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U Rajendra Acharya

U Rajendra Acharya

(SCI Highly Cited Researcher 2016-2022) Ph D; D Eng; DSc

About

876
Publications
565,819
Reads
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49,485
Citations
Introduction
He is ranked in the top 1% of the Highly Cited Researchers for the past four consecutive years (2016, 2017,2018, and 2019) in Computer Science according to the Essential Science Indicators of Thomson. (Hirsch index=84;https://scholar.google.nl/citations?user=8FjY99sAAAAJ&hl=de). Senior Editor of Computers in Biology Medicine (CBM) and AE of ISI Journals Computer Methods and Programs in Biomedicine (CMPB), International Journal of Neural Systems (IJNS), Knowledge Based Systems (KBS), Biomedical Engineering OnLine (BMEOL), International Journal of Environmental Research and Public Health (IJERPH), International Journal of Imaging Systems and Technology (IMA), Journal of Translational Internal Medicine (JTIM), Engineering Reports, and Informatics in Medicine Unlocked (IMU)etc.
Additional affiliations
January 2012 - November 2014
Ngee Ann Polytechnic, University of Malaya, SIM University, University of Glasgow
Position
  • Faculty Member
August 2001 - November 2014
Ngee Ann Polytechnic, SIM University, University of Glasgow
Position
  • Faculty Member

