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Author: search.filters.author.Wangsiripitak, SomkiatSubject: search.filters.subject.machine learning

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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
    ;
    Pluempitiwiriyawej, Charnchai
    ;
    Wangsiripitak, Somkiat
    ;
    Murtaza, Ghulam
    ;
    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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    Learning Extended Term Frequency-Inverse Document Frequency (TF-IDF++) for Depression Screening From Sentences in Thai Blog Post
    (2023-01-01)
    Khunruksa, Sahussawud
    ;
    Wangsiripitak, Somkiat
    This paper proposed the method of depression screening from a sentence in Thai blog posts. Three classifiers based on a decision tree, linear SVC, and logistic regression were used to create classification models; each learned from extended term frequency-inverse document frequency (TF-IDF++) which is a feature vector created from a term frequency-inverse document frequency (TF-IDF), part-of-speech, and statistics of sentences such as word counts of selected terms. Our experiments showed that the model based on logistic regression achieves the top average score with a precision of 78.32%, a recall of 78.26%, and an f1-score of 78.27%. The proposed method outperforms the Thai BERT model by 0.75%, 0.77%, and 0.76%, respectively. Our investigation also showed that excessive confidence in the Thai BERT model tends to classify a sample with high probability. This also happens in case of an incorrect prediction; the error in such a case becomes noticeably higher than that of the wrong prediction in our proposed logistic regression-based model.