important for other feature extraction in ECG, and R-wave has a lot of medical information about heart. Numerous R-wave detection algorithms have been studied on the ECG signal shape analysis, but it was difficult to find accurate R-wave when the shape of R-wave is similar to the shape of P-wave. This paper presents an R-wave detection method based on the refractory period that is the period of depolarization and repolarization of the cell membrane after excitation. And we also use the shape of kurtosis in the refractory period. The proposed method is validated using the ECG records of the MIT-BIH arrhythmia database. Experimental results show that the proposed method significantly outperforms other method in case of 105 and 108 record that have R-wave similar to P-wave, as well as other records.

No abstract

For designing broadcast protocols in mobile ad hoc networks (MANETs), one of the important goals is to reduce the rebroadcast packets redundancy while reaching all the nodes in network. In this paper, we propose a probabilistic broadcasting mechanism based on selfishness and additional coverage in MANETs. Our approach dynamically adjusts the rebroadcast probability according to the extra covered area and number of neighbor nodes. By these two factors, mobile hosts can be classified into three groups: normal, low selfishness, and high selfishness groups. The nodes in the normal group forward packets for other nodes with high probability, whereas the nodes in the low selfishness group rebroadcast packets with low probability and the nodes in the high selfishness group do not rebroadcast packets. We compared our approach with simple flooding and the fixed probabilistic approach. The simulation results show that the proposed schemes can significantly reduce the number of retransmissions by up to 40% compared simple flooding and fixed probabilistic scheme without significant reduction in the network reachability and end-to-end packet delay.