A novel Bayesian deep learning methodology for enhanced foetal cardiac signal mining

Jagannath, D. J.; Dolly, D. Raveena Judie; Peter, J. Dinesh

doi:10.1080/0952813x.2018.1544283

Cited by 7 publications

(7 citation statements)

References 11 publications

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“…ese functions of data mining are all intrinsically related, they are interrelated and affect each other, and they cooperate with each other and play a role together in the process of data mining [10].…”

Section: Comparison and Analysis Methods Ofmentioning

confidence: 99%

A Cluster Analysis Model for PhD Dissertation Quality Based on the Depth Algorithm

Zheng¹

2022

Security and Communication Networks

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Doctoral education is an important part of higher education and plays an important role in cultivating many higher talents for the country. In doctoral education, thesis writing is an indispensable teaching section. The quality of the doctoral thesis will directly affect the graduation of doctoral students. Therefore, the analysis of the quality of the doctoral thesis is a typical aspect of the evaluation of doctoral education work. This paper aims to study the quality clustering analysis of the doctoral dissertation based on the depth algorithm. This paper establishes a doctoral paper quality cluster analysis model based on the depth algorithm and carries out the doctoral paper quality cluster analysis experiment based on this model. After the experiment, the main factors affecting the quality of doctoral dissertation were also analyzed. The conclusion is the following: The accuracy rate of the doctoral paper quality cluster analysis model based on the depth algorithm has reached 88.5%.

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Section: Comparison and Analysis Methods Ofmentioning

confidence: 99%

A Cluster Analysis Model for PhD Dissertation Quality Based on the Depth Algorithm

Zheng¹

2022

Security and Communication Networks

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“…This is compounded by other disturbances such as power line noise, maternal muscle, respiration activity, fetal movement, and background noise. 17 …”

Section: Introductionmentioning

confidence: 99%

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis

et al. 2022

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Fetal electrocardiogram (ECG) waveform analysis along with cardiac time intervals (CTIs) measurements are critical for the management of high-risk pregnancies. Currently, there is no system that can consistently and accurately measure fetal ECG. In this work, we present a new automatic approach to attenuate the maternal ECG in the frequency domain and enhance it with measurable CTIs. First, the coherent components between the maternal ECG and abdominal ECG were identified and subtracted from the latter in the frequency domain. The residual was then converted into the time domain using the inverse Fourier transform to yield the fetal ECG. This process was improved by averaging multiple beats. Two fetal cardiologists, blinded to the method, assessed the quality of fetal ECG based on a grading system and measured the CTIs. We evaluated the fetal ECG quality of our method and time-based methods using one synthetic dataset, one human dataset available in the public domain, and 37 clinical datasets. Among the 37 datasets analyzed, the mean average (± standard deviation) grade was 3.49 ± 1.22 for our method vs. 2.64 ± 1.26 for adaptive interference cancellation (p-value < 0.001), thus showing the frequency-based fetal ECG extraction was the superior method, as assessed from our clinicians’ perspectives. This method has the potential for use in clinical settings.

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“…The paper of Hua et al [94] is one of only a few papers that used different types of prior distributions in BDL. Jagannath et al [95] also used BDL to extract the fetal cardiac signals. The fetal ECG can be detected in an advanced stage of fetal growth.…”

Section: B Medical Signal Processingmentioning

confidence: 99%

“…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. Jagannath et al [95] deployed BDL to extract the fetal cardiac signals using ECG. The authors tested the proposed method on two datasets, namely Physionet and DaISy.…”

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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A novel Bayesian deep learning methodology for enhanced foetal cardiac signal mining

Cited by 7 publications

References 11 publications

A Cluster Analysis Model for PhD Dissertation Quality Based on the Depth Algorithm

A Cluster Analysis Model for PhD Dissertation Quality Based on the Depth Algorithm

Frequency-Based Maternal Electrocardiogram Attenuation for Fetal Electrocardiogram Analysis

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

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