Siti Nurmaini
Sriwijaya University
Deep learningEngineeringControl engineeringPosition (vector)Artificial intelligenceSwarm roboticsPattern recognitionSwarm behaviourMobile robotMobile robot navigationComputer visionComputer scienceArtificial neural networkFeature extractionFuzzy logicControl theoryMedicineReal-time computingControl theoryParticle swarm optimizationSegmentationRobot
168Publications
12H-index
359Citations
Publications 184
Newest
The accuracy of electrocardiogram (ECG) delineation can affect the precise diagnose for cardiac disorders interpretation. Some nonideal ECG presentation can make a false decision in precision medicine. Besides, the physiological variation of heart rate and different characteristics of the different ECG waves in terms of shape, frequency, amplitude, and duration is also affected. This paper proposes a Discrete Wavelet Transform (DWT), non-stationary signal analysis for noise removal, and onset-of...
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Atrial fibrillation is a disturbance in the function of the heart's electrical system which is characterized by an irregular heartbeat. Conventional detection of AF is often diagnosed through data visualization using an electrocardiograph by cardiologists with the results of the evaluation in the form of a recorded electrocardiogram (ECG) wave. The method used in this research is Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) architecture. The RNN method is very suitable for proce...
#2Rido Mulawarman (Sriwijaya University)H-Index: 2
Last. Irfannuddin Irfannuddin (Sriwijaya University)H-Index: 2
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INTRODUCTION Coronavirus disease 2019 (COVID-19) has been associated with cardiac arrhythmias. Several electrocardiographic markers have been used to predict the risk of arrhythmia in patients with COVID-19. We aim to investigate the electrocardiographic (ECG) ventricular repolarization indices in patients with COVID-19. METHODOLOGY We performed a comprehensive systematic literature search from PubMed, EuropePMC, SCOPUS, Cochrane Central Database, and Google Scholar Preprint Servers. The primary...
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Artificial intelligence (AI) technologies continue to play significant roles during the Coronavirus 2019 (COVID-19) pandemic in the world. However, health is an area where the rules are stringent and inflexible. This can be justified because it deals with human life. Nevertheless, at the same time, a large number of tests, certifications, and panels will lead to innovations in AI for healthcare that are longer, more complex, and difficult to incorporate into real-world applications. Indonesia ha...
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#1Bambang TutukoH-Index: 7
#2Rossi PassarellaH-Index: 5
Last. Siti NurmainiH-Index: 12
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The prevalent type of arrhythmia associated with an increased risk of stroke and mortality is atrial fibrillation (AF). It is a known priority to identify AF before the first complication occurs. No previous studies have explored the feasibility of conducting AF screening using a deep learning (DL) algorithm (integrated cloud-computing) telehealth surveillance system. Hence, we address this problem. The goal of this research was to determine the feasibility of AF screening using an embedded clou...
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The heart signal is taken from the electric current that is generated or called an electrocardiogram. This electrocardiogram is useful for doctors to find out heart defects in patients. One of the heart defects is myocardial Infarction. Myocardial Infarction has symptoms such as fatigue, chest tightness and even death. Myocardial infarction is caused because blood flow to the heart does not flow so that the heart stops beating. ST-elevation is one of the causes of Myocardial Infarction. ST-Eleva...
#1Jannes EffendiH-Index: 1
#2Siti NurmainiH-Index: 12
Electrocardiogram (ECG) is electrical records that contains information about human heart. In the medical field, humans heart condition can be diagnosed by analyzing the changes in hearts beat or rhythm that contain p wave, QRSComplex and T wave. Delineation can be very hard for doctor to do because of human errors. Because of that, automation of ECG delineation by using deep learning is preferred. The deep learnings methodology used in this study is Recurrent Neural Network(RNN) with Long Short...
#1Siti NurmainiH-Index: 12
Last. Bambang TutukoH-Index: 7
view all 7 authors...
Abstract Analysis of electrocardiogram (ECG) signals is challenging due to the complexity of their signal morphology. Any irregularity in a cardiac rhythm can change the ECG waveform. A reliable machine learning model is developed here to provide substantial input to cardiologists and help confirm their diagnoses. To achieve high diagnostic accuracy, nearly all ECG analytics tools require records of the positions and morphologies of various segments of P-waves, QRS complexes, and T-waves to be k...
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#1Ria Nova (Sriwijaya University)H-Index: 1
#2Siti Nurmaini (Sriwijaya University)H-Index: 12
Last. Sukman Tulus Putra (UI: University of Indonesia)H-Index: 4
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Abstract Echocardiogram examination is important for diagnosing cardiac septal defects. With the development of AI-based technology, an echocardiogram examination previously performed manually by cardiologists can be done automatically. Automatic segmentation of cardiac septal defects can help a physician to make an initial diagnosis before referring a pediatric cardiologist for further treatment. In previous studies, automatic object segmentation using convolutional neural networks (CNNs) was o...
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#1Ade Iriani SapitriH-Index: 2
#2Siti NurmainiH-Index: 12
Last. Sukemi SukemiH-Index: 5
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Congenital heart disease is a common disease that can be life-threatening. CHD has an important role in knowing the early diagnosis of the heart, especially the fetus. Medical image analysis is one of the topics that can support the diagnosis process, especially the occurrence of septal defects. The image analysis process can be done by segmenting, detecting, and classifying it. This is the main key in carrying out the analysis process in diagnosing diseases of defects. Convolutional neural netw...
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