An exhaustive review of machine and deep learning based diagnosis of heart diseases

2021 ◽  
Author(s):  
Adyasha Rath ◽  
Debahuti Mishra ◽  
Ganapati Panda ◽  
Suresh Chandra Satapathy
Keyword(s):  
Deep Learning ◽  
Heart Diseases

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

Deep learning-based automated left ventricular ejection fraction assessment using 2D echocardiography

2021 ◽  
Author(s):  
Xin Liu ◽  
Yiting Fan ◽  
Shuang Li ◽  
Meixiang Chen ◽  
Ming Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ejection Fraction ◽  
High Performance ◽  
Diagnostic Performance ◽  
Heart Diseases ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
2D Echocardiography ◽  
The Impact

Background. Deep-learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measurement, but the diagnostic performance was rarely evaluated for various phenotypes of heart disease. This study aims to evaluate a new DL algorithm for automated LVEF measurement using two-dimensional echocardiography (2DE) images collected from 3 centers. The impact of 3 ultrasound machines and 3 phenotypes of heart diseases on the automatic LVEF measurement was evaluated. Methods and Results. Using 36890 frames of 2DE from 340 patients, we developed a DL algorithm based on U-Net (DPS-Net) and the biplane Simpson's method was applied for LVEF calculation. Results showed a high performance in LV segmentation and LVEF measurement across phenotypes and echo systems by using DPS-Net. Good performance was obtained for LV segmentation when DPS-Net was tested on the CAMUS dataset (Dice coefficient of 0.932 and 0.928 for ED and ES). Better performance of LV segmentation in study-wise evaluation was observed by comparing the DPS-Net v2 to the EchoNet-dynamic algorithm (p = 0.008). DPS-Net was associated with high correlations and good agreements for the LVEF measurement. High diagnostic performance was obtained that the area under receiver operator characteristic curve was 0.974, 0.948, 0.968 and 0.972 for normal hearts and disease phenotypes including atrial fibrillation, hypertrophic cardiomyopathy, dilated cardiomyopathy, respectively. Conclusion. High performance was obtained by using DPS-Net in LV detection and LVEF measurement for heart failure with several phenotypes. High performance was observed in a large-scale dataset, suggesting that the DPS-Net was highly adaptive across different echocardiographic systems.

Early Warning and Prediction of Heart Failure by Ensemble Deep Learning and Trend Similarity Measure based on Real Healthcare Data (Preprint)

2021 ◽  
Author(s):  
Chunjie Zhou ◽  
Aihua Hou ◽  
Ali Li ◽  
Zhenxing Zhang ◽  
Pengfei Dai ◽  
...  
Keyword(s):  
Risk Factors ◽  
Heart Failure ◽  
Deep Learning ◽  
Similarity Measure ◽  
Early Warning ◽  
Heart Diseases ◽  
Prediction Method ◽  
Physiological Data ◽  
Slow Disease Progression ◽  
Deep Learning Model

BACKGROUND In recent years, heart diseases cause more than 18 million deaths every year. Heart failure (HF) prediction is essential to slow disease progression by changing lifestyle and pharmacologic interventions before heart diseases occur. Various researches have been proposed recently to predict heart failure. However, these methods did not combine different data sources with high-dimensional for heart failure prediction. In addition, the existing methods failed to consider the coexisting risk factors for heart failure and the complex relationships among them. OBJECTIVE Our goal is to make early warning and prediction of heart failure, which can offer the opportunity to test and ultimately develop effective lifestyle and pharmacologic interventions. In this paper, both electronic medical records and physiological data are considered, so as to provide enough source information to identify valuable risk factors of heart failure and make HF prediction. METHODS In this paper, an early warning and prediction method for heart failure is proposed using deep learning and trend similarity measure approaches. First, we present the data fusion and feature extraction method to merge different sources of data and get several important risk factors, which contain relevant and valuable information for HF. Second, an ensemble deep learning model for HF prediction is proposed based on gradient algorithms and back propagation techniques. In addition, we present an anomaly detection method to eliminate abnormal data caused by mood changes or environmental factors. Finally, evaluated by the Haar wavelet decomposition strategy, a data sequence trend similarity measure method is proposed aiming at prediction and early warning of heart failure in massive medical data. RESULTS The proposed method is evaluated based on our real research project HeartCarer, which includes risk factor information and physiological data. We combine these datasets from 2015 to 2020 to make a better performance evaluation for the proposed deep learning model and similarity measure method. The combined dataset totally involves 2,976 HF patients, 18,203 family members closely related to patients, and 295,801 healthy people. By comparing with other state-of-the-art methods and our prior work in [2] (90%), the proposed method can obtain a higher accuracy of 98.5% in heart disease prediction. CONCLUSIONS Heart failure (HF) prediction is essential to slow disease progression by changing lifestyle and pharmacologic interventions before heart diseases occur. An early warning and prediction method for heart failure is proposed using deep learning and trend similarity measure approaches in this paper. The proposed method is evaluated based on our real research project HeartCarer and obtain a high accuracy in heart disease prediction.

