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Detection of electrocardiogram QRS complex based on modified adaptive threshold Ehab AbdulRazzaq Hussein; Ali Shaban Hassooni; Hilal Al-Libawy
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 5: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (840.34 KB) | DOI: 10.11591/ijece.v9i5.pp3512-3521

Abstract

It is essential for medical diagnoses to analyze Electrocardiogram (ECG signal). The core of this analysis is to detect the QRS complex. A modified approach is suggested in this work for QRS detection of ECG signals using existing database of arrhythmias. The proposed approach starts with   the same steps of previous approaches by filtering the ECG. The filtered signal is then fed to a differentiator to enhance the signal. The modified adaptive threshold method which is suggested in this work, is used to detect QRS complex. This method uses a new approach for adapting threshold level, which is based on statistical analysis of the signal. Forty-eight records from an existing arrhythmia database have been tested using the modified method. The result of the proposed method shows the high performance metrics with sensitivity of 99.62% and a positive predictivity of 99.88% for QRS complex detection.
Efficient SOVA decoding and enhanced early termination mechanism based on new attenuation factors Ahmed A. Hamad; Hussain F. Jaafar; Hilal Al-Libawy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 2: April 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i2.23164

Abstract

This paper proposes two attenuation factors (AF’s) to improve the performance of the soft-output Viterbi algorithm (SOVA) as well as to enhance the early termination mechanism in turbo decoding. The mean square difference between the systematic bipolar coded symbols and the a-posteriori information is used to estimate the first AF. The second AF is computed online for each iteration based on the correlation coefficient between the extrinsic and a-priori information instead of intrinsic information as customary to calculate in literature. The second factor is used in the early termination (ET) scheme which is practically useful to terminate iterations when there is no significant improvement is achieved. In addition, a method for offline computing the AF’s that cover a specific range of signal-to-noise power ratio is provided which results in a reduction of utilization and latency estimates with a shallow degradation in performance. The results show that the proposed scheme outperforms the previous related works by about 0.2 dB at bit error rate (BER) of 10-5 using interleaver depth of 512 and reducing the average number of iterations (ANI) by about 3 iterations.
A new approach for varied speed weigh-in-motion vehicle based on smartphone inertial sensors Ahmed A. Hamad; Yasseen Sadoon Atiya; Hilal Al-Libawy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i4.pp%p

Abstract

Dynamic vehicle weight measuring, weigh-in-motion (WIM), is an important metric that can reflect significantly vehicle driving behaviour and in turn, it will affect both safety and traffic status. Several accurate (WIM) systems are developed and implemented successfully. These systems are using under road weighing sensor which are costly to implement. Moreover, it is costly and not very practical to embed a continuous weighing system in used cars. In this work, a low-cost varied-speed weigh-in-motion approach was suggested to continuously measuring vehicle load based on the response of smartphone sensors which is a reflection of vehicle dynamics. This approach can apply to any moving vehicle at any driving speed without the need for extra added hardware which makes it very applicable because smartphone is widely used device. The approach was tested through a six-trips experiment. Three capacities of load had been designed in this approach to be classified using a neural network classifier. The classification performance metrics are calculated and show an accuracy of 91.2%. This accuracy level is within error limits of existing WIM systems especially for high speed and proved the success of the suggested approach.