Publications

Publications (876)
Article
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Our cardiovascular system weakens and is more prone to arrhythmia as we age. An arrhythmia is an abnormal heartbeat rhythm which can be life-threatening. Atrial fibrillation (Afib), atrial flutter (Afl), and ventricular fibrillation (Vfib) are the recurring life-threatening arrhythmias that affect the elderly population. An electrocardiogram (ECG)...
Article
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The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart’s electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amp...
Article
Full-text available
The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their c...
Article
Full-text available
An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provid...
Article
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Coronary artery disease (CAD) is caused due by the blockage of inner walls of coronary arteries by plaque. This constriction reduces the blood flow to the heart muscles resulting in myocardial infarction (MI). The electrocardiogram (ECG) is commonly used to screen the cardiac health. The ECG signals are nonstationary and nonlinear in nature whereby...
Article
Electroencephalography (EEG) signal is an important physiological signal commonly used in machine learning to decode brain activities, including imagined words and sentences. We aimed to develop an automated lightweight EEG signal-based sentence classification model using a novel dynamic-sized binary pattern (DSBP) textural feature extractor and it...
Article
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Background and objectives : Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box...
Article
Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating...
Article
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COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected pers...
Article
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Grasping is a challenging problem in robotics and prosthetic applications due to its control requirements. The visual percep-tion and analyzing electromyography (EMG) signals are the two ways to give the inputs to robots and prosthetic amputees for grasping abilities. The EMG is a diagnostic manner that evaluates the fitness condition of skeletal m...
Article
Prostate cancer (PCa) is the most common type of cancer among men. Digital rectal examination and prostate-specific antigen (PSA) tests are used to diagnose the PCa accurately. Since PSA is organ-specific and not disease-specific, multiparametric magnetic resonance imaging (mpMRI) is used to reduce unnecessary biopsies. Prostate imaging reporting a...
Article
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities...
Article
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The polymerase chain reaction (PCR) test is not only time-intensive but also a contact method that puts healthcare personnel at risk. Thus, contactless and fast detection tests are more valuable. Cough sound is an important indicator of COVID-19, and in this paper, a novel explainable scheme is developed for cough sound-based COVID-19 detection. In...
Article
Ultrasound (US) is an important imaging modality used to assess breast lesions for malignant features. In the past decade, many machine learning models have been developed for automated discrimination of breast cancer versus normal on US images, but few have classified the images based on the Breast Imaging Reporting and Data System (BI-RADS) class...
Article
Recent advances in remote patient monitoring (RPM) systems can recognize various human activities to measure vital signs, including subtle motions from superficial vessels. There is a growing interest in applying artificial intelligence (AI) to this area of healthcare by addressing known limitations and challenges such as predicting and classifying...
Article
Abstract Objectives The tumor microenvironment (TME) consists of cellular and noncellular components which enable the tumor to interact with its surroundings and plays an important role in the tumor progression and how the immune system reacts to the malignancy. In the present study, we investigate the diagnostic potential of the TME in differentia...
Article
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Diabetic retinopathy (DR) is a common complication of diabetes that can lead to progressive vision loss. Regular surveillance with fundal photography, early diagnosis, and prompt intervention are paramount to reducing the incidence of DR-induced vision loss. However, manual interpretation of fundal photographs is subject to human error. In this stu...
Article
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Electroencephalography (EEG) may detect early changes in Alzheimer's disease (AD), a debilitating progressive neurodegenerative disease. We have developed an automated AD detection model using a novel directed graph for local texture feature extraction with EEG signals. The proposed graph was created from a topological map of the macroscopic connec...
Article
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An early stage detection of Parkinson's disease (PD) is crucial for its appropriate treatment. The quality of life degrades with the advancement of the disease. In this paper, we propose a natural (time) domain technique for the diagnosis of PD. The proposed technique eliminates the need for transformation of the signal to other domains by extracti...
Article
Full-text available
Coronary artery disease (CAD) is the leading cause of morbidity and death worldwide. Invasive coronary angiography is the most accurate technique for diagnosing CAD, but is invasive and costly. Hence, analytical methods such as machine learning and data mining techniques are becoming increasingly more popular. Although physicians need to know which...
Article
Background and purpose : Rotator cuff tear (RCT) and biceps tendinosis (BT) are the two most common shoulder disorders worldwide. These disorders can be diagnosed using magnetic resonance imaging (MRI), but the expert interpretation is manual, time-consuming, and subjected to human errors. Therefore, a fixed-size feature extraction model was create...
Article
Problem Cough-based disease detection is a hot research topic for machine learning, and much research has been published on the automatic detection of Covid-19. However, these studies are useful for the diagnosis of different diseases. Aim In this work, we collected a new and large (n=642 subjects) cough sound dataset comprising four diagnostic ca...
Preprint
Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitt...
Article
Full-text available
Sleep apnea is a severe sleep disorder that degrades the quality of sleep and causes patients to feel restless and fatigued even after a full night's sleep. Sleep-disordered breathing (SDB) research has grown its importance in recent decades, owing to patients' substantial health effects and poor quality of life. Obstructive sleep apnea (OSA), the...
Article
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Language classification using speeches is a complex issue in machine learning and pattern recognition. Various text and image-based language classification methods have been presented. But there are limited speech-based language classification methods in the literature. Also, the previously presented models classified limited numbers of languages,...
Article
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Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals worldwide. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligen...
Article
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Coronary artery disease (CAD) is a prevalent disease with high morbidity and mortality rates. Invasive coronary angiography is the reference standard for diagnosing CAD but is costly and associated with risks. Noninvasive imaging like cardiac magnetic resonance (CMR) facilitates CAD assessment and can serve as a gatekeeper to downstream invasive te...
Article
