Search results for: heart disease

Authors: Mahdi Farajzadeh Ajirlou

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Prior evidence of normal cardiac anatomy is desirable to relieve the anxiety of cases with a family history of congenital heart disease or to offer the option of early gestation termination or close follow-up should a cardiac anomaly be proved. Fetal heart discovery plays an important part in the opinion of the fetus, and it can reflect the fetal heart function of the fetus, which is regulated by the central nervous system. Acquisition of ventricular volume and inflow data would be useful to quantify more valve regurgitation and ventricular function to determine the degree of cardiovascular concession in fetal conditions at threat for hydrops fetalis. This study discusses imaging the fetal heart with transvaginal ultrasound, Doppler ultrasound, three-dimensional ultrasound (3DUS) and four-dimensional (4D) ultrasound, spatiotemporal image correlation (STIC), glamorous resonance imaging and cardiac catheterization. Doppler ultrasound (DUS) image is a kind of real- time image with a better imaging effect on blood vessels and soft tissues. DUS imaging can observe the shape of the fetus, but it cannot show whether the fetus is hypoxic or distressed. Spatiotemporal image correlation (STIC) enables the acquisition of a volume of data concomitant with the beating heart. The automated volume accession is made possible by the array in the transducer performing a slow single reach, recording a single 3D data set conforming to numerous 2D frames one behind the other. The volume accession can be done in a stationary 3D, either online 4D (direct volume scan, live 3D ultrasound or a so-called 4D (3D/ 4D)), or either spatiotemporal image correlation-STIC (off-line 4D, which is a circular volume check-up). Fetal cardiovascular MRI would appear to be an ideal approach to the noninvasive disquisition of the impact of abnormal cardiovascular hemodynamics on antenatal brain growth and development. Still, there are practical limitations to the use of conventional MRI for fetal cardiovascular assessment, including the small size and high heart rate of the mortal fetus, the lack of conventional cardiac gating styles to attend data accession, and the implicit corruption of MRI data due to motherly respiration and unpredictable fetal movements. Fetal cardiac MRI has the implicit to complement ultrasound in detecting cardiovascular deformations and extracardiac lesions. Fetal cardiac intervention (FCI), minimally invasive catheter interventions, is a new and evolving fashion that allows for in-utero treatment of a subset of severe forms of congenital heart deficiency. In special cases, it may be possible to modify the natural history of congenital heart disorders. It's entirely possible that future generations will ‘repair’ congenital heart deficiency in utero using nanotechnologies or remote computer-guided micro-robots that work in the cellular layer.

Keywords: fetal, cardiac MRI, ultrasound, 3D, 4D, heart disease, invasive, noninvasive, catheter

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

Procedia PDF Downloads 348

Authors: Gehan Hussein, Hams Ahmad, Baher Matta, Yasmeen Mansi, Mohamad Fawzi

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Background: Congenital heart disease (CHD) is a common problem worldwide with variable incidence in different countries. The exact etiology is unknown, suggested to be multifactorial. We aimed to study the incidence of various CHD in a neonatal intensive care unit (NICU) in a tertiary care hospital in Egypt and the possible associations with variable risk factors. Methods: Prospective study was conducted over a period of one year (2013 /2014) at NICU KasrAlAini School of Medicine, Cairo University. Questionnaire about possible maternal and/or paternal risk factors for CHD, clinical examination, bedside echocardiography were done. Cases were classified into groups: group 1 without CHD and group 2 with CHD. Results: from 723 neonates admitted to NICU, 180 cases were proved to have CHD, 58 % of them were males. patent ductus arteriosus(PDA) was the most common CHD (70%), followed by an atrial septal defect (ASD8%), while Fallot tetralogy and single ventricle were the least common (0.45 %) for each. CHD was found in 30 % of consanguineous parents Maternal age ≥ 35 years at the time of conception was associated with increased incidence of PDA (p= 0.45 %). Maternal diabetes and insulin intake were significantly associated with cases of CHD (p=0.02 &0.001 respectively), maternal hypertension and hypothyroidism were both associated with VSD, but the difference did not reach statistical significance (P=0.36 &0.44respectively). Maternal passive smoking was significantly associated with PDA (p=0.03). Conclusion: The most frequent CHD in the studied population was PDA, followed by ASD. Maternal conditions as diabetes was associated with VSD occurrence.

