Khaled Daqrouq

King Abdulaziz University

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i-10 index

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Abdulhameed F. Alkhateeb

King Abdulaziz University

Abdel‐Rahman Al‐Qawasmi

Majmaah University

Ali Morfeq

King Abdulaziz University

Emad Khalaf

King Abdulaziz University

Ibrahim N. Abu-Isbeih

Philadelphia University

Rami Al‐Hmouz

Sultan Qaboos University

Ahmed Balamesh

King Abdulaziz University

Mohammed N. Ajour

King Abdulaziz University

Mohammed N. Ajour

King Abdulaziz University

Mikhled Alfaouri

Philadelphia University

Cooperative Institutions

King Abdulaziz University

Philadelphia University

Technische Universität Berlin

Friedrich-Alexander-Universität Erlangen-Nürnberg

King Abdul Aziz University Hospital

Philadelphia University

Al-Hussein Bin Talal University

Systems Research Institute

Polish Academy of Sciences

Prince Sattam Bin Abdulaziz University

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Quality Evaluation Techniques of Processing the ECG Signal

American Journal of Applied Sciences (2008)

Mikhled Alfaouri Khaled Daqrouq

In this research, methods to detect and evaluate the main parameters of the ECG signal are presented in order to improve its clinical properties. Two techniques are addressed in this research, Piecewise method and the Analytical Approach. These techniques are used to determine such as: percent (rms) difference (PRD), signal-noise-ratio (SNR) and the compression ratio (CR) which are used to evaluate the quality of ECG signals. The evaluation and processing of the methodology quality and the metrological point of view of ECG signal problems are subsequently obtained from detection and filtration. This technique has been successfully implemented to determine the legal accuracy rang of ECG signal for detection, filtration and compression.

10.3844/ajassp.2008.1737.1741

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Citations (12)

Quality Evaluation of Reconstructed Biological Signals

American Journal of Applied Sciences (2009)

Mikhled Alfaouri Khaled Daqrouq Ibrahim N. Abu-Isbeih Emad Khalaf Abdel‐Rahman Al‐Qawasmi

10.3844/ajas.2009.187.193

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Citations (4)

A universal ECG signal classification system using the wavelet transform

Neural Network World (2022)

Khaled Daqrouq Abdulhameed F. Alkhateeb Waleed Ahmad Emad Khalaf Mohamed M. Awad

The electrocardiograph (ECG) is one of the most successful medical diagnostic tools. The ECG can show, roughly speaking, all types of heart disordersthat appear as ECG signal arrhythmias or problems with the rate or rhythm of thehuman heartbeat. In this paper, a universal ECG signal arrhythmia classificationsystem is proposed. The proposed system is based on using the wavelet transformin two of its known forms, namely, the discrete wavelet transform (DWT) andthe wavelet packet transform (WPT), or a combination thereof. The purpose ofthe research reported herein is to find out a universal classification system; in thesense of providing a capability for simultaneous classification of all types of known heart arrhythmias. Three algorithms based on the wavelet transform are tested for different wavelet levels, wavelet functions, training and testing ratios, and elapsed times. We rank these algorithms according to the elapsed times needed for their processing over the whole loop of the eight different arrhythmia classes. This ranking nominates the WPT-based algorithm to be the most superior method among the competing methods. A different ranking according to successful recognition rates assigns priority instead to the method combining the WPT and the DWT.

Stationary wavelet transform

Heart beat

SIGNAL (programming language)

Second-generation wavelet transform

10.14311/nnw.2022.32.003

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Citations (4)

The Learning Motivating Strategy for Engineering Education

Current Journal of Applied Science and Technology (2018)

Khaled Daqrouq Mohammed N. Ajour

10.9734/cjast/2019/44042

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Citations (0)

Wavelet Transform based multistage speaker feature tracking identification system using Linear Prediction Coefficient

International Conference on Advances in Computational Tools for Engineering Applications (2009)

Khaled Daqrouq Abdel‐Rahman Al‐Qawasmi Wael Al-Sawalmeh Tareq Abu Hilal

In this paper wavelet transform (WT) in its two forms continuous and discrete are used to create text-dependent robust to noise speaker recognition system. The research intends to investigate a high accuracy of identification the speech signal of very difficult nature that is non- stationary. Three methods are used to extract the essential speaker features based on continuous, discrete wavelet transform and linear prediction coefficient (LPC). To have better identification rate three measurement methods are used: percentage rms difference (PRD), correlation coefficient (CC), and statically deformation determination coefficient (SDDC). 95% identification rate is accomplished. The presented system in this paper depends on multi-stage features extracting due to its better accuracy. The system works with excellent capability of features tracking even when the tested signals are very noisy with -32dB SNR. This is accomplished because of multistage features tracking based system using wavelet transform, which is suitable for non-stationary signal.

