Batyrkhan Omarov^1,*, Meirzhan Baikuvekov¹, Zeinel Momynkulov², Aray Kassenkhan³, Saltanat Nuralykyzy³, Mereilim Iglikova³

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3745-3761, 2023, DOI:10.32604/cmc.2023.042627 - 08 October 2023

Abstract Heart disease is a leading cause of mortality worldwide. Electrocardiograms (ECG) play a crucial role in diagnosing heart disease. However, interpreting ECG signals necessitates specialized knowledge and training. The development of automated methods for ECG analysis has the potential to enhance the accuracy and efficiency of heart disease diagnosis. This research paper proposes a 3D Convolutional Long Short-Term Memory (Conv-LSTM) model for detecting heart disease using ECG signals. The proposed model combines the advantages of both convolutional neural networks (CNN) and long short-term memory (LSTM) networks. By considering both the spatial and temporal dependencies of… More >

Muhammad Tayyeb¹, Muhammad Umer¹, Khaled Alnowaiser², Saima Sadiq³, Ala’ Abdulmajid Eshmawi⁴, Rizwan Majeed⁵, Abdullah Mohamed⁶, Houbing Song⁷, Imran Ashraf^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1677-1694, 2023, DOI:10.32604/cmes.2023.026535 - 26 June 2023

Abstract Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately. Currently, electrocardiogram (ECG) data is analyzed by medical experts to determine the cardiac abnormality, which is time-consuming. In addition, the diagnosis requires experienced medical experts and is error-prone. However, automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures. This study proposes a simple multilayer perceptron (MLP) model for heart disease prediction to reduce computational complexity. ECG dataset containing averaged signals More >

Fei Yang^1,2,#, Xiaoqing Zhang^1,*,#, Yong Zhu³

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 365-382, 2020, DOI:10.32604/cmes.2020.010798 - 18 September 2020

Abstract Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms (ECG) signals. Over the past years, deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems (CADs), but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods. To tackle this problem, this work proposes a convolutional neural network (CNN) model named PDNet to recognize different types of heart arrhythmias efficiently. In the PDNet, a convolutional block named PDblock is devised, which is comprised More >

Yihe Liu¹, Aaqif Afzaal Abbasi², Atefeh Aghaei³, Almas Abbasi⁴, Amir Mosavi^{5, 6, 7}, Shahaboddin Shamshirband^{8, 9, *}, Mohammed A. A. Al-qaness¹⁰

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 31-39, 2020, DOI:10.32604/cmc.2020.07708 - 30 March 2020

Abstract Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, More >

Soham Dasgupta¹, Shae Anderson¹, Michael Kelleman², Ritu Sachdeva¹

Congenital Heart Disease, Vol.14, No.2, pp. 230-235, 2019, DOI:10.1111/chd.12687

Abstract Introduction: In the pediatric Appropriate Use Criteria (AUC), abnormal electrocardiogram (ECG) in an asymptomatic patient has been rated as an “Appropriate” indication for transthoracic echocardiogram (TTE). We hypothesized that the yield of abnormal findings on TTE for this indication will be low.
Methods: All asymptomatic patients (≤ 18 years) from January 1, 2015 to December 31, 2017 who underwent initial outpatient evaluation at our center and had a TTE ordered for an abnormal ECG, were included. Clinic records were reviewed to obtain ECG and TTE findings.
Results: Of the 199 study patients, 13 (6.5%) had abnormal findings. Incomplete… More >