Keywords: NICU, risk factors, congenital heart disease, echocardiography

Procedia PDF Downloads 190

Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Onder Yakut, Oguzhan Timus, Emine Dogru Bolat

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Health diseases have a vital significance affecting human being's life and life quality. Sudden death events can be prevented owing to early diagnosis and treatment methods. Electrical signals, taken from the human being's body using non-invasive methods and showing the heart activity is called Electrocardiogram (ECG). The ECG signal is used for following daily activity of the heart by clinicians. Heart Rate Variability (HRV) is a physiological parameter giving the variation between the heart beats. ECG data taken from MITBIH Arrhythmia Database is used in the model employed in this study. The detection of arrhythmic heart beats is aimed utilizing the features extracted from the HRV time domain parameters. The developed model provides a satisfactory performance with ~89% accuracy, 91.7 % sensitivity and 85% specificity rates for the detection of arrhythmic beats.

Keywords: arrhythmic beat detection, ECG, HRV, kNN classifier

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Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Mende Mensa Sorato, Majid Davari, Abbas Kebriaeezadeh, Nizal Sarrafzadegan, Tamiru Shibru, Behzad Fatemi

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Markov model, like cardiovascular disease (CVD) policy model based simulation, is being used for evaluating the cost-effectiveness of hypertension treatment. Stroke, angina, myocardial infarction (MI), cardiac arrest, and all-cause mortality were included in this model. Hypertension is a risk factor for a number of vascular and cardiac complications and CVD outcomes. Objective: This systematic review was conducted to evaluate the comprehensiveness of this model across different regions globally. Methods: We searched articles written in the English language from PubMed/Medline, Ovid/Medline, Embase, Scopus, Web of Science, and Google scholar with a systematic search query. Results: Thirteen cohort studies involving a total of 2,165,770 (1,666,554 hypertensive adult population and 499,226 adults with treatment-resistant hypertension) were included in this scoping review. Hypertension is clearly associated with coronary heart disease (CHD) and stroke mortality, unstable angina, stable angina, MI, heart failure (HF), sudden cardiac death, transient ischemic attack, ischemic stroke, subarachnoid hemorrhage, intracranial hemorrhage, peripheral arterial disease (PAD), and abdominal aortic aneurism (AAA). Association between HF and hypertension is variable across regions. Treatment resistant hypertension is associated with a higher relative risk of developing major cardiovascular events and all-cause mortality when compared with non-resistant hypertension. However, it is not included in the previous CVD policy model. Conclusion: The CVD policy model used can be used in most regions for the evaluation of the cost-effectiveness of hypertension treatment. However, hypertension is highly associated with HF in Latin America, the Caribbean, Eastern Europe, and Sub-Saharan Africa. Therefore, it is important to consider HF in the CVD policy model for evaluating the cost-effectiveness of hypertension treatment in these regions. We do not suggest the inclusion of PAD and AAA in the CVD policy model for evaluating the cost-effectiveness of hypertension treatment due to a lack of sufficient evidence. Researchers should consider the effect of treatment-resistant hypertension either by including it in the basic model or during setting the model assumptions.

Keywords: cardiovascular disease policy model, cost-effectiveness analysis, hypertension, systematic review, twelve major cardiovascular events

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Authors: Y. S. Yusoff, G. Streftaris, H. R Waters

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Death within 30 days is an important factor to be looked into, as there is a significant risk of deaths immediately following or soon after, Myocardial Infarction (MI) or stroke. In this paper, we will model the deaths within 30 days following a Myocardial Infarction (MI) or stroke in the UK. We will see how the probabilities of sudden deaths from MI or stroke have changed over the period 1981-2000. We will model the sudden deaths using a Generalized Linear Model (GLM), fitted using the R statistical package, under a Binomial distribution for the number of sudden deaths. We parameterize our model using the extensive and detailed data from the Framingham Heart Study, adjusted to match UK rates. The results show that there is a reduction for the sudden deaths following a MI over time but no significant improvement for sudden deaths following a stroke.

Keywords: sudden deaths, myocardial infarction, stroke, ischemic heart disease

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Authors: Anam Naveed, Asma Andleeb, Mehreen Sirshar

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In the article, Image processing techniques have been applied on cardiac images for enhancing the image quality. Two types of methodologies considers for survey, invasive techniques and non-invasive techniques. Different image processes for improvement of cardiac image quality and reduce the amount of radiation exposure for invasive techniques are explored. Different image processing algorithms for enhancing the noninvasive cardiac image qualities are described. Beside these two methodologies, third methodology has applied on live streaming of heart rate on ECG window for extracting necessary information, removing noise and enhancing quality. Sensitivity analyses have been carried out to investigate the impacts of cardiac images for diagnosis of cardiac arteries disease and how the enhancement on images will help the cardiologist to diagnoses disease. The paper evaluates strengths and weaknesses of different techniques applied for improved the image quality and draw a conclusion. Some specific limitations must be considered for whole survey, like the patient heart beat must be 70-75 beats/minute while doing the angiography, similarly patient weight and exposure radiation amount has some limitation.

