scholarly journals Using Deep Learning Detection Of Arrhythmia

2019 ◽  
Vol 27 ◽  
pp. 03001
Author(s):  
Zhang Yue ◽  
Li Feng
Keyword(s):  
Deep Learning ◽  
Large Error ◽  
Arrhythmia Detection ◽  
Single Output ◽  
Average Accuracy ◽  
Multiple Input ◽  
Atrial Premature Beat ◽  
Point Detection ◽  
Deep Learning Model

The field of deep learning applications is becoming more widespread. The use of traditional algorithms for arrhythmia detection is cumbersome and the algorithm complexity is relatively high. Using the deep learning model to directly input data into the model will make it difficult to effectively segment the data, which will have a large error in the recognition accuracy. We collected data from six commonly used ECG databases and then used the label shuffling method to amplify the samples, effectively overcoming the effects of sample imbalance. The LSTM model is used for feature point detection to effectively locate the key feature points of the ECG signal, thereby completing the segmentation and processing of the data. Finally, the use of a multiple input model single output for arrhythmia detection has achieved significant results. The average accuracy of the final arrhythmia classification of the model reached 0.711. The accuracy of detecting the fusion of ventricular and normal beat and the aberrated atrial premature beat exceeds 0.9.

scholarly journals Deep Learning Model Comparison for Vision-Based Classification of Full/Empty-Load Trucks in Earthmoving Operations

2019 ◽  
Vol 9 (22) ◽  
pp. 4871 ◽  
Author(s):  
Quan Liu ◽  
Chen Feng ◽  
Zida Song ◽  
Joseph Louis ◽  
Jian Zhou
Keyword(s):  
Deep Learning ◽  
Model Comparison ◽  
Surveillance Systems ◽  
Comparison Study ◽  
Learning Models ◽  
The Core ◽  
Dump Trucks ◽  
Deep Learning Model ◽  
Contact Field

Earthmoving is an integral civil engineering operation of significance, and tracking its productivity requires the statistics of loads moved by dump trucks. Since current truck loads’ statistics methods are laborious, costly, and limited in application, this paper presents the framework of a novel, automated, non-contact field earthmoving quantity statistics (FEQS) for projects with large earthmoving demands that use uniform and uncovered trucks. The proposed FEQS framework utilizes field surveillance systems and adopts vision-based deep learning for full/empty-load truck classification as the core work. Since convolutional neural network (CNN) and its transfer learning (TL) forms are popular vision-based deep learning models and numerous in type, a comparison study is conducted to test the framework’s core work feasibility and evaluate the performance of different deep learning models in implementation. The comparison study involved 12 CNN or CNN-TL models in full/empty-load truck classification, and the results revealed that while several provided satisfactory performance, the VGG16-FineTune provided the optimal performance. This proved the core work feasibility of the proposed FEQS framework. Further discussion provides model choice suggestions that CNN-TL models are more feasible than CNN prototypes, and models that adopt different TL methods have advantages in either working accuracy or speed for different tasks.

Detection and classification of arrhythmia using an explainable deep learning model

2021 ◽  
Author(s):  
Yong-Yeon Jo ◽  
Joon-myoung Kwon ◽  
Ki-Hyun Jeon ◽  
Yong-Hyeon Cho ◽  
Jae-Hyun Shin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Attention-Guided Digital Adversarial Patches on Visual Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Dapeng Lang ◽  
Deyun Chen ◽  
Ran Shi ◽  
Yongjun He
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Detection Algorithm ◽  
Classification Model ◽  
Practical Application ◽  
Single Target ◽  
Attachment Mechanism ◽  
Human Eyes ◽  
Deep Learning Model

Deep learning has been widely used in the field of image classification and image recognition and achieved positive practical results. However, in recent years, a number of studies have found that the accuracy of deep learning model based on classification greatly drops when making only subtle changes to the original examples, thus realizing the attack on the deep learning model. The main methods are as follows: adjust the pixels of attack examples invisible to human eyes and induce deep learning model to make the wrong classification; by adding an adversarial patch on the detection target, guide and deceive the classification model to make it misclassification. Therefore, these methods have strong randomness and are of very limited use in practical application. Different from the previous perturbation to traffic signs, our paper proposes a method that is able to successfully hide and misclassify vehicles in complex contexts. This method takes into account the complex real scenarios and can perturb with the pictures taken by a camera and mobile phone so that the detector based on deep learning model cannot detect the vehicle or misclassification. In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. Through the test of different detectors, the patch generated in the single target detection algorithm can also attack other detectors and do well in transferability. Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. Affected by the real world, such as distance, light, angles, resolution, etc., the false classification of the target is realized by reducing the confidence level and background of the target, which greatly perturbs the detection results of the target detector. In COCO Dataset 2017, it reveals that the success rate of this algorithm reaches 88.7%.

1140P Validation of a deep learning model for the classification of lung cancer in a large cohort of biopsied samples

2021 ◽  
Vol 32 ◽  
pp. S926-S927
Author(s):  
G. Toyokawa ◽  
Y. Yamada ◽  
N. Haratake ◽  
Y. Shiraishi ◽  
T. Takenaka ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Learning Model ◽  
Large Cohort ◽  
Deep Learning Model
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