The management of intracerebral hemorrhage (ICH) requires prompt diagnostic assessment and recognition. Accurate localization and categorization of ICH-type is crucial. There are two main categories of ICH: 1) hemorrhagic stroke (HS), which occurs in the deeper or subcortical regions of the brain, where the arterial network tapers to fine end-arter...
Article
Objective : Parkinson's disease (PD) is a common neurological disorder with variable clinical manifestations and magnetic resonance imaging (MRI) findings. We propose a handcrafted image classification model that can accurately (i) classify different PD stages, (ii) detect comorbid dementia, and (iii) discriminate PD-related motor symptoms. Method...
Article
Artificial intelligence (AI) algorithms have an enormous potential to impact the field of radiology and diagnostic imaging, especially the field of cancer imaging. There have been efforts to use AI models to differentiate between benign and malignant breast lesions. However, most studies have been single-center studies without external validation....
Article
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Myocarditis is heart muscle inflammation that is becoming more prevalent these days, especially with the prevalence of COVID-19. Noninvasive imaging cardiac magnetic resonance (CMR) can be used to diagnose myocarditis, but the interpretation is time-consuming and requires expert physicians. Computer-aided diagnostic systems can facilitate the autom...
Preprint
Full-text available
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these...
Article
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Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered i...
Article
Full-text available
In this study, we evaluate and compare the diagnostic performance of ultrasound for non‐invasive axillary lymph node (ALN) metastasis detection. The study was based on fusing shear wave elastography (SWE) and B‐mode ultrasonography (USG) images. These images were subjected to pre‐processing and feature extraction, based on bi‐dimensional empirical...
Article
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High quality and efficient medical service is one of the major factors defining living standards. Developed countries strive to make their healthcare systems as efficient and cost-effective as possible. Remote medical services are a promising approach to lower medical costs and, at the same time, accelerating diagnosis and treatment of diseases. In...
Article
Hypertension (HPT) is a physiological abnormality characterized by high blood pressure, headache, wooziness, and fainting that may lead to various heart, kidney, or brain diseases. Detection and continuous monitoring of HPT by sphygmometer is arduous and hectic. Nowadays, ballistocardiogram (BCG) signals are used to determine HPT as it indicates th...
Article
Advanced cervical screening via liquid-based cytology (LBC)/Pap smear is a highly efficient precancerous cell detection tool based on cell image analysis, in which cells are classified as normal/abnormal. This paper outlines the drawbacks by introducing a new framework for the accurate classification of cervical cells. The proposed methodology comp...
Article
This study aims to introduce a hand-crafted machine learning method to classify ischemic and hemorrhagic strokes with satisfactory performance. In the first step of this work, a new CT brain for images dataset was collected for stroke patients. A highly accurate hand-crafted machine learning method is developed and tested for these cases. This mode...
Preprint
Full-text available
Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This...
Article
Full-text available
Objectives: Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties while acquiring diagnostic images. We aimed to develop an efficient automated US-based fetal sex cla...
Article
Full-text available
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machi...
Article
Nowadays, fires have been commonly seen worldwide and especially forest fires are big disasters for humanity. The prime objective of this work is to develop an accurate fire warning model by using images. In this work, two new deep feature engineering models are proposed to detect the fire accurately using images. To create deep features, residual...
Article
Background and purpose Valvular heart disease (VHD) is an important cause of morbidity and mortality. Echocardiography is the reference standard for VHD diagnosis but is not universally accessible. Manual cardiac auscultation is inadequate for screening VHD. Many machine learning models using heart sounds acquired with an electronic stethoscope may...
Article
Full-text available
Sleep contributes to more than a third of a person's life, making sleep monitoring essential for overall well-being. Cyclic alternating patterns (CAP) are crucial in monitoring sleep quality and associated illnesses such as insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, etc. However, traditionally medical specialists practice manual...
Article
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and an...
Article
Full-text available
Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment....
Article
The automatic and accurate analysis of medical images (e.g., segmentation, detection, classification) prerequisites for modern disease diagnosis and prognosis. Computer-aided diagnosis (CAD) systems empower accurate and effective detection of various diseases and timely treatment decisions. The past decade witnessed a spur in deep learning (DL)-bas...
Article
Full-text available
Myocardial infarct (MI) accounts for a high number of deaths globally. In acute MI, accurate electrocardiography (ECG) is important for timely diagnosis and intervention in the emergency setting. Machine learning is increasingly being explored for automated computer-aided ECG diagnosis of cardiovascular diseases. In this study, we have developed De...
Article
Full-text available
The fetal echocardiogram is useful for monitoring and diagnosing cardiovascular diseases in the fetus in utero. Importantly, it can be used for assessing prenatal congenital heart disease, for which timely intervention can improve the unborn child’s outcomes. In this regard, artificial intelligence (AI) can be used for the automatic analysis of fet...
Article
Full-text available
Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms. However, the h...
Article
Full-text available
Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical emergency issues that needs immediate treatment. Large numbers of noncontrast-computed tomography (NCCT) brain images are analyzed manually by radiologists to diagnose the hemorr...
Data
In this study, photographs of mask (name start 1), no mask (name start 2), and improper mask (name start 3) were collected by researchers via internet search. The discovered photos were combined with 4072 photos that were uploaded to the Kaggle website by Larxel (https://www.kaggle.com/andrewmvd/face-mask-detection). A face detection application wa...
Article
Full-text available
Hypertrophic cardiomyopathy (HCM) is a genetic disorder that exhibits a wide spectrum of clinical presentations, including sudden death. Early diagnosis and intervention may avert the latter. Left ventricular hypertrophy on heart imaging is an important diagnostic criterion for HCM, and the most common imaging modality is heart ultrasound (US). The...
Article
Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directe...
Article
Full-text available
Since the emergence of COVID-19, there has been an exponential surge in the number of casualties which increases the demand for numerous research works that can successfully detect the disease accurately in the early stage. This study provides some methods based on deep learning for the diagnosis of patients suffering from COVID disease, healthy co...
Article
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Early identification of coronary artery disease (CAD) can facilitate timely clinical intervention and save lives. This study aims to develop a machine learning framework that uses tensor analysis on heart rate (HR) signals to automate the CAD detection task. A third-order tensor representing a time-frequency relationship is constructed by fusing sc...
Article
Full-text available
Background and purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented u...