Ali Ghaffari

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A robust multi-lead ECG wave detection-delineation algorithm is developed in this study on the basis of discrete wavelet transform (DWT). By applying a new simple approach to a selected scale obtained from DWT, this method is capable of detecting QRS complex, P-wave and T-wave as well as determining parameters such as start time, end time, and wave sign(More)
Congestion occurs in wireless sensor networks (WSNs) when nodes are densely distributed, and/or the application produces high flow rate near the sink due to the convergent nature of upstream traffic. Congestion can lead to packet losses, delay, and energy waste due to a large number of packet drops and retransmissions. Therefore it is necessary to carry out(More)
In this study, we have introduced an open-source program that can be used for the detection and analysis of different waves in the ECG signal. The effect of noise is first reduced by applying an adaptive least-squares method to the signal using a sliding window. The maximums and minimums of the signal are determined, and the R-waves are then detected using(More)
The aim of this study is to detect acute hypotensive episodes (AHE) and mean arterial pressure dropping regimes (MAPDRs) using electrocardiographic (ECG) signals and arterial blood pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative modified Hilbert transform-based(More)
Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. Reliable data gathering and transmission are important in wireless sensor networks. While node redundancy in WSNs, increases the fault tolerance, no guarantees on reliability levels can be assured. Furthermore, the frequent node(More)
In this study, a mathematical model is developed based on algebraic equations which is capable of generating artificially normal events of electrocardiogram (ECG) signals such as P-wave, QRS complex, and T-wave. This model can also be implemented for the simulation of abnormal phenomena of electrocardiographic signals such as ST-segment episodes (i.e.(More)
The most straightforward method for heart beat estimation is R-peak detection based on an electrocardiogram (ECG) signal. Current R-peak detection methods do not work properly when the ECG signal is contaminated or missing, which leads to the incorrect estimation of the heart rate. This raises the need for reliable algorithms which can locate heart beats in(More)
In this study, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution, is introduced. Proposed method consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique. Toward this objective, first, the events of the electrocardiogram (ECG) signal are detected and delineated using a(More)