A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal

Kucharski, Dariusz; Grochala, Dominik; Kajor, Marcin; Kańtoch, Eliasz

doi:10.1007/978-3-319-67220-5_1

Cited by 16 publications

(9 citation statements)

References 9 publications

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“…Based on the experimental results, our proposed method gives better results compared with other classifiers (ANN and SVM) (conducted on PSCAL CHSC dataset). From the results in Table (2) and Fig, (11), it was observed that the proposed model achieves better Ac, Se and Spas compared to various models reported by other researchers [50][51][52][53][54][55][56]. Evaluation of results demonstrates the potentiality of the proposed PCG acoustic signal classification method for automatically differentiating between pathological and normal PCG signal.…”

Section: Discussionmentioning

confidence: 68%

An Efficient Computational Approach for Phonocardiogram Signals Analysis and Normal/Abnormal heart sounds diagnosis

El‐Dahshan

Ali

Yahiea

2020

Arab Journal of Nuclear Sciences and Applications

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In the present work, the authors proposed of cardiac sound signals (ANN) is executed as indicator and classifier of PCG abnormalities using the features extracted from PCG acoustic signals via the discrete wavelet transform (DWT). To develop and validate the proposed approach, the PASCAL CHSC 2011 dataset was utilized. The assess the efficiency of the proposed intelligent approach. The results demonstrate that the approach achieves a high performance compared to other classificat results showed an overall accuracy with the recently published ones that achieved utilizing different machine learning (ML) approaches. The achieved results showed that the PCG acoustic signals. cardiovascular abnormalities.

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Section: Discussionmentioning

confidence: 68%

An Efficient Computational Approach for Phonocardiogram Signals Analysis and Normal/Abnormal heart sounds diagnosis

El‐Dahshan

Ali

Yahiea

2020

Arab Journal of Nuclear Sciences and Applications

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“…They achieved an overall score of 83.99% placing eighth at PHY16 challenge. Kucharski et al [109] used an eight second spectrogram on the segments, before feeding them to a five layer CNN with dropout. Their method achieved 99.1% sensitivity and 91.6% specificity which are comparable to state-of-the-art methods on the task.…”

Section: B Phonocardiogram With Physionet 2016 Challengementioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

147

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The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

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“…Several studies show the potential for computer-assisted phonocardiography to correctly analyse and classify heart murmurs for CHD screening. [39][40][41] Software called Automated Auscultation Diagnosis Devices and Computer-aided Auscultation use multilayer perceptron neural networks to evaluate discriminative energy frequencies particular to cardiac lesions and distinguish between innocent and pathological murmurs. 42 Authors argue that phonocardiography technology can reduce time to diagnosis, triage patients that need immediate care and assist paediatricians in rural communities without access to paediatric cardiologists.…”

Section: Biomedical Diagnosticsmentioning

confidence: 99%

A primer on artificial intelligence for the paediatric cardiologist

2020

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AbstractThe combination of pediatric cardiology being both a perceptual and a cognitive subspecialty demands a complex decision-making model which makes artificial intelligence a particularly attractive technology with great potential. The prototypical artificial intelligence system would autonomously impute patient data into a collaborative database that stores, syncs, interprets and ultimately classifies the patient’s profile to specific disease phenotypes to compare against a large aggregate of shared peer health data and outcomes, the current medical body of literature and ongoing trials to offer morbidity and mortality prediction, drug therapy options targeted to each patient’s genetic profile, tailored surgical plans and recommendations for timing of sequential imaging. The focus of this review paper is to offer a primer on artificial intelligence and paediatric cardiology by briefly discussing the history of artificial intelligence in medicine, modern and future applications in adult and paediatric cardiology across selected concentrations, and current barriers to implementation of these technologies.

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A Deep Learning Approach for Valve Defect Recognition in Heart Acoustic Signal

Cited by 16 publications

References 9 publications

An Efficient Computational Approach for Phonocardiogram Signals Analysis and Normal/Abnormal heart sounds diagnosis

An Efficient Computational Approach for Phonocardiogram Signals Analysis and Normal/Abnormal heart sounds diagnosis

Deep Learning in Cardiology

A primer on artificial intelligence for the paediatric cardiologist

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