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 Stratification of Cardiovascular Diseases Using Deep Learning

2020 ◽  
Vol 34 (4) ◽  
pp. 377-385
Author(s):  
Bhanu Prakash Doppala ◽  
Debnath Bhattacharyya ◽  
Midhun Chakkravarthy
Keyword(s):  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Magnetic Resonance ◽  
Hybrid Algorithm ◽  
Heart Diseases ◽  
Neural Model ◽  
Ant Colony ◽  
World Health ◽  
African Buffalo ◽  
Fuzzy Neural

Heart-based diseases are one of the causes for major death rate in the world. WHO (World Health Organization) specified that 17 million of people are losing their lives per year due to several heart diseases. Artificial Intelligence playing a prominent role in disease identification and prediction from medical data. Magnetic Resonance Imaging plays a vital role in producing detailed images of internal organs and soft tissues for better understanding the condition. Magnetic Resonance Image contains more noisy data this is one of the issues to be addressed, hence this research focuses on the prediction of cardiovascular diseases using an innovative hybrid algorithm and addresses the issue of noise using Hann filters. A Hybrid algorithm is proposed with combination of Cat Fuzzy Neural Model (CFuNM) and Hybrid Ant Colony and African Buffalo Optimization. Cat Fuzzy Neural Model (CFuNM) is used to classify cardiac diseases such as cardiomyopathy, pericardial effusion, coronary artery, amyloidosis, and other coronary heart diseases and for the severity analysis of disease we used Hybrid Ant Colony and African Buffalo Optimization (HAC-ABO) mechanism. This research of Hybrid deep learning model improved the classification accuracy of 99.3% and error rate of 0.18% which is considerably good when compared to existing methods.

scholarly journals Detection and Classification of Cardiac Arrhythmias by a Challenge-Best Deep Learning Neural Network Model

2019 ◽  
Author(s):  
Tsai-Min Chen ◽  
Chih-Han Huang ◽  
Edward S. C. Shih ◽  
Yu-Feng Hu ◽  
Ming-Jing Hwang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Cardiac Arrhythmia ◽  
Heart Diseases ◽  
Clinical Applications ◽  
Modeling Tools ◽  
Bundle Branch Block ◽  
St Segment Elevation ◽  
St Segment ◽  
Depth Analysis

AbstractBackgroundElectrocardiogram (ECG) is widely used to detect cardiac arrhythmia (CA) and heart diseases. The development of deep learning modeling tools and publicly available large ECG data in recent years has made accurate machine diagnosis of CA an attractive task to showcase the power of artificial intelligence (AI) in clinical applications.Methods and FindingsWe have developed a convolution neural network (CNN)-based model to detect and classify nine types of heart rhythms using a large 12-lead ECG dataset (6877 recordings) provided by the China Physiological Signal Challenge (CPSC) 2018. Our model achieved a median overall F1-score of 0.84 for the 9-type classification on CPSC2018’s hidden test set (2954 ECG recordings), which ranked first in this latest AI competition of ECG-based CA diagnosis challenge. Further analysis showed that concurrent CAs observed in the same patient were adequately predicted for the 476 patients diagnosed with multiple CA types in the dataset. Analysis also showed that the performances of using only single lead data were only slightly worse than using the full 12 lead data, with leads aVR and V1 being the most prominent. These results are extensively discussed in the context of their agreement with and relevance to clinical observations.ConclusionsAn AI model for automatic CA diagnosis achieving state-of-the-art accuracy was developed as the result of a community-based AI challenge advocating open-source research. In- depth analysis further reveals the model’s ability for concurrent CA diagnosis and potential use of certain single leads such as aVR in clinical applications.AbbreviationsCA, cardiac arrhythmia; AF, Atrial fibrillation; I-AVB, first-degree atrioventricular block; LBBB, left bundle branch block; RBBB, right bundle branch block; PAC, premature atrial contraction; PVC, premature ventricular contraction; STD, ST-segment depression; STE, ST-segment elevation.

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