Muhammad Aleem Raza¹, Muhammad Anwar², Kashif Nisar³, Ag. Asri Ag. Ibrahim^3,*, Usman Ahmed Raza¹, Sadiq Ali Khan⁴, Fahad Ahmad⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3817-3834, 2023, DOI:10.32604/cmc.2023.032275

Abstract With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layer-based CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute (ANSI) standards and the Association… More >

Dengyong Zhang^1,2, Minzhi Yuan^1,2, Feng Li^1,2, Lebing Zhang^3,*, Yanqiang Sun⁴, Yiming Ling⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2809-2824, 2024, DOI:10.32604/cmes.2023.029181

Abstract Electrocardiogram (ECG) signal is one of the noninvasive physiological measurement techniques commonly used in cardiac diagnosis. However, in real scenarios, the ECG signal is susceptible to various noise erosion, which affects the subsequent pathological analysis. Therefore, the effective removal of the noise from ECG signals has become a top priority in cardiac diagnostic research. Aiming at the problem of incomplete signal shape retention and low signal-to-noise ratio (SNR) after denoising, a novel ECG denoising network, named attention-based residual dense shrinkage network (ARDSN), is proposed in this paper. Firstly, the shallow ECG characteristics are extracted by a shallow feature extraction network… More >

Premanand S., Sathiya Narayanan^*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 25-45, 2023, DOI:10.32604/cmc.2023.042590

Abstract Machine Learning (ML) and Deep Learning (DL) technologies are revolutionizing the medical domain, especially with Electrocardiogram (ECG), by providing new tools and techniques for diagnosing, treating, and preventing diseases. However, DL architectures are computationally more demanding. In recent years, researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches, say for example, combining the convolutional layer blocks of Convolution Neural Networks (CNNs) into ML algorithms such as Extreme Gradient Boosting (XGBoost) and K-Nearest Neighbor (KNN) resulting in CNN-XGBoost and CNN-KNN, respectively. However, these approaches are homogenous in the sense that they use a… More >

Maryam Bukhari¹, Sadaf Yasmin¹, Sheneela Naz², Mehr Yahya Durrani¹, Mubashir Javaid³, Jihoon Moon⁴, Seungmin Rho^5,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1251-1279, 2023, DOI:10.32604/cmc.2023.040329

Abstract Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics, which aid in the prevention of several diseases including heart-related abnormalities. In this context, regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram (ECG) signals has the potential to save many lives. In existing studies, several heart disease diagnostic systems are proposed by employing different state-of-the-art methods, however, improving such methods is always an intriguing area of research. Hence, in this research, a smart healthcare system is proposed for the diagnosis of heart disease using ECG signals. The proposed framework extracts both linear and… More >

Madallah Alruwaili^1,*, Muhammad Hameed Siddiqi¹, Kamran Farid², Mohammad Azad¹, Saad Alanazi¹, Asfandyar Khan², Abdullah Khan²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2293-2311, 2023, DOI:10.32604/csse.2023.040312

Abstract Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate, ECG, nasal/oral airflow, temperature, light sensor, and fall detection sensor. Different researchers have done different work in the field of health monitoring with sensor networks. Different researchers used built-in apps, such as some used a small number of parameters, while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate, and outdated tools for study development. While no efficient, cheap, and updated work is proposed in the field of sensor-based health monitoring systems. Therefore, this study developed… 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

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 with window size 10 is… More >

Md Saidul Islam^*

Journal on Internet of Things, Vol.4, No.3, pp. 127-140, 2022, DOI:10.32604/jiot.2022.037489

Abstract Internet of Things (IoT) technologies used in health have the potential to address systemic difficulties by offering tools for cost reduction while improving diagnostic and treatment efficiency. Numerous works on this subject focus on clarifying the constructs and interfaces between various components of an IoT platform, such as knowledge generation via smart sensors collecting biosignals from the human body and processing them via data mining and, in recent times, deep neural networks offered to host on cloud computing architecture. These approaches are intended to assist healthcare professionals in their daily activities. In this comparative research, we discuss the construction of… More >

Yixin Tang^*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1195-1212, 2023, DOI:10.32604/cmc.2023.039182

Abstract Visual illustration transformation from real-world to cartoon images is one of the famous and challenging tasks in computer vision. Image-to-image translation from real-world to cartoon domains poses issues such as a lack of paired training samples, lack of good image translation, low feature extraction from the previous domain images, and lack of high-quality image translation from the traditional generator algorithms. To solve the above-mentioned issues, paired independent model, high-quality dataset, Bayesian-based feature extractor, and an improved generator must be proposed. In this study, we propose a high-quality dataset to reduce the effect of paired training samples on the model’s performance.… More >

Hoon Ko¹, Chul Park², Wu Seong Kang³, Yunyoung Nam⁴, Dukyong Yoon⁵, Jinseok Lee^1,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2369-2381, 2023, DOI:10.32604/csse.2023.035641

Abstract Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For the model, this study extracted… More >

Eunchan Kim¹, Jaehyuk Kim², Juyoung Park³, Haneul Ko⁴, Yeunwoong Kyung^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1751-1764, 2023, DOI:10.32604/cmc.2023.031663

Abstract Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and… More >