ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks

Park, Junsang; Kim, Jin Kook; Jung, Sunghoon; Gil, Yeongjoon; Choi, Jong Il; Son, Ho Sung

doi:10.3390/app10186495

Cited by 30 publications

(22 citation statements)

References 15 publications

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“…Park et al [35] combined attention mechanism SENet with ResNet, and added SE to different depth layers of ResNet (18/34/50/101/152), which was suitable for 7 classification of electrocardiogram signals. Miao et al [36] based on ResNet29, introduced gating strategy into residual unit, which changed feature fusion from adding the same weight to weighted summation,and pointed out that the performance improvement brought by gating strategy decreased with the increase of network complexity.…”

Section: A．models and Methodsmentioning

confidence: 99%

Detection of Wheat Unsound Kernels Based on Improved ResNet

Gao

Tong

2022

IEEE Access

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In the process of grain acquisition, the unsound kernel of wheat is detected mainly by manual detection, and the method of detection by computer vision is still in the experimental stage. Aiming at the problems such as expensive equipment for image acquisition, difficulty in adhesion segmentation, and low recognition efficiency in detection, this paper takes six kinds of wheat as objects, namely sound kernel, broken kernel, sprouted kernel, injured kernel, moldy kernel and spotted kernel, builds a wheat image acquisition platform, and establishes a two-kernels adhesion wheat segmentation algorithm based on concave-mask. Among the total 9988 wheat grains, the error rate accounts for 0.93%. By comparing the advantages and disadvantages of GoogleNet, DenseNet, IX-ResNet, Res2Net, exploring the improvement of depth, width, downsampling mode, convolution order, attention mechanism18, receptive field, and finally puts forward a wheat unsound kernel detection model based on Res24_D_CBAM_Atrous. The Macro avg values of Precision, Recall and F1 are 94%, 95% and 94%, respectively, which are increased by 3%-4% based on the original Res34 and the prediction time is reduced by 220s, which can meet the rapid and accurate evaluation of wheat appearance quality, and has important theoretical significance and practical application value for wheat market circulation.

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Section: A．models and Methodsmentioning

confidence: 99%

Detection of Wheat Unsound Kernels Based on Improved ResNet

Gao

Tong

2022

IEEE Access

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“…x= m 4 k c e 3 I e j 2 n 2 a "m" means single occurrence and "m 2 " represents the occurrence of primitive m twice and so on. [26,[33][34][35][36][37][38].…”

Section: Algorithmmentioning

confidence: 99%

Machine Learning Techniques and Syntactic Pattern Recognition Based Heart Disease Prediction for Smart Health

Bandyopadhyay¹,

Dutta²

2021

Preprint

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Cardiovascular disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of body. CVD plays an important factor of life since it may cause death of human. It is necessary to detect early of this disease for securing patients life. In this chpter two exclusively different methods are proposed for detection of heart disease. The first one is Pattern Recognition Approach with grammatical concept and the second one is machine learning approach. In the syntactic pattern recognition approach initially ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input to the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases beside normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information in order to recognize underlying relationship patterns from the information set. It is basically a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) are used in the paper for diagnosing the heart disease.

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“…A recent follow-up [12] made two extensions to the study above. In this follow-up, six types of arrhythmia were considered, i.e., atrial fibrillation, atrial flutter, sinus bradycardia, sinus tachycardia, premature ventricular contraction and first-degree atrioventricular block.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For this reason, emerging literature has focused on the early diagnosis of arrhythmia, using deep neural networks for their better performance measures than those of other approaches [6][7][8][9][10][11][12][13][14]. These studies utilized electrocardiogram (ECG) data, applying convolutional neural networks (Alexnet, Resnet) [6][7][8][9][10][11][12], recurrent neural networks (long short-term memory) [13] or both [14] with various class categories and accuracy results (80-99%). For instance, a recent study [11] employed ECG data in a general hospital, comparing 30 convolutional neural networks for the classification of the normal sinus rhythm vs. atrial fibrillation status: six Alexnets with five convolutional layers, three fully connected layers and 3 to 256 kernels; and 24 Resnets with 2 to 8 residual blocks and 2 to 64 kernels.…”

Section: Introductionmentioning

confidence: 99%

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Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device

Lee

Park

Kim

et al. 2022

Sensors

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The importance of an embedded wearable device with automatic detection and alarming cannot be overstated, given that 15–30% of patients with atrial fibrillation are reported to be asymptomatic. These asymptomatic patients do not seek medical care, hence traditional diagnostic tools including Holter are not effective for the further prevention of associated stroke or heart failure. This is likely to be more so in the era of COVID-19, in which patients become more reluctant on hospitalization and checkups. However, little literature is available on this important topic. For this reason, this study developed efficient deep learning with model compression, which is designed to use ECG data and classify arrhythmia in an embedded wearable device. ECG-signal data came from Korea University Anam Hospital in Seoul, Korea, with 28,308 unique patients (15,412 normal and 12,896 arrhythmia). Resnets and Mobilenets with model compression (TensorFlow Lite) were applied and compared for the diagnosis of arrhythmia in an embedded wearable device. The weight size of the compressed model registered a remarkable decrease from 743 MB to 76 KB (1/10000), whereas its performance was almost the same as its original counterpart. Resnet and Mobilenet were similar in terms of accuracy, i.e., Resnet-50 Hz (97.3) vs. Mo-bilenet-50 Hz (97.2), Resnet-100 Hz (98.2) vs. Mobilenet-100 Hz (97.9). Here, 50 Hz/100 Hz denotes the down-sampling rate. However, Resnets took more flash memory and longer inference time than did Mobilenets. In conclusion, Mobilenet would be a more efficient model than Resnet to classify arrhythmia in an embedded wearable device.

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ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks

Cited by 30 publications

References 15 publications

Detection of Wheat Unsound Kernels Based on Improved ResNet

Detection of Wheat Unsound Kernels Based on Improved ResNet

Machine Learning Techniques and Syntactic Pattern Recognition Based Heart Disease Prediction for Smart Health

Compressed Deep Learning to Classify Arrhythmia in an Embedded Wearable Device

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