scholarly journals Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Ahmad Hasasneh ◽  
Nikolas Kampel ◽  
Praveen Sripad ◽  
N. Jon Shah ◽  
Jürgen Dammers
Keyword(s):  
Classification Scheme ◽  
Data Augmentation ◽  
High Reliability ◽  
Combined Model ◽  
Independent Components ◽  
Artifact Rejection ◽  
Auxiliary Signal ◽  
Electrocardiogram Ecg ◽  
And Training

We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

scholarly journals Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

2022 ◽  
Vol 12 ◽  
Author(s):  
Shenda Hong ◽  
Wenrui Zhang ◽  
Chenxi Sun ◽  
Yuxi Zhou ◽  
Hongyan Li
Keyword(s):  
Data Augmentation ◽  
Meta Analysis ◽  
Single Type ◽  
Training Strategy ◽  
Advantages And Disadvantages ◽  
The Common ◽  
Classification Tasks ◽  
Electrocardiogram Ecg ◽  
And Training ◽  
Better Than

Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.

scholarly journals MODELS AND METHODS OF CLASSIFICATION OF TEXT REQUESTS IN THE SYSTEM OF PATTERN IDENTIFICATION OF POLYLINGUAL TEXTS

2020 ◽  
Vol 1 ◽  
pp. 130-134
Author(s):  
Vladimir P. Kulikov ◽  
Valentina P. Kulikova ◽  
Elena M. Krylova ◽  
Gulnur T. Yerkebulan
Keyword(s):  
Quality Assessment ◽  
Classification Scheme ◽  
Pattern Identification ◽  
Classification Methods ◽  
Text Documents ◽  
And Training ◽  
Selection Of

A classification scheme for text documents consisting of five steps is described: pre-processing, indexing, selection of features, construction and training of a classifier, quality assessment. Two comparative analyzes by classification methods are considered. Conclusions are drawn about models and classification methods regarding implementation efficiency.

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

scholarly journals UAV Image Multi-Labeling with Data-Efficient Transformers

2021 ◽  
Vol 11 (9) ◽  
pp. 3974
Author(s):  
Laila Bashmal ◽  
Yakoub Bazi ◽  
Mohamad Mahmoud Al Rahhal ◽  
Haikel Alhichri ◽  
Naif Al Ajlan
Keyword(s):  
Data Augmentation ◽  
Feature Representation ◽  
Aerial Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Proposed Model ◽  
Class Labels ◽  
Using Data ◽  
Uav Image

In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers. During the training phase, we generated a second view for each image from the training set using data augmentation. Then, both the image and its augmented version were reshaped into a sequence of flattened patches and then fed to the transformer encoder. The latter extracts a compact feature representation from each image with the help of a self-attention mechanism, which can handle the global dependencies between different regions of the high-resolution aerial image. On the top of the encoder, we mounted two classifiers, a token and a distiller classifier. During training, we minimized a global loss consisting of two terms, each corresponding to one of the two classifiers. In the test phase, we considered the average of the two classifiers as the final class labels. Experiments on two datasets acquired over the cities of Trento and Civezzano with a ground resolution of two-centimeter demonstrated the effectiveness of the proposed model.

scholarly journals Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Picture ◽  
Data Augmentation ◽  
Lower Amount ◽  
Training Procedure ◽  
X Ray ◽  
Severe Clinical Picture ◽  
Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

scholarly journals Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

2021 ◽  
Author(s):  
Christian Horn ◽  
Oscar Ivarsson ◽  
Cecilia Lindhé ◽  
Rich Potter ◽  
Ashely Green ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bronze Age ◽  
Data Augmentation ◽  
Rock Art ◽  
Training Data ◽  
Future Research ◽  
Rock Surface ◽  
Southern Scandinavia ◽  
Bounding Boxes

AbstractRock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art.

scholarly journals Midventricular takotsubo syndrome: different dyskinesia location leading to a different electrocardiogram?

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
M Padilla Lopez ◽  
A Duran Cambra ◽  
M Vidal Burdeus ◽  
L Rodriguez Sotelo ◽  
J Sanchez Vega ◽  
...  
Keyword(s):  
Acute Phase ◽  
Clinical Symptoms ◽  
Takotsubo Syndrome ◽  
St Segment Elevation ◽  
Funding Sources ◽  
Wave Inversion ◽  
St Segment ◽  
Electrocardiogram Ecg ◽  
Study Inclusion

Abstract Funding Acknowledgements Type of funding sources: None. Background Takotsubo syndrome (TKS) is characterized by the appearance of apical reversible dyskinesia in its typical form. Electrocardiogram (ECG) in the acute phase (<12 from symptom onset) generally shows anterior ST segment elevation. Nonetheless, other atypical forms of TKS have been described depending on the location of the dyskinetic segments, such as, mid-ventricular, basal and focal forms. Considering the different segments involved in these atypical forms, it seems reasonable to consider that ST changes in acute phase ECG could be different. Purpose To compare ECG in the acute phase of typical TKS versus mid-ventricular TKS, as it was the more frequent form of atypical TKS in our registry. Methods Patients included in the prospective TKS registry of our center according to the Mayo Clinic diagnostic criteria, with the first ECG performed less than 12 hours from the symptoms onset were reviewed. All cardiac left ventriculographies were reviewed to ensure a correct classification of the different types of TKS. Results A total of 297 patients were included in our local registry. 80 patients met our study inclusion criteria. 56 ECGs of typical apical TKS were compared to 24 ECGs of atypical midventricular TKS. There were no differences between the baseline characteristics in both groups, except for mid-ventricular TKS, that was more frequently triggered by physical stressor. Regarding the ECG analysis, the main difference found in our serie was related to ST-segment deviation (Table 1). While ST-segment elevation was more common in typical TKS than in atypical TKS (73% vs 50%), ST-segment depression (generally in inferior leads) was observed in 54% of patients with atypical TKS and in no patient with typical TKS (figure 1). Conclusion The different location of dyskinesia between typical TKS and mid-ventricular TKS is associated to significant differences in the ECG obtained in the first hours after the onset of the clinical symptoms. The presence of ST-segment depression is highly suggestive of mid-ventricular TKS. ECG characteristicsTypical (n = 56)Midventricular (n = 24)pSTe > 1mm, no (%)41 (73)12 (50)0,044STd >0,5 mm, no (%)013 (54)< 0,001T wave inversion, no (%)12 (21)4 (17)0,626Q wave, no (%)22 ( 39)12 (50)0,374cQT, mean (SD)445 (54)438 (37)0,578QRS low voltages*, n (%)9 ( 16)1 (4)0,328STe ST-segment elevation, STd: ST-segment depression, cQT: corrected QT interval *Voltages <5mm in all limb leads or <10mm in all precordial leads Abstract Figure. 12-lead ECG and left ventriculography

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