Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks

Jin, Yanrui; Qin, Chengjin; Huang, Yongfeng; Zhao, Wenyi; Liu, Chengliang

doi:10.1016/j.knosys.2019.105460

Cited by 94 publications

(43 citation statements)

References 45 publications

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“…Faced with an existential threat, humanity must put its best feet forward with any and every solution to support detection, diagnosis, and treatment of COVID-19. Meanwhile, machine and deep learning techniques have proven potent in numerous medical techniques [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ], and epidemiological techniques [ 24 , 25 ]. This study supports engineering and computational science efforts in our collective battle to defeat the COVID-19 scourge.…”

Section: Data Augmentation and Efficient Learning By Dlmsmentioning

confidence: 99%

“…For their part, DLMs, such as CNNs [ 17 , 18 , 19 ] and ConvLSTM [ 20 , 21 , 22 ], have been widely applied in several medical fields as improvements to ML techniques. While ML techniques and DLMs may seem instinctive candidates in our present crusade to annihilate COVID-19, the absence of reliable data to exploit the “learnability” inherent to DLMs makes them palpable choices.…”

Section: Introductionmentioning

confidence: 99%

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Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Sedik

Iliyasu

El-Rahiem

et al. 2020

Viruses

172

125

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This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

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Section: Data Augmentation and Efficient Learning By Dlmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Sedik

Iliyasu

El-Rahiem

et al. 2020

Viruses

172

125

View full text Add to dashboard Cite

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“…Common examples include recurrent neural networks and convolutional neural networks. Examples of deep learning in diagnostics are the classification of dermatological diseases [66] and atrial fibrillation detection [67]. The development of supervised, unsupervised, and deep learning algorithms presupposes that the available data can be split into training and test sets [33,36].…”

Section: Related Workmentioning

confidence: 99%

“…It is known as the leading cause of death and disability in the world, with more than 17 million deaths per year [135]. Specific examples are acute myocardial infarction [116], coronary artery disease [136], or atrial fibrillation [67]…”

Section: Organic System Descriptionmentioning

confidence: 99%

“…However, in the context of this study, these play an insignificant role, which is why no detailed description is provided at this point large dataset (N > 1,000), and we found only a subset of nine publications which considered large samples. For example, [67] analyzed atrial fibrillation using a convolutional neural network based on 150,060 samples. In contrast, [89] examined 53 patients to diagnose osteoporosis.…”

Section: Pulmonarymentioning

confidence: 99%

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Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

2021

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The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.

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Detection of atrial fibrillation using variable length genetic algorithm and convolutional neural network

Qaraghuli

Sheibani

Tabatabaee

2021

Concurrency and Computation

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Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and it is considered as one of the most important risk factor for death, stroke, hospitalization, and heart failure. It is possible to detect AF by analyzing electrocardiogram (ECG) of patients. To work on clean signals and reduce errors resulted from noise, we have used Butterworth filter. The short-term Fourier transform was used to analyze ECG segments to obtain ECG spectrogram images. Convolutional neural network (CNN) models have been proposed for improving automatic detection of AF. The number of convolutional layers varies in different CNN models, and as the model become deeper, more hyper parameters are added. So in this article, variable length genetic algorithm was used in order to optimize hyper parameters of CNN. The results of experiments that performed onthe MIT-BIH AF database showed that the proposed method achieved 100%, 98.90%, and 99.95% for the sensitivity, specificity, and accuracy, respectively, so the proposed method outperforms the deep CNNs. Hence, the proposed method is an accurate and efficient method for detection of AF.

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Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks

Cited by 94 publications

References 45 publications

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction

Detection of atrial fibrillation using variable length genetic algorithm and convolutional neural network

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