Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Aseeri, Ahmad O.

doi:10.3390/computers10060082

Cited by 16 publications

(12 citation statements)

References 44 publications

(49 reference statements)

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“…ECG is used to test the activities of the heart through an electrical signal that is produced by heartbeats and recorded by sensors attached to the skin. For this purpose, Aseeri et al [93] used BDL to classify the arrhythmias ''abnormal rhythm of heart beats'' through ECG signals. The proposed method estimates the uncertainty associated with the predicted output.…”

Section: B Medical Signal Processingmentioning

confidence: 99%

“…The designed method used MC-dropout for sampling, and it was assessed on MICCAI 2017 dataset, in which 100 cases were used; 75 during training and 25 for testing. Aseeri et al [93] also used BDL to classify cardiac arrhythmias based on ECG signals. The suggested method used MC-dropout for sampling, and the model performance was tested on three datasets (MIT-BIH for 48 patients, St Petersburg INCART with 75 records for 34 patients, BIDMC dataset for 15 patients), which achieved an F1-score of 98.8%, 99.2%, and 97.2%, respectively.…”

Section: B Cardiovascular Diseasementioning

confidence: 99%

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A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

2022

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In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing. DL models have also been intensely used in different tasks of healthcare such as disease diagnostics and treatments. Deep learning techniques have surpassed other machine learning algorithms and proved to be the ultimate tools for many state-of-the-art applications. Despite all that success, classical deep learning has limitations and their models tend to be very confident about their predicted decisions because it does not know when it makes mistake. For the healthcare field, this limitation can have a negative impact on models predictions since almost all decisions regarding patients and diseases are sensitive. Therefore, Bayesian deep learning (BDL) has been developed to overcome these limitations. Unlike classical DL, BDL uses probability distributions for the model parameters, which makes it possible to estimate the whole uncertainties associated with the predicted outputs. In this regard, BDL offers a rigorous framework to quantify all sources of uncertainties in the model. This study reviews popular techniques of using Bayesian deep learning with their benefits and limitations. It also reviewed recent deep learning architecture such as Convolutional Neural Networks and Recurrent Neural Networks. In particular, the applications of Bayesian deep learning in healthcare have been discussed such as its use in medical imaging tasks, clinical signal processing, medical natural language processing, and electronic health records. Furthermore, this paper has covered the deployment of Bayesian deep learning for some of the widespread diseases. This paper has also discussed the fundamental research challenges and highlighted some research gaps in both the Bayesian deep learning and healthcare perspective.

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Section: B Medical Signal Processingmentioning

confidence: 99%

Section: B Cardiovascular Diseasementioning

confidence: 99%

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

2022

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“…RHM systems are implemented to examine and monitor patients' conditions remotely benefiting patients, hospital staff, and resources [62]. e main aim of such systems is to provide ANN + CNN + LSTM Walking behavior detection 96% [33] CNN + CAE + DAE Fall detection 99.9% [34] Faster RCNN Remote healthcare system Faster RCNN outperformed fast RCNN and RCNN [35] Deep ensemble learning Cardiovascular disease detection 98.62% [36] MobileNet Skin cancer detection 91.25% [37] Deep CNN Skin carcinoma classification 93.16% [38] Capsule network Brain tumor classification 86.56% [39] Pretrained CNN models Breast cancer detection and classification 98.96% [40] CNN + DarkNet-53 Breast cancer classification 99.1% the best healthcare services to rural area patients. Even though this system provides diverse services some challenges that need to be overcome are data privacy and data storage which can be solved by introducing cloud-based IoT healthcare systems.…”

Section: 3mentioning

confidence: 99%

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

Raoof

Durai

2022

Contrast Media & Molecular Imaging

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Growth and advancement of the Deep Learning (DL) and the Internet of Things (IoT) are figuring out their way over the modern contemporary world through integrating various technologies in distinct fields viz, agriculture, manufacturing, energy, transportation, supply chains, cities, healthcare, and so on. Researchers had identified the feasibility of integrating deep learning, cloud, and IoT to enhance the overall automation, where IoT may prolong its application area through utilizing cloud services and the cloud can even prolong its applications through data acquired by IoT devices like sensors and deep learning for disease detection and diagnosis. This study explains a summary of various techniques utilized in smart healthcare, i.e., deep learning, cloud-based-IoT applications in smart healthcare, fog computing in smart healthcare, and challenges and issues faced by smart healthcare and it presents a wider scope as it is not intended for a particular application such aspatient monitoring, disease detection, and diagnosing and the technologies used for developing this smart systems are outlined. Smart health bestows the quality of life. Convenient and comfortable living is made possible by the services provided by smart healthcare systems (SHSs). Since healthcare is a massive area with enormous data and a broad spectrum of diseases associated with different organs, immense research can be done to overcome the drawbacks of traditional healthcare methods. Deep learning with IoT can effectively be applied in the healthcare sector to automate the diagnosing and treatment process even in rural areas remotely. Applications may include disease prevention and diagnosis, fitness and patient monitoring, food monitoring, mobile health, telemedicine, emergency systems, assisted living, self-management of chronic diseases, and so on.

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“…They have been widely deployed in various artificial intelligence (AI) applications such as in object detection [1] or scene segmentation [2]. However, standard NNs are incapable of quantifying their uncertainty [3], so they are unsuitable for safety-critical applications such as those in autonomous driving, medicine or chemistry [1], [4], [5], [6], [7]. For instance, physicians or selfdriving systems can be deceived by a standard NN which does not quantify the level of uncertainty in its output.…”

Section: Introductionmentioning

confidence: 99%

“…However, given random noise input, the standard NN is completely overconfident and wrong, while the BayesNN can make use of its uncertainty estimation capability to lower its confidence. Hence BayesNNs, along with their robustness to overfitting [10], have become popular in applications where uncertainty quantification is essential [1], [5], [6], [7], [11].…”

Section: Introductionmentioning

confidence: 99%

Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations

Fan¹,

Ferianc²,

Que³

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Bayesian neural networks (BayesNNs) have demonstrated their advantages in various safety-critical applications, such as autonomous driving or healthcare, due to their ability to capture and represent model uncertainty. However, standard BayesNNs require to be repeatedly run because of Monte Carlo sampling to quantify their uncertainty, which puts a burden on their real-world hardware performance. To address this performance issue, this paper systematically exploits the extensive structured sparsity and redundant computation in BayesNNs. Different from the unstructured or structured sparsity in standard convolutional NNs, the structured sparsity of BayesNNs is introduced by Monte Carlo Dropout and its associated sampling required during uncertainty estimation and prediction, which can be exploited through both algorithmic and hardware optimizations. We first classify the observed sparsity patterns into three categories: channel sparsity, layer sparsity and sample sparsity. On the algorithmic side, a framework is proposed to automatically explore these three sparsity categories without sacrificing algorithmic performance. We demonstrated that structured sparsity can be exploited to accelerate CPU designs by up to 49 times, and GPU designs by up to 40 times. On the hardware side, a novel hardware architecture is proposed to accelerate BayesNNs, which achieves a high hardware performance using the runtime adaptable hardware engines and the intelligent skipping support. Upon implementing the proposed hardware design on an FPGA, our experiments demonstrated that the algorithm-optimized BayesNNs can achieve up to 56 times speedup when compared with unoptimized Bayesian nets. Comparing with the optimized GPU implementation, our FPGA design achieved up to 7.6 times speedup and up to 39.3 times higher energy efficiency.

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Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Cited by 16 publications

References 44 publications

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

Accelerating Bayesian Neural Networks via Algorithmic and Hardware Optimizations

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