Keywords: cardiac images, CT angiography, critical analysis, exposure radiation, invasive techniques, invasive techniques, non-invasive techniques

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Authors: Hafid Nadia, Meziane Toufik

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The aim of this study was the characterization of the goat meat by the determination of quantity and the quality in Batna region. The first part was the evaluation of production and consumption. The investigations show that the goat meat third after mutton and beef, it’s especially consumed by the indigenous population located in the Mountain and steep area. The second part of this review treats nutritional quality of this meat by the quantification of the chemical composition, including fat profile, and establishes a link between animal age and the values of these parameters. Moisture, fat contents, and cholesterol levels varied with age. Because of the decreasing level of cholesterol in the Chevon meat, it is more recommended for consumption to prevent or reduce the incidence of coronary disease and heart attack.

Keywords: biochemical composition, cholesterol, goat meat, heart attack

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Authors: Murtadha Kareem, Oliver Faust

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Wearable biosensor systems can be designed and developed for health monitoring. There is much interest in both scientific and industrial communities established since 2007. Fundamentally, the cost of healthcare has increased dramatically and the world population is aging. That creates the need to harvest technological improvements with small bio-sensing devices, wireless-communication, microelectronics and smart textiles, that leads to non-stop developments of wearable sensor based systems. There has been a significant demand to monitor patient's health status while the patient leaves the hospital in his/her personal environment. To address this need, there are numerous system prototypes which has been launched in the medical market recently, the aim of that is to provide real time information feedback about patient's health status, either to the patient himself/herself or direct to the supervising medical centre station, while being capable to give a notification for the patient in case of possible imminent health threatening conditions. Furthermore, wearable health monitoring systems comprise new techniques to address the problem of managing and monitoring chronic heart diseases for elderly people. Wearable sensor systems for health monitoring include various types of miniature sensors, either wearable or implantable. To be specific, our proposed system able to measure essential physiological parameter, such as heart rate signal which could be transmitted through Bluetooth to the cloud server in order to store, process, analysis and visualise the data acquisition. The acquired measurements are connected through internet of things to a central node, for instance an android smart phone or tablet used for visualising the collected information on application or transmit it to a medical centre.

Keywords: Wearable sensor, Heart rate, Internet of things, Chronic heart disease

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Authors: Farhad Fakoursevom

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Heart disease is one of the diseases which is found in abundance today. Various types of surgeries, such as bypasses, angiography, angioplasty, etc., are used to treat patients. People may receive such surgeries, some of which are invasive and some non-invasive, throughout their lives. People might cope with pre-surgery anxiety and stress, which can disrupt their normal life and even reduce the effects of the surgery, so the desired result can not be achieved in surgery. Considering this issue, the present study aimed to do a comparative assessment of people who received sedatives before surgery and people who did not receive sedatives. In terms of the purpose, this is an applied research and descriptive survey in terms of method. The statistical population included patients who underwent surgeries in the specialist heart hospitals of Mashhad, Iran; 60 people were considered as a statistical population, 30 of them received sedatives before surgery, and 30 others had not received sedatives before surgery. Valid and up-to-date articles were systematically used to collect theoretical bases, and a researcher-made questionnaire was used to examine the level of stress and anxiety of people. The questionnaire content validity was assessed by a panel of experts in psychology and medicine. The construct validity was tested using the software. Cronbach's alpha and composite reliability were used for reliability, which shows the appropriate reliability of the questionnaire. SPSS software was used to compare the research results between two groups, and the research findings showed that there is no significant association between the people who received sedatives and those who did not receive sedatives in terms of the amount of stress and anxiety. The longer the time of taking the drugs before the surgery, the more the mental peace of the patients will be. According to the results, it can be said that if we don't need to have an emergency operation and need more time, we have to use sedative drugs with different doses compared to the severity of the surgery, and also in case of a medical emergency such as heart surgery due to a stroke, we have to take advantage of psychological services during and before the operation and sedative drugs so that the patients can control their stress and anxiety and achieve better outcomes.

Keywords: sedative drugs, stress, anxiety, surgery

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Authors: Tarnai Tena, Strinić Dean

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Chronic non-communicable diseases are today the leading cause of mortality, morbidity and mortality disability at the world level and in Croatia. Among them are the most represented precisely cardiovascular diseases (CVD), so today we are talking about their global card epