ORCID Profile
0000-0002-1585-5832
Current Organisations
University of Tokyo
,
Chittagong Independent University
,
Chittagong University of Engineering and Technology
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Publisher: Hindawi Limited
Date: 29-09-2022
DOI: 10.1155/2022/9475162
Abstract: Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five in iduals of supraventricular MIT-BIH arrhythmia and five in iduals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.
Publisher: Wiley
Date: 26-11-2012
DOI: 10.1111/TPJ.12028
Abstract: The galactolipids monogalactosyldiacylglycerol (MGDG) and digalactosyldiacylglycerol (DGDG) are the predominant lipids in thylakoid membranes and indispensable for photosynthesis. Among the three isoforms that catalyze MGDG synthesis in Arabidopsis thaliana, MGD1 is responsible for most galactolipid synthesis in chloroplasts, whereas MGD2 and MGD3 are required for DGDG accumulation during phosphate (Pi) starvation. A null mutant of Arabidopsis MGD1 (mgd1-2), which lacks both galactolipids and shows a severe defect in chloroplast biogenesis under nutrient-sufficient conditions, accumulated large amounts of DGDG, with a strong induction of MGD2/3 expression, during Pi starvation. In plastids of Pi-starved mgd1-2 leaves, biogenesis of thylakoid-like internal membranes, occasionally associated with invagination of the inner envelope, was observed, together with chlorophyll accumulation. Moreover, the mutant accumulated photosynthetic membrane proteins upon Pi starvation, indicating a compensation for MGD1 deficiency by Pi stress-induced galactolipid biosynthesis. However, photosynthetic activity in the mutant was still abolished, and light-harvesting hotosystem core complexes were improperly formed, suggesting a requirement for MGDG for proper assembly of these complexes. During Pi starvation, distribution of plastid nucleoids changed concomitantly with internal membrane biogenesis in the mgd1-2 mutant. Moreover, the reduced expression of nuclear- and plastid-encoded photosynthetic genes observed in the mgd1-2 mutant under Pi-sufficient conditions was restored after Pi starvation. In contrast, Pi starvation had no such positive effects in mutants lacking chlorophyll biosynthesis. These observations demonstrate that galactolipid biosynthesis and subsequent membrane biogenesis inside the plastid strongly influence nucleoid distribution and the expression of both plastid- and nuclear-encoded photosynthetic genes, independently of photosynthesis.
Publisher: Oxford University Press (OUP)
Date: 03-2012
Publisher: Hindawi Limited
Date: 12-04-2022
DOI: 10.1155/2022/3408501
Abstract: Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random s ler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.
Publisher: Oxford University Press (OUP)
Date: 13-02-2017
DOI: 10.1104/PP.16.01368
Location: United Kingdom of Great Britain and Northern Ireland
Location: United States of America
Location: Bangladesh
No related grants have been discovered for Tatsuru Masuda.