A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram

Agrusa, Anjulie S.; Gharibans, Armen A.; Allegra, Alexis A.; Kunkel, David C.; Coleman, Todd P.

doi:10.1109/tbme.2019.2922235

Cited by 20 publications

(15 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 20 ] According to the body surface gastric electrical signals, the EGG, which can show the electrical activity of the gastric smooth muscle, indirectly reflects the ionization activity and the relaxation and contraction situation of the gastric wall muscle, and objectively reflects the gastrointestinal dynamic status. [ 21 ] In the study published in the current issue, Zheng et al found that 16-week EA treatment was more effective than 16-week sham EA treatment: a higher percentage of patients had no symptoms of dyspepsia at all or improved symptoms of dyspepsia. [ 13 ] Previously, many studies have explored the influence of EA on FD.…”

Section: Discussionmentioning

confidence: 99%

Electroacupuncture for the treatment of functional dyspepsia

Mao

Guo

et al. 2020

Medicine

View full text Add to dashboard Cite

Background: Functional dyspepsia (FD) is a common functional gastrointestinal disease. Acupuncture, including electroacupuncture (EA) is widely used as a complementary and alternative treatment for patients with FD. This study aimed to explore the effectiveness of EA for the treatment of FD. Methods: We searched Embase, PubMed, and the Cochrane Central Register of Controlled Trials (Cochrane Library) for randomized controlled trials of FD treated by EA from inception to February 3, 2020. Two reviewers will independently screen studies for data extraction and assess the quality and risk of bias. The Cochrane Collaboration's risk of bias tool, RevMan 5.3 software were used for meta-analysis. Data were pooled to calculate relative risk and 95% confidence intervals (CIs) of substantial improvement after treatment for dichotomous data and mean differences (SMDs) and 95% CIs for continuous data. Results: Seven randomized clinical trials included 853 patients. This meta-analysis investigated the effectiveness of EA alone in the treatment of FD relative to sham-EA or pharmacologic medication (PM). The results showed that EA could significantly improve clinical symptoms. Compared with sham-EA, EA was more effective in reducing symptom scores (SMD −3.44, 95% CI −4.21 to −2.67) and increasing normal slow waves of electrogastrogram (SMD 0.93, 95% CI −0.30 to1.55). When EA was combined with PM, there was no significant difference in reducing symptom scores (SMD −0.18, 95% CI −0.51 to 0.16), increasing the effective rate of clinical symptoms (risk ratio 1.04, 95% CI 0.96 to 1.13), enhancing the level of plasma motilin (SMD 0.93, 95% CI −0.30 to1.55), and reducing gastric half-emptying time (SMD 0.02, 95% CI −0.16 to 0.20). The results also showed that there were very few adverse events reported. Conclusion: This meta-analysis suggests that EA is better than the placebo (sham-EA) in treating FD, and the therapeutic effect of EA on FD is equivalent to that of PM on FD. Compared with PM, EA for FD is safer and has fewer adverse reactions. Despite limitations due to the quality and number of the included studies, EA might be used as an effective and safe treatment for FD.

show abstract

Section: Discussionmentioning

confidence: 99%

Electroacupuncture for the treatment of functional dyspepsia

Mao

Guo

et al. 2020

Medicine

View full text Add to dashboard Cite

show abstract

“…In order to verify the comparison effect of the method proposed in this paper with other methods in the past, this paper compresses the deep learning prediction model with radial basis function neural network and Kalman filter neural network [37][38][39].…”

Section: Index Forecasting Effect Analysismentioning

confidence: 99%

Research on Market Stock Index Prediction Based on Network Security and Deep Learning

2021

Security and Communication Networks

View full text Add to dashboard Cite

As one of the most popular financial management methods, stocks have attracted more and more investors to participate. The risks of stock investment are relatively high. How to reduce risks and increase profits has become the most concerned issue for investors. Traditional stock forecasting models use forecasting models based on stock time series analysis, but time series models cannot consider the influence of investor sentiment on stock market changes. In order to use investor sentiment information to make more accurate stock market forecasts, this paper establishes a stock index forecast and network security model based on time series and deep learning. Based on the time series model, it is proposed to use CNN to extract in-depth emotional information to replace the basic emotional features of the emotional extraction level. At the data source level, other information sources, such as basic features, are introduced to further improve the predictive performance of the model. The results show that the algorithm is feasible and effective and can better predict the changes in the market stock index. This also proves that multiple information sources can improve the accuracy of model prediction more effectively than a single information source.

show abstract

“…In this paper, we used transfer learning and fine-tuning approaches (see Figure 5 ). Transfer learning is characterized by using a trained model in one domain as a starting point for another domain’s training phase, reducing the amount of data required for achieving acceptable results, consequently reducing the processing time [ 47 ]. The trained model provides a structure that previously acquired knowledge and can be used to solve new problems, in contrast to the approaches of classical machine learning algorithms [ 48 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

DeepHP: A New Gastric Mucosa Histopathology Dataset for Helicobacter pylori Infection Diagnosis

Gonçalves

Santos

Brito

et al. 2022

IJMS

View full text Add to dashboard Cite

Emerging deep learning-based applications in precision medicine include computational histopathological analysis. However, there is a lack of the required training image datasets to generate classification and detection models. This phenomenon occurs mainly due to human factors that make it difficult to obtain well-annotated data. The present study provides a curated public collection of histopathological images (DeepHP) and a convolutional neural network model for diagnosing gastritis. Images from gastric biopsy histopathological exams were used to investigate the performance of the proposed model in detecting gastric mucosa with Helicobacter pylori infection. The DeepHP database comprises 394,926 histopathological images, of which 111 K were labeled as Helicobacter pylori positive and 283 K were Helicobacter pylori negative. We investigated the classification performance of three Convolutional Neural Network architectures. The models were tested and validated with two distinct image sets of 15% (59K patches) chosen randomly. The VGG16 architecture showed the best results with an Area Under the Curve of 0.998%. The results showed that CNN could be used to classify histopathological images from gastric mucosa with marked precision. Our model evidenced high potential and application in the computational pathology field.

show abstract

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram

Cited by 20 publications

References 70 publications

Electroacupuncture for the treatment of functional dyspepsia

Electroacupuncture for the treatment of functional dyspepsia

Research on Market Stock Index Prediction Based on Network Security and Deep Learning

DeepHP: A New Gastric Mucosa Histopathology Dataset for Helicobacter pylori Infection Diagnosis

Contact Info

Product

Resources

About