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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Mohamed Khalil-Hani
Universiti Teknologi Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Ventricular Tachyarrhythmia Prediction based on Heart Rate Variability and Genetic Algorithm Khang Hua Boon; Malarvili Bala Krishnan; Mohamed Khalil-Hani
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 3: September 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i3.3665

Abstract

Predicting ventricular tachyarrhythmia (VTA) provides opportunities to reduce casualties due to sudden cardiac death. However, prediction accuracy is still need improvement. In this paper, we propose a method that can predict VTA events using support vector machine (SVM) that trained with HRV features from heart rate variability (HRV). The Spontaneous Ventricular Tachyarrhythmia Database (Medtronic Version 1.0), comprising 106 pre-VT records, 26 pre-VF records, and 135 control data, is used.  Fifty percent of the data was used to train the SVM, and the remainder was used to verify the performance. Each data set was subjected to preprocessing and HRV feature extraction. After correcting the ectopic beats, 5 minutes RR intervals prior to each event was cropped for feature extraction. Extraction of the time domain, spectral, non-linear and bispectrum features were performed subsequently. Furthermore, both t-test and genetic algorithm (GA) were used to optimize the HRV feature subset. With optimized feature subset by GA, proposed method of current work able to outperform previous works with 77.94%, 80.88% and 79.41 % for senstivity, specificity and accuracy respectively.
A Low-complexity Complex-valued Activation Function for Fast and Accurate Spectral Domain Convolutional Neural Network Shahriyar Masud Rizvi; Ab Al-Hadi Ab Rahman; Mohamed Khalil-Hani; Sayed Omid Ayat
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 9, No 1: March 2021
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v9i1.2737

Abstract

Conventional Convolutional Neural Networks (CNNs), which are realized in spatial domain, exhibit high computational complexity. This results in high resource utilization and memory usage and makes them unsuitable for implementation in resource and energy-constrained embedded systems. A promising approach for low-complexity and high-speed solution is to apply CNN modeled in the spectral domain. One of the main challenges in this approach is the design of activation functions. Some of the proposed solutions perform activation functions in spatial domain, necessitating multiple and computationally expensive spatial-spectral domain switching. On the other hand, recent work on spectral activation functions resulted in very computationally intensive solutions. This paper proposes a complex-valued activation function for spectral domain CNNs that only transmits input values that have positive-valued real or imaginary component. This activation function is computationally inexpensive in both forward and backward propagation and provides sufficient nonlinearity that ensures high classification accuracy. We apply this complex-valued activation function in a LeNet-5 architecture and achieve an accuracy gain of up to 7% for MNIST and 6% for Fashion MNIST dataset, while providing up to 79% and 85% faster inference times, respectively, over state-of-the-art activation functions for spectral domain.
Co-Authors Ab Al-Hadi Ab Rahman Khang Hua Boon Malarvili Bala Krishnan Sayed Omid Ayat Shahriyar Masud Rizvi