Cardiac arrhythmia detection using deep learning: A review

Parvaneh, Saman; Rubin, Jonathan; Babaeizadeh, Saeed; Xu-Wilson, Minnan

doi:10.1016/j.jelectrocard.2019.08.004

Cited by 109 publications

(52 citation statements)

References 8 publications

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“…Deep learning models, one of the artificial intelligence techniques, are used successfully in the biomedical field. Diagnosis of cardiac arrhythmia [13] , [14] , brain injuries [15] , [16] , lung segmentation [17] , [18] , breast cancer [19] , [20] , skin cancer [21] , [22] , epilepsy [23] , [40] , [72] , and pneumonia [24] , [25] , [26] , [27] , [28] , [29] with deep learning models has increased the popularity of these algorithms in biomedical field.…”

Section: Introductionmentioning

confidence: 99%

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman

Alakuş

Türkoğlu

2020

Chaos, Solitons & Fractals

290

183

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Highlights In this study, unlike CNN architectures, COVID-19 was determined from chest X-ray images with a smaller number of layers. More COVID-19, pneumonia, and no-findings images were used than in previous studies. This increases the reliability of the system more. As is known, reducing the size of the image may cause some information in the image to be lost. Given these facts, good classification accuracy has been achieved with capsule networks, even the image size has been reduced to 128 × 128 pixels.

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Section: Introductionmentioning

confidence: 99%

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Toraman

Alakuş

Türkoğlu

2020

Chaos, Solitons & Fractals

290

183

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“…Traditional approaches for cardiac abnormality classification often 1) apply signal processing techniques on the raw ECG data; 2) extract handcrafted features using domain knowledge to construct a feature vector; and 3) apply offthe-shelf machine learning models to classify the feature vector into different abnormality classes. Recently deep learning approaches have been applied to this task and achieve superior performance [3][4][5][6]. Compared to traditional approaches, deep learning-based approaches can automatically learn informative feature representations that are predictive of cardiac abnormalities in an end-to-end manner.…”

Section: Introductionmentioning

confidence: 99%

A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification

Natarajan

Chang

Mariani

et al. 2020

Computing in Cardiology Conference (CinC)

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Cardiac abnormalities are a leading cause of death and their early diagnosis are of importance for providing timely interventions. The goal of 2020 PhysioNet/CinC challenge was to develop algorithms to diagnose multiple cardiac abnormalities using 12-lead ECG data. In this work, we develop a wide and deep transformer neural network to classify each 12-lead ECG sequence into 27 cardiac abnormality classes. Our approach combines handcrafted ECG features, which were determined to be important by a random forest model, and discriminative feature representations that are automatically learned from a transformer neural network. Our entry to the 2020 Phys-ioNet/CinC challenge placed 1 st out of 41 official ranking teams (team name = prna). Using the official generalized weighted accuracy metric for evaluation, we achieved a validation score of 0.587 and top score of 0.533 on the full held-out test set.

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“…[59][60][61] Classical AI approaches consist of training an algorithm on population data to identify statistical pat terns that can be then used to diagnose and treat an indi vidual based on his or her specific demographics and a specific disease history. 62 Among other examples, those approaches have demonstrated success in classifying elec trocardiography signals toward the detection of arrythmia 63 and radiology images toward correct diagnosis. 64 Populationbased AI approaches can further reveal the most impactful features affecting selected clinical outcomes based on the identified statistical patterns.…”

Section: Introductionmentioning

confidence: 99%

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

2020

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The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials’ results provide the basis for guidance and support for official protocols that clinicians use to base their decisions upon. However, individuals rarely demonstrate the reported response from relevant clinical trials, often the average from a group representing a population or subpopulation. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual’s response to the treatment varies over time with the changes in his or her condition, whether via the indication or physiology. In practice, the drug and the dose selection depend greatly on the medical protocol of the healthcare provider and the medical team’s experience. As such, the results are inherently varied and often suboptimal. Big data approaches have emerged as an excellent decision-making support tool, but their application is limited by multiple challenges, the main one being the availability of sufficiently big datasets with good quality, representative information. An alternative approach—phenotypic personalized medicine (PPM)—finds an appropriate drug combination (quadratic phenotypic optimization platform [QPOP]) and an appropriate dosing strategy over time (CURATE.AI) based on small data collected exclusively from the treated individual. PPM-based approaches have demonstrated superior results over the current standard of care. The side effects are limited while the desired output is maximized, which directly translates into improving the length and quality of individuals’ lives.

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Cardiac arrhythmia detection using deep learning: A review

Cited by 109 publications

References 8 publications

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks

A Wide and Deep Transformer Neural Network for 12-Lead ECG Classification

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

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