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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Pham Thi Viet Huong
Vietnam National University

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A novel fast-qualitative balance test method of screening for vestibular disorder patients Tran Anh Vu; Hoang Quang Huy; Le Van Tuan; Pham Thi Viet Huong
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp910-919

Abstract

Body balance test is one of the methods of assessing vestibular level. However, the results are still qualitative, depending on the subjectivity of the doctor. This study proposes a new, low-cost method to quantitatively determine the degree of body imbalance. The proposal includes a low-cost laser source, a proposed rectangular paper frame, a camera, and a computer. The rectangular frame is mounted on the patient. The laser source is fixed and projected onto this rectangular frame. The laser projection point is taken as the origin point to evaluate the movement of the frame, which is also the movement of the patient’s body. This rectangular frame is pre-marked with points to get more accuracy of the position of the laser point. Therefore, this measurement is not affected by the position of the camera during recording. The video is then procecced by computer to determine the position of laser point, it is also presented the movement of the patient’s body. Initial trials were conducted on vestibular and normal patients. The results show that there is a clear difference in the balance of the vestibular and healthy people. The proposed method can be used to support quantitative screening for vestibular disease.
Classify arrhythmia by using 2D spectral images and deep neural network Tran Anh Vu; Hoang Quang Huy; Pham Duy Khanh; Nguyen Thi Minh Huyen; Trinh Thi Thu Uyen; Pham Thi Viet Huong
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 2: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i2.pp931-940

Abstract

Electrocardiogram (ECG) is the most common method for monitoring the working of the heart. ECG signal is the basis to determine normal or abnormal rhythm, thereby helping to accurately diagnose cardiovascular diseases. Therefore, an automatic algorithm to detect and diagnose abnormal heart rhythms is essential. There are many methods of classifying arrhythmias using machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), based on the features extracted from the record of ECG signal. Actually, deep learning algorithms are evolving and highly effective in image analysis and processing. In this research, a dense neural network model is proposed to classify normal and abnormal beats. Input ECG signal presenting a time series is converted into 2-D spectral image by applying wavelet transform. Our research is evaluated based on using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. The accuracy of the classification algorithm we employ is 99.8%, demonstrating the model's validity when compared to other reports' findings. This is the foundation for our algorithm to prove it can be utilized as an efficient model for categorizing arrhythmia using ECG signals.
A novel approach for detecting diabetic retinopathy using two-stream CNNs model Pham Thi Viet Huong; Le Duc Thinh; Tran Thi Oanh; Tran Xuan Bach; Hoang Quang Huy; Tran Anh Vu
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp200-209

Abstract

Major causes of visual impairment, particularly diabetic retinopathy (DR) and aged-related macular degeneration (AMD), has posed significant challenges for clinical diagnosis and treatment. Early detection and prompt intervention can help prevent severe consequences for patients. The study presents a novel approach for detecting eye diseases using a two-stream convolutional neural network (CNN) model. The first stream processes preprocessed fundus images, while the second stream analyzes high-pass filtered fundus images in the spatial frequency domain. To assess the model’s performance, we use the APTOS 2019 dataset, which was originally compiled for the Asia Pacific Tele-Ophthalmology Society 2019 Blindness Detection competition and is publicly available on Kaggle. Our method shows promise as an early screening tool for DR detection with an accuracy of 0.986.
Co-Authors Hoang Quang Huy Le Duc Thinh Le Van Tuan Nguyen Thi Minh Huyen Pham Duy Khanh Tran Anh Vu Tran Thi Oanh Tran Xuan Bach Trinh Thi Thu Uyen