Mr Rashid Ghorbani Afkhami
I have received my bachelor and master degrees in Electrical Engineering from the University of Tabriz, Iran, in 2012 and 2014, respectively. I have started my Ph.D. in Electrical Engineering at the University of Newcastle in 2017. My current research interests are biomedical signal processing techniques and optical imaging.
- Signal Processing
- Azeri (Mother)
- English (Working)
- Turkish (Fluent)
Fields of Research
|090399||Biomedical Engineering not elsewhere classified||50|
For publications that are currently unpublished or in-press, details are shown in italics.
Journal article (2 outputs)
Sultan Qurraie S, Ghorbani Afkhami R, 'ECG arrhythmia classification using time frequency distribution techniques', Biomedical Engineering Letters, 7 325-332 (2017) [C1]
Ghorbani Afkhami R, Azarnia G, Tinati MA, 'Cardiac arrhythmia classification using statistical and mixture modeling features of ECG signals', Pattern Recognition Letters, 70 45-51 (2016)
Conference (3 outputs)
Afkhami RG, Low K, Walker F, Johnson S, 'A Dynamic Model of Synthetic Resting-State Brain Hemodynamics', 2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), Rome, ITALY (2018) [E1]
Azarnia G, Tinati MA, Ghorbani Afkhami R, 'Heart blocks detection in ECG signals using time frequency distribution techniques', Electrical Engineering (ICEE), 2016 24th Iranian Conference on, IEEE (2016)
Afkhami RG, Tinati MA, 'ECG based detection of left ventricular hypertrophy using higher order statistics', ICEE 2015 - Proceedings of the 23rd Iranian Conference on Electrical Engineering (2015)
© 2015 IEEE. Electrocardiogram (ECG) is a popular non-invasive test record, which shows the electrical activities of the heart. In this paper we propose a novel method to detect l... [more]
© 2015 IEEE. Electrocardiogram (ECG) is a popular non-invasive test record, which shows the electrical activities of the heart. In this paper we propose a novel method to detect left ventricular hypertrophy (LVH) with the use of ECG. Left ventricular hypertrophy is defined as the enlargement of the left ventricle, which is a common disease among hypertension patients. Proposed algorithm uses discrete wavelet transform (DWT) to extract morphological features of ECG signal and exploits higher order statistic (HOS) features including kurtosis, skewness and 5th moment. These features are fed to a support vector machine (SVM) classifier with kernel function of radial basis function (RBF). Our method has been tested on a large database of ECG signals and we have obtained the highest accuracy of 99.6% and sensitivity of 99.4%.