Patient-specific ECG classification by deeper CNN from generic to dedicated

Li, Yazhao; Pang, Yanwei; Wang, Jian; Li, Xuelong

doi:10.1016/j.neucom.2018.06.068

Cited by 110 publications

(43 citation statements)

References 35 publications

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“…[38] Furthermore, a pre-trained model with weights from ImageNet was used. [38] In this work, the learning rate is set to 0.0001 and the value of dropout is 0.5. And the network architecture parameters of VGG16 is shown in S1 Table. ResNet.…”

Section: Plos Onementioning

confidence: 99%

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

Tan

Huang

et al. 2020

PLoS ONE

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Zanthoxyli Pericarpium (ZP) are the dried ripe peel of Zanthoxylum schinifolium Sieb. et Zucc (ZC) or Zanthoxylum bungeanum Maxim (ZB). It has wide range of uses both medicine and food, and favorable market value. The diverse specifications of components of ZP is exceptional, and the common aims of adulteration for economic profit is conducted. In this work, a novel method for the identification different species of ZP is proposed using convolutional neural networks (CNNs). The data used for the experiment is 5 classes obtained from camera and mobile phones. Firstly, the data considering 2 categories are trained to detect the labels by YOLO. Then, the multiple deep learning including VGG, ResNet, Inception v4, and DenseNet are introduced to identify the different species of ZP (HZB, DZB, OZB, ZA and JZC). In order to assess the performance of CNNs, compared with two traditional identification models including Support Vector Machines (SVM) and Back Propagation (BP). The experimental results demonstrate that the CNN model have a better performance to identify different species of ZP and the highest identification accuracy is 99.35%. The present study is proved to be a useful strategy for the discrimination of different traditional Chinese medicines (TCMs).

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Section: Plos Onementioning

confidence: 99%

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

Tan

Huang

et al. 2020

PLoS ONE

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“…Deep learning has been the preferred mode of ECG classification over the last few years [4, 31, [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] ]. One-dimensional convolutional neural networks (1D-CNN) have become popular to classify ECG records because of their one-dimension structure.…”

Section: Introductionmentioning

confidence: 99%

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

Yıldırım

Talo

Ciaccio

et al. 2020

Computer Methods and Programs in Biomedicine

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Highlights DNN model is proposed to detect arrhythmia. More than 10,000 individual subject ECG records subject records are used. Two different scenarios are employed: (i) reduced rhythms (seven rhythm types) and (ii) merged rhythms (four rhythm types). Achieved 92.24% and 96.13% accuracies for the reduced and merged rhythm classes, respectively. System can aid cardiologists in the accurate detection of arrhythmia accurately.

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“…However, they have been poorly validated by making use of too reduced proprietary datasets. For instance, Li et al [18] achieved a discriminant ability greater than 97% when a CNN-based method was trained and tested with databases only composed of 24 and 14 subjects, respectively. Similarly, Yildirim et al [13] also developed another CNNbased algorithm able to classify seventeen different ECG abnormalities with an accuracy of about 90%.…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

Huerta

Martínez-Rodrigo

Rieta

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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A broad variety of algorithms for detection and classification of rhythm and morphology abnormalities in ECG recordings have been proposed in the last years. Although some of them have reported very promising results, they have been mostly validated on short and non-public datasets, thus making their comparison extremely difficult. PhysioNet/CinC Challenge 2020 provides an interesting opportunity to compare these and other algorithms on a wide set of ECG recordings. The present model was created by "ELBIT" team. The algorithm is based on deep learning, and the segmentation of all beats in the 12-lead ECG recording, generating a new signal for each one by concatenating sequentially the information found in each lead. The resulting signal is then transformed into a 2-D image through a continuous Wavelet transform and inputted to a convolutional neural network. According to the competition guidelines, classification results were evaluated in terms of a class-weighted F-score (F β) and a generalization of the Jaccard measure (G β). In average for all training signals, these metrics were 0.933 and 0.811, respectively. Regarding validation on the testing set from the first phase of the challenge, mean values for both performance indices were 0.654 and 0.372, respectively.

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Patient-specific ECG classification by deeper CNN from generic to dedicated

Cited by 110 publications

References 35 publications

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

Identification of different species of Zanthoxyli Pericarpium based on convolution neural network

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs

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