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Author: search.filters.author.Pluempitiwiriyawej, Charnchai

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    The Applications of Deep Learning in ECG Classification for Disease Diagnosis: A Systematic Review and Meta-Data Analysis
    (2024-08-31)
    Khalid, Mudassar
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    Pluempitiwiriyawej, Charnchai
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    Wangsiripitak, Somkiat
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    Murtaza, Ghulam
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    Abdulkadhem, Abdulkadhem A.
    The supremacy of deep learning in artificial intelligence (AI) contexts, including image and speech recognition, computer vision, and medical imaging, among others, has established it as AI’s dominant approach. Several studies have been conducted on the use of deep learning in physiological signals, especially in ECG signals, in recent years, but there has been a lack of comprehensive review on the use of deep learning in ECG for biometric systems. This review is divided into two main sections: it provides a comprehensive bibliographic review of deep learning for ECG classification towards assisting in disease diagnosis in the first part while presenting an overview of the field, pioneers, and landmark studies. The second part offers comprehensive information on the subject, starting with the mathematical background of deep learning algorithms, the ECG signal processing, and the function of the heart. Using a PRISMA framework, 309 research papers were initially identified through specified keywords. After applying inclusion criteria, 90 articles were retained for detailed analysis, excluding 24 documents based on exclusion criteria EC1 and the remainder due to EC2. Key findings reveal that deep learning models achieve an average accuracy improvement of 10-15% over traditional methods, with convolutional neural networks (CNNs) and recurrent neural networks (RNNs) demonstrating superior performance in capturing complex ECG patterns. Through ECG databases, deep learning algorithms, assessment frameworks, metrics, and code availability, this review designs a systematic view from different perspectives to highlight the trends, challenges, and opportunities of deep learning for ECG arrhythmia classification. This paper’s goal is to contribute to the knowledge of both new and experienced researchers and practitioners in the field so that they can learn and understand the various processes involved in ECG signal processing using deep learning.
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    Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos
    (2022-07-01)
    Aung, Hsu Mon Lei
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    Pluempitiwiriyawej, Charnchai
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    Hamamoto, Kazuhiko
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    Wangsiripitak, Somkiat
    Biometric recognition is a critical task in security control systems. Although the face has long been widely accepted as a practical biometric for human recognition, it can be easily stolen and imitated. Moreover, in video surveillance, it is a challenge to obtain reliable facial information from an image taken at a long distance with a low-resolution camera. Gait, on the other hand, has been recently used for human recognition because gait is not easy to replicate, and reliable information can be obtained from a low-resolution camera at a long distance. However, the gait biometric alone still has constraints due to its intrinsic factors. In this paper, we propose a multimodal biometrics system by combining information from both the face and gait. Our proposed system uses a deep convolutional neural network with transfer learning. Our proposed network model learns discriminative spatiotemporal features from gait and facial features from face images. The two extracted features are fused into a common feature space at the feature level. This study conducted experiments on the publicly available CASIA-B gait and Extended Yale-B databases and a dataset of walking videos of 25 users. The proposed model achieves a 97.3 percent classification accuracy with an F1 score of 0.97and an equal error rate (EER) of 0.004.
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    Real-Time Vision Based Human Height Measurement Using Sliding Window on Selected Candidates
    (2018-07-02)
    Dokthurian, Siriporn
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    Pluempitiwiriyawej, Charnchai
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    Wangsiripitak, Somkiat
    This paper presents a real-time human height estimation using image sequences obtained from a single calibrated camera. For each image frame, the candidate value of human height is calculated based on the approximated 3D ground plane and 2D positions of human head top and foot bottom. Some candidates whose values do not differ much from the previous height estimate are selected; a sliding window is then applied on an array of those candidate height values; candidate heights bounded inside the sliding window that has the maximum votes and some candidate heights around that window are finally used in final height estimate. The proposed algorithm of sliding window on selected candidate helps achieve the average accuracy of height estimate at 99.05%; it is a 1.30% increase of accuracy when compared to the final height estimate from all candidates. The standard deviation also decreases from 4.26 to 1.58; the proposed method is superior in terms of accuracy and stability.
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    Feature-based motion detection and tracking on approximate 3D ground plane
    (2017-01-04)
    Saelao, Wongsatorn
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    Wangsiripitak, Somkiat
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    Pluempitiwiriyawej, Charnchai
    The success of movement detection based on the distance moved in a 2D image sequence depends highly on the angle between a camera's optical axis and the normal vector of the ground plane on which the moving object is traveling. When the same 3D displacement occurs at various positions in the scene, the higher the angle is, the greater the distance observed in the image at a position close to camera differs from (strictly speaking, is larger than) those happening at the far end. As a consequence, a detection failure and/or false alarm may occur if no such 3D geometry is utilized. This paper estimates a 3D ground plane, which is then used to measure the approximate 3D displacement of features being detected and tracked. The 3D distances are therefore available and utilized in deciding whether they are of moving objects or just blinking features caused by illumination changes. FAST points are used to enhance a real-time system. Experimental results show superior performance in tracking: a longer trace of continuous tracking, a higher number of detected moving features, earlier detection, better recall rate, no misses, and no false alarms. A SURF descriptor and FLANN matcher were utilized here, however the robustness was not much enhanced when compared to the expense of finding the best match.