Fatimah Abdulrahman Alrawajeh¹, F. M. Allehiany², Ali Raza^3,*, Shaimaa A. M. Abdelmohsen⁴, Tahir Nawaz Cheema⁵, Muhammad Rafiq⁶, Muhammad Mohsin⁷

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2357-2374, 2022, DOI:10.32604/cmc.2022.024604

Abstract In 2021, most of the developing countries are fighting polio, and parents are concerned with the disabling of their children. Poliovirus transmits from person to person, which can infect the spinal cord, and paralyzes the parts of the body within a matter of hours. According to the World Health Organization (WHO), 18 million currently healthy people could have been paralyzed by the virus during 1988–2020. Almost all countries but Pakistan, Afghanistan, and a few more have been declared polio-free. The mathematical modeling of poliovirus is studied in the population by categorizing it as susceptible individuals (S), exposed individuals (E), infected… More >

Rachel Gardner¹, Emily Durbak¹, Rachael Baird², Katherine Singh², Jeff Chapa², David Majdalany^3,*

Congenital Heart Disease, Vol.17, No.2, pp. 147-160, 2022, DOI:10.32604/chd.2022.017366

Abstract Background: There is limited literature written on the course and outcomes for pregnant mothers with Shone complex. Methods: We describe a case series of five pregnancies in four women with Shone complex within a multidisciplinary cardio-obstetrics clinic from 2016–2018. Results: Maternal age ranged from 21–39 years. Three patients had preserved left ventricular function while one had moderately decreased function. Gestational age at presentation ranged from 6–15 weeks. There were three successful pregnancies (mean gestational age = 37 weeks, range 35–39 weeks) with one patient accounting for two unsuccessful pregnancies. All infants were delivered via Cesarean section. One infant required a… More >

Fuad Ali Mohammed Al-Yarimi^*

Intelligent Automation & Soft Computing, Vol.32, No.3, pp. 1729-1742, 2022, DOI:10.32604/iasc.2022.022418

Abstract Computer-aided arrhythmia prediction from ECG (electrocardiograms) is essential in clinical practices, which promises to reduce the mortality caused by inexperienced clinical practitioners. Moreover, computer-aided methods often succeed in the early detection of arrhythmia scope from electrocardiogram reports. Machine learning is the buzz of computer-aided clinical practices. Particularly, computer-aided arrhythmia prediction methods highly adopted machine learning methods. However, the high dimensionality in feature values considered for the machine learning models’ training phase often causes false alarming. This manuscript addressed the high dimensionality in the learning phase and proposed an (Ensemble Learning method for Arrhythmia Prediction) ELAP (ensemble learning-based arrhythmia prediction). The… More >

S. Karthik¹, M. Santhosh^1,*, M. S. Kavitha¹, A. Christopher Paul²

Computer Systems Science and Engineering, Vol.42, No.1, pp. 183-199, 2022, DOI:10.32604/csse.2022.021698

Abstract Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to… More >

Zainab Arshad¹, Sohail Masood Bhatti^2,*, Huma Tauseef³, Arfan Jaffar²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1195-1205, 2022, DOI:10.32604/iasc.2022.022160

Abstract According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A… More >

Mohd Zubir Suboh^1,2, Nazrul Anuar Nayan^1,3,*, Noraidatulakma Abdullah^4,5, Nurul Ain Mhd Yusof⁴, Mariatul Akma Hamid⁴, Azwa Shawani Kamalul Arinfin⁴, Syakila Mohd Abd Daud⁴, Mohd Arman Kamaruddin⁴, Rosmina Jaafar¹, Rahman Jamal⁴

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1111-1132, 2022, DOI:10.32604/cmc.2022.022123

Abstract A comprehensive study was conducted to differentiate cardiovascular disease (CVD) subjects from non-CVD subjects using short recording electrocardiogram (ECG) of 244 Malaysian adults in The Malaysian Cohort project. An automated peak detection algorithm to detect nine fiducial points of electrocardiogram (ECG) was developed. Forty-eight features were extracted in both time and frequency domains, including statistical features obtained from heart rate variability and Poincare plot analysis. These include five new features derived from spectrum counts of five different frequency ranges. Feature selection was then made based on p-value and correlation matrix. Selected features were used as input for five classifiers of… More >

R. Krishnaswamy^1,*, B. Sivakumar², B. Viswanathan³, Fahd N. Al-Wesabi^4,5, Marwa Obayya⁶, Anwer Mustafa Hilal⁷

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 255-268, 2022, DOI:10.32604/cmc.2022.021995

Abstract Automatic biomedical signal recognition is an important process for several disease diagnoses. Particularly, Electrocardiogram (ECG) is commonly used to identify cardiovascular diseases. The professionals can determine the existence of cardiovascular diseases using the morphological patterns of the ECG signals. In order to raise the diagnostic accuracy and reduce the diagnostic time, automated computer aided diagnosis model is necessary. With the advancements of artificial intelligence (AI) techniques, large quantity of biomedical datasets can be easily examined for decision making. In this aspect, this paper presents an intelligent biomedical ECG signal processing (IBECG-SP) technique for CVD diagnosis. The proposed IBECG-SP technique examines… More >

Visvasam Devadoss Ambeth Kumar¹, Chetan Swarup², Indhumathi Murugan¹, Abhishek Kumar³, Kamred Udham Singh⁴, Teekam Singh⁵, Ramu Dubey^6,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 855-869, 2022, DOI:10.32604/cmc.2022.021582

Abstract Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is… More >

R. Sujatha¹, Jyotir Moy Chatterjee², NZ Jhanjhi³, Thamer A. Tabbakh⁴, Zahrah A. Almusaylim^5,*

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 115-129, 2022, DOI:10.32604/iasc.2022.019133

Abstract Heart failure (HF) is an intercontinental pandemic influencing in any event 26 million individuals globally and is expanding in commonness. HF healthiness consumptions are extensive and will increment significantly with a maturing populace. As per the World Health Organization (WHO), Cardiovascular diseases (CVDs) are the major reason for all-inclusive death, taking an expected 17.9 million lives per year. CVDs are a class of issues of the heart, blood vessels and include coronary heart sickness, cerebrovascular illness, rheumatic heart malady, and various other conditions. In the medical care industry, a lot of information is as often as possible created. Nonetheless, it… More >

ILGIZ GAREEV¹, OZAL BEYLERLI¹, CHUNLEI WANG^2,3, ANDREI SOKHATSKII^2,3, YANCHAO LIANG^2,3, HUAN XIANG^2,3, CHUNYANG LIU^2,3, XUN XU^2,3, YANG GUANG^2,3

BIOCELL, Vol.46, No.1, pp. 27-35, 2022, DOI:10.32604/biocell.2022.016288

Abstract Brain arteriovenous malformations (AVMs) are abnormal vessels that are prone to rupture, causing life-threatening intracerebral hemorrhage (ICH). Understanding the molecular basis of pathogenesis, timely diagnosis, and treatment of brain AVMs are some of the urgent problems in neurosurgery. MicroRNAs (miRNAs) are small endogenous RNAs that regulate gene-expression posttranscriptionally. MiRNAs are involved in almost all biological processes, including cell proliferation, apoptosis, and cell differentiation. Recent studies have shown that miRNAs can be involved in brain AVMs formation and rupture. There are also extracellular forms of miRNAs. Circulating miRNAs have been detected in the blood circulation and other body fluids. Owing to… More >