Search Results - "Oh, Shu Lih"

Automated recognition of major depressive disorder from cardiovascular and respiratory physiological signals by Zitouni, M Sami, Lih Oh, Shu, Vicnesh, Jahmunah, Khandoker, Ahsan, Acharya, U Rajendra

“…Major Depressive Disorder (MDD) is a neurohormonal disorder that causes persistent negative thoughts, mood and feelings, often accompanied with suicidal…”
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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals by Acharya, U. Rajendra, Oh, Shu Lih, Hagiwara, Yuki, Tan, Jen Hong, Adeli, Hojjat

“…An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical…”
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Deep learning for healthcare applications based on physiological signals: A review by Faust, Oliver, Hagiwara, Yuki, Hong, Tan Jen, Lih, Oh Shu, Acharya, U Rajendra

“…•Importance: In 2017 the number of publications on deep learning for physiological signal analysis increased significantly. Indeed, 2017 saw more papers…”
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Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals by Acharya, U. Rajendra, Fujita, Hamido, Oh, Shu Lih, Hagiwara, Yuki, Tan, Jen Hong, Adam, Muhammad

“…•Classification of normal and MI ECG beats.•With and without noise ECG beats are considered.•Convolutional neural network is employed.•R peak detection is not…”
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Automated EEG-based screening of depression using deep convolutional neural network by Acharya, U. Rajendra, Oh, Shu Lih, Hagiwara, Yuki, Tan, Jen Hong, Adeli, Hojjat, Subha, D. P

“…•Classification of normal and depression using EEG signals.•Employed a 13-layer deep convolutional neural network model.•Minimum hand-crafted features required…”
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Automated detection of schizophrenia using nonlinear signal processing methods by Jahmunah, V., Lih Oh, Shu, Rajinikanth, V., Ciaccio, Edward J., Hao Cheong, Kang, Arunkumar, N., Acharya, U. Rajendra

“…•Automated detection of schizophrenia is proposed.•Nonlinear features are extracted from EEG signals.•Obtained classification accuracy of 92.91% using SVM…”
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A deep learning approach for Parkinson’s disease diagnosis from EEG signals by Oh, Shu Lih, Hagiwara, Yuki, Raghavendra, U., Yuvaraj, Rajamanickam, Arunkumar, N., Murugappan, M., Acharya, U. Rajendra

“…An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by…”
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Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network by Acharya, U. Rajendra, Fujita, Hamido, Oh, Shu Lih, Raghavendra, U., Tan, Jen Hong, Adam, Muhammad, Gertych, Arkadiusz, Hagiwara, Yuki

“…Ventricular tachycardia (VT) and ventricular fibrillation (VFib) are the life-threatening shockable arrhythmias which require immediate attention…”
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Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals by Acharya, U Rajendra, Fujita, Hamido, Oh, Shu Lih, Hagiwara, Yuki, Tan, Jen Hong, Adam, Muhammad, Tan, Ru San

“…Congestive heart failure (CHF) is a chronic heart condition associated with debilitating symptoms that result in increased mortality, morbidity, healthcare…”
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Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals by Tan, Jen Hong, Hagiwara, Yuki, Pang, Winnie, Lim, Ivy, Oh, Shu Lih, Adam, Muhammad, Tan, Ru San, Chen, Ming, Acharya, U. Rajendra

“…Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the…”
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Characterization of focal EEG signals: A review by Acharya, U. Rajendra, Hagiwara, Yuki, Deshpande, Sunny Nitin, Suren, S., Koh, Joel En Wei, Oh, Shu Lih, Arunkumar, N., Ciaccio, Edward J., Lim, Choo Min

“…Epilepsy is a common neurological condition that can occur in anyone at any age. Electroencephalogram (EEG) signals of non-focal (NF) and focal (F) types…”
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Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review by Hagiwara, Yuki, Fujita, Hamido, Oh, Shu Lih, Tan, Jen Hong, Tan, Ru San, Ciaccio, Edward J, Acharya, U Rajendra

“…•Existing AF detection techniques are discussed.•Building blocks of CADx system are described.•Different features explored by researchers are…”
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Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals by Oh, Shu Lih, Vicnesh, Jahmunah, Ciaccio, Edward J, Yuvaraj, Rajamanickam, Acharya, U Rajendra

“…A computerized detection system for the diagnosis of Schizophrenia (SZ) using a convolutional neural system is described in this study. Schizophrenia is an…”
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Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study by Acharya, U Rajendra, Fujita, Hamido, Adam, Muhammad, Lih, Oh Shu, Sudarshan, Vidya K, Hong, Tan Jen, Koh, Joel EW, Hagiwara, Yuki, Chua, Chua K., Poo, Chua Kok, San, Tan Ru

“…•Classification of three types of ECG beats are proposed.•Normal, CAD and MI are three classes considered in this work.•DCT, DWT and EMD decomposition methods…”
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EpilepsyNet: Novel automated detection of epilepsy using transformer model with EEG signals from 121 patient population by Lih, Oh Shu, Jahmunah, V., Palmer, Elizabeth Emma, Barua, Prabal D., Dogan, Sengul, Tuncer, Turker, García, Salvador, Molinari, Filippo, Acharya, U Rajendra

“…Epilepsy is one of the most common neurological conditions globally, and the fourth most common in the United States. Recurrent non-provoked seizures…”
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Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals by Koh, Joel.E.W., Ooi, Chui Ping, Lim-Ashworth, Nikki SJ, Vicnesh, Jahmunah, Tor, Hui Tian, Lih, Oh Shu, Tan, Ru-San, Acharya, U.Rajendra, Fung, Daniel Shuen Sheng

“…The most prevalent neuropsychiatric disorder among children is attention deficit hyperactivity disorder (ADHD). ADHD presents with a high prevalence of…”
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Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads by Acharya, U. Rajendra, Fujita, Hamido, Sudarshan, Vidya K., Oh, Shu Lih, Adam, Muhammad, Koh, Joel E.W., Tan, Jen Hong, Ghista, Dhanjoo N., Martis, Roshan Joy, Chua, Chua K., Poo, Chua Kok, Tan, Ru San

“…Identification and timely interpretation of changes occurring in the 12 electrocardiogram (ECG) leads is crucial to identify the types of myocardial infarction…”
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Future IoT tools for COVID‐19 contact tracing and prediction: A review of the state‐of‐the‐science by Jahmunah, Vicnesh, Sudarshan, Vidya K., Oh, Shu Lih, Gururajan, Raj, Gururajan, Rashmi, Zhou, Xujuan, Tao, Xiaohui, Faust, Oliver, Ciaccio, Edward J., Ng, Kwan Hoong, Acharya, U. Rajendra

“…In 2020 the world is facing unprecedented challenges due to COVID‐19. To address these challenges, many digital tools are being explored and developed to…”
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Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal by Acharya, U Rajendra, Fujita, Hamido, Sudarshan, Vidya K, Oh, Shu Lih, Adam, Muhammad, Tan, Jen Hong, Koo, Jie Hui, Jain, Arihant, Lim, Choo Min, Chua, Kuang Chua

“…•Classification of normal, CAD, MI and CHF classes using ECG beat is proposed•CWT transform is performed on ECG beat•Contourlet and shearlet transforms are…”
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Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020) by Loh, Hui Wen, Ooi, Chui Ping, Vicnesh, Jahmunah, Oh, Shu Lih, Faust, Oliver, Gertych, Arkadiusz, Acharya, U. Rajendra

“…Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep…”
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