scholarly journals Detection of subclinical rheumatic heart disease in children using a deep learning algorithm on digital stethoscope: a study protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e044070
Author(s):  
Fatima Ali ◽  
Babar Hasan ◽  
Huzaifa Ahmad ◽  
Zahra Hoodbhoy ◽  
Zainab Bhuriwala ◽  
...  
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Study Participant ◽  
Scientific Publications ◽  
Timely Initiation ◽  
Waveform Data ◽  
Rheumatic Heart

IntroductionRheumatic heart diseases (RHDs) contribute significant morbidity and mortality globally. To reduce the burden of RHD, timely initiation of secondary prophylaxis is important. The objectives of this study are to determine the frequency of subclinical RHD and to train a deep learning (DL) algorithm using waveform data from the digital auscultatory stethoscope (DAS) in predicting subclinical RHD.Methods and analysisWe aim to recruit 1700 children from a group of schools serving the underprivileged over a 12-month period in Karachi (Pakistan). All consenting students within the age of 5–15 years with no underlying congenital heart disease will be eligible for the study. We will gather information regarding sociodemographics, anthropometric data, history of symptoms or diagnosis of rheumatic fever, phonocardiogram (PCG) and electrocardiography (ECG) data obtained from DAS. Handheld echocardiogram will be performed on each study participant to assess the presence of a mitral regurgitation (MR) jet (>1.5 cm), or the presence of aortic regurgitation (AR) in any view. If any of these findings are present, a confirmatory standard echocardiogram using the World Heart Federation (WHF) will be performed to confirm the diagnosis of subclinical RHD. The auscultatory data from digital stethoscope will be used to train the deep neural network for the automatic identification of patients with subclinical RHD. The proposed neural network will be trained in a supervised manner using labels from standard echocardiogram of the participants. Once trained, the neural network will be able to automatically classify the DAS data in one of the three major categories—patient with definite RHD, patient with borderline RHD and normal subject. The significance of the results will be confirmed by standard statistical methods for hypothesis testing.Ethics and disseminationEthics approval has been taken from the Aga Khan University, Pakistan. Findings will be disseminated through scientific publications and to collaborators.Article focusThis study focuses on determining the frequency of subclinical RHD in school-going children in Karachi, Pakistan and developing a DL algorithm to screen for this condition using a digital stethoscope.

scholarly journals Automatic Classification of Cardiac Arrhythmias based on ECG Signals Using Transferred Deep Learning Convolution Neural Network

2021 ◽  
Vol 2089 (1) ◽  
pp. 012058
Author(s):  
P. Giriprasad Gaddam ◽  
A Sanjeeva reddy ◽  
R.V. Sreehari
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Normal Sinus Rhythm ◽  
Automatic Classification ◽  
Ecg Signals ◽  
Major Interest

Abstract In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

scholarly journals Heart Diseases Prediction using Deep Learning Neural Network Model

2020 ◽  
Vol 9 (3) ◽  
pp. 2244-2248
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Heart Diseases ◽  
Medical Science ◽  
Disease Prediction ◽  
Health Issues ◽  
Deep Learning Neural Network

Deep learning plays an important role in the field of medical science in solving health issues and diagnosing various diseases. So in this paper, we will discuss heart disease. We proposed a model for heart disease prediction. Heart Disease is on of key area where Deep Neural Network can be used so we can improve the overall quality of the classification of heart disease. The classification can be performed on the various ways like KNN, SVM, Naïve Bayes, Random Forest. Heart Disease UCI dataset will be used to demonstrate Talos Hyper-parameter optimization is more efficient than others.

Share Market Data Prediction Strategies using Deep Learning Algorithm

2019 ◽  
Vol 13 ◽  
Author(s):  
A John. ◽  
D. Praveen Dominic ◽  
M. Adimoolam ◽  
N. M. Balamurugan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Stock Market ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Market Price ◽  
Stochastic Gradient ◽  
Stochastic Gradient Descent ◽  
Mining Machine ◽  
Gradient Descent Algorithm

Background:: Predictive analytics has a multiplicity of statistical schemes from predictive modelling, data mining, machine learning. It scrutinizes present and chronological data to make predictions about expectations or if not unexplained measures. Most predictive models are used for business analytics to overcome loses and profit gaining. Predictive analytics is used to exploit the pattern in old and historical data. Objective: People used to follow some strategies for predicting stock value to invest in the more profit-gaining stocks and those strategies to search the stock market prices which are incorporated in some intelligent methods and tools. Such strategies will increase the investor’s profits and also minimize their risks. So prediction plays a vital role in stock market gaining and is also a very intricate and challenging process. Method: The proposed optimized strategies are the Deep Neural Network with Stochastic Gradient for stock prediction. The Neural Network is trained using Back-propagation neural networks algorithm and stochastic gradient descent algorithm as optimal strategies. Results: The experiment is conducted for stock market price prediction using python language with the visual package. In this experiment RELIANCE.NS, TATAMOTORS.NS, and TATAGLOBAL.NS dataset are taken as input dataset and it is downloaded from National Stock Exchange site. The artificial neural network component including Deep Learning model is most effective for more than 100,000 data points to train this model. This proposed model is developed on daily prices of stock market price to understand how to build model with better performance than existing national exchange method.

scholarly journals Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 652 ◽  
Author(s):  
Carlo Augusto Mallio ◽  
Andrea Napolitano ◽  
Gennaro Castiello ◽  
Francesco Maria Giordano ◽  
Pasquale D'Alessio ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitor ◽  
Learning Algorithm ◽  
Checkpoint Inhibitor ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Algorithm

Background: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. Methods: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann–Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). Results: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). Conclusions: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.

scholarly journals Developing an Individual Glucose Prediction Model Using Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6460
Author(s):  
Dae-Yeon Kim ◽  
Dong-Sik Choi ◽  
Jaeyun Kim ◽  
Sung Wan Chun ◽  
Hyo-Wook Gil ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Glucose Level ◽  
Recurrent Neural Network ◽  
Learning Algorithm ◽  
Percentage Error ◽  
Glucose Prediction

In this study, we propose a personalized glucose prediction model using deep learning for hospitalized patients who experience Type-2 diabetes. We aim for our model to assist the medical personnel who check the blood glucose and control the amount of insulin doses. Herein, we employed a deep learning algorithm, especially a recurrent neural network (RNN), that consists of a sequence processing layer and a classification layer for the glucose prediction. We tested a simple RNN, gated recurrent unit (GRU), and long-short term memory (LSTM) and varied the architectures to determine the one with the best performance. For that, we collected data for a week using a continuous glucose monitoring device. Type-2 inpatients are usually experiencing bad health conditions and have a high variability of glucose level. However, there are few studies on the Type-2 glucose prediction model while many studies performed on Type-1 glucose prediction. This work has a contribution in that the proposed model exhibits a comparative performance to previous works on Type-1 patients. For 20 in-hospital patients, we achieved an average root mean squared error (RMSE) of 21.5 and an Mean absolute percentage error (MAPE) of 11.1%. The GRU with a single RNN layer and two dense layers was found to be sufficient to predict the glucose level. Moreover, to build a personalized model, at most, 50% of data are required for training.

Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

2021 ◽  
Vol 11 (12) ◽  
pp. 3044-3053
Author(s):  
Rakesh Kumar Mahendran ◽  
V. Prabhu ◽  
V. Parthasarathy ◽  
A. Mary Judith
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Detection Techniques

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

2019 ◽  
pp. 191-207
Author(s):  
P. Priyanga ◽  
N. C. Naveen
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
K Nearest Neighbor ◽  
Aged Patients ◽  
Learning Machine

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.

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