Stationary wavelet transform

Second-generation wavelet transform

SIGNAL (programming language)

Tracking (education)

Continuous wavelet transform

Identification

Feature (linguistics)

10.1109/actea.2009.5227955

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Citations (3)

An investigation of speech enhancement using wavelet filtering method

International Journal of Speech Technology (2010)

Khaled Daqrouq Ibrahim N. Abu-Isbeih Omar Daoud Emad Khalaf

Biorthogonal wavelet

SIGNAL (programming language)

Convolution (computer science)

10.1007/s10772-010-9073-1

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Citations (24)

Wavelet Linear Prediction Coding with Feed Forward Backpropagation Neural Network for Noisy Speaker Identification System

Journal of Applied Sciences (2014)

Khaled Daqrouq Abdulhameed F. Alkhateeb Mohammad Ajour Ali Morfeq

Backpropagation

Speaker identification

Identification

Linear prediction

Feedforward neural network

Predictive coding

10.3923/jas.2014.2341.2349

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Citations (1)

Speaker identification using vowels features through a combined method of formants, wavelets, and neural network classifiers

Applied Soft Computing (2014)

Khaled Daqrouq Tarek A. Tutunji

Feature vector

Feature (linguistics)

10.1016/j.asoc.2014.11.016

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Citations (62)

Simulation and Analysis with Wavelet Transform Technique and the Vibration Characteristics for Early Revealing of Cracks in Structures

Mathematical Problems in Engineering (2021)

Nidal H. Abu‐Hamdeh Khaled Daqrouq Fateh Mebarek‐Oudina

Implementation of improved instruments is used to detect damage in an accurate manner and fully analyze its characteristics. An aluminum beam has been used in this work to identify cracks by using a vibration technique. The simulation of frequency response feature was conducted using a finite element model to provide average measures of intensities of vibration. Two forms of wavelet packet transform (WPT) entropies Shannon and log energy were applied to identify the position, width, and size of the crack. The results showed that with an increase in crack depth, the amplitude also increased at certain crack sizes and for all crack positions. For two crack depths of 1.6 mm and 0.16 mm having the same crack size and position 12 mm and 60 mm, respectively, a 4.5% increase in amplitude was observed at a crack depth of 1.6 mm. Moreover, the amplitude varied inversely with the position. A 12.6% increase in amplitude was observed at a crack depth of 1.6 mm rather than 0.16 mm, while both depths occurred at the same crack position (75 mm) and size (20 mm). Experimental validation was performed on a cantilever beam with one crack. The maximum absolute error found was 7.5% for the crack position and 9.1% for the crack size. With the increase in crack depth, the obtained results decrease the stiffness of a beam in a single crack case.

Position (finance)

10.1155/2021/6626232

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Citations (18)

Speech signal enhancement using neural network and wavelet transform

Khaled Daqrouq Ibrahim N. Abu-Isbeih Mikhled Alfauri

Speech enhancement is concerned with the processing of corrupted or noisy speech signal in order to improve the quality or intelligibility of the signal. Our goal is to enhance speech signal corrupted by noise to obtain a clean signal with higher quality. However, the presence of noise in speech signals will contribute to a high degree of inaccuracy in a system that requires speech processing. This idea of noise cancellation for the speech signal was processed through the neural networks. Three methods were tested: 1. The adaptive linear neuron (ADALINE). 2. Feed Forward Neural Network Enhancement Method FFNN 3. Wavelet Transform and Adaline Enhancement Method. The results obtained showed high quality due to fast processing and high signal-noise-ratio. The tested signal was enhanced 10 dB by Adaline, 3 dB by FFNN and 8 dB by Wavelet Transform and Adaline Enhancement Method.

Intelligibility (philosophy)

SIGNAL (programming language)

10.1109/ssd.2009.4956823

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Citations (8)