Convolutional Neural Networks for Electrocardiogram Classification

Rahhal, Mohamad Mahmoud Al; Bazi, Yakoub; Othman, Esam; Benjdira, Bilel

doi:10.1007/s40846-018-0389-7

Cited by 99 publications

(64 citation statements)

References 51 publications

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“…Up-to-date research has uncovered findings on the many applications of deep learning in biosignal event detection, specifically in the field of cardiology [13]. For example, Yildirim et al proposed an algorithm for arrhythmia detection [14].…”

Section: Introductionmentioning

confidence: 99%

Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

Klempíř

Krupička

Bakštein

et al. 2019

Measurement Science Review

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Deep brain stimulation (DBS) is an internationally accepted form of treatment option for selected patients with Parkinson’s disease and dystonia. Intraoperative extracellular microelectrode recordings (MER) are considered as the standard electrophysiological method for the precise positioning of the DBS electrode into the target brain structure. Pre-processing of MERs is a key phase in clinical analysis, with intraoperative microelectrode recordings being prone to several artifact groups (up to 25 %). The aim of this methodological article is to provide a convolutional neural network (CNN) processing pipeline for the detection of artifacts in an MER. We applied continuous wavelet transform (CWT) to generate an over-complete time–frequency representation. We demonstrated that when attempting to find artifacts in an MER, the new CNN + CWT provides a high level of accuracy (ACC = 88.1 %), identifies individual classes of artifacts (ACC = 75.3 %) and also offers artifact time onset detail, which can lead to a reduction in false positives/negatives. In summary, the presented methodology is capable of identifying and removing various artifacts in a comprehensive database of MER and represents a substantial improvement over the existing methodology. We believe that this approach will assist in the proposal of interesting clinical hypotheses and will have neurologically relevant effects.

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Section: Introductionmentioning

confidence: 99%

Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

Klempíř

Krupička

Bakštein

et al. 2019

Measurement Science Review

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“…The proposed hybrid model is found to effectively predict and classify liver disease. Al Rahhal et al [7] applied convolutional neural networks (CNN) to handle very large labeled images using a deep learning approach to accurately detect ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). Lu et al [8] propose a modification confidence measure algorithm to improve the accuracy of bio-image denoising processes, showing significant performance improvements especially in sliding window processes from the top-left to the bottom-right of bio-images.…”

Section: Fewer Research Questions Diverse Fieldsmentioning

confidence: 99%

Big Data and Situation-Aware Technology for Smarter Healthcare

2018

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“…Since 2012 [9] deep learning based approaches have shown an attractive efficiency in object segmentation for multiple types of imaging (RGB imaging, aerial imaging, multi-spectral imaging etc.) [10][11][12][13][14]. This was a source of inspiration for the medical imaging community to move their interest towards a great adoption of these approaches for different medical imaging modalities (MRI, CT Scan, 2D Ultrasound, 3D Ultrasound etc.)…”

Section: Introductionmentioning

confidence: 99%

Spinal Cord Segmentation in Ultrasound Medical Imagery

et al. 2020

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In this paper, we study and evaluate the task of semantic segmentation of the spinal cord in ultrasound medical imagery. This task is useful for neurosurgeons to analyze the spinal cord movement during and after the laminectomy surgical operation. Laminectomy is performed on patients that suffer from an abnormal pressure made on the spinal cord. The surgeon operates by cutting the bones of the laminae and the intervening ligaments to relieve this pressure. During the surgery, ultrasound waves can pass through the laminectomy area to give real-time exploitable images of the spinal cord. The surgeon uses them to confirm spinal cord decompression or, occasionally, to assess a tumor adjacent to the spinal cord. The Freely pulsating spinal cord is a sign of adequate decompression. To evaluate the semantic segmentation approaches chosen in this study, we constructed two datasets using images collected from 10 different patients performing the laminectomy surgery. We found that the best solution for this task is Fully Convolutional DenseNets if the spinal cord is already in the train set. If the spinal cord does not exist in the train set, U-Net is the best. We also studied the effect of integrating inside both models some deep learning components like Atrous Spatial Pyramid Pooling (ASPP) and Depthwise Separable Convolution (DSC). We added a post-processing step and detailed the configurations to set for both models.

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Convolutional Neural Networks for Electrocardiogram Classification

Cited by 99 publications

References 51 publications

Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

Identification of Microrecording Artifacts with Wavelet Analysis and Convolutional Neural Network: An Image Recognition Approach

Big Data and Situation-Aware Technology for Smarter Healthcare

Spinal Cord Segmentation in Ultrasound Medical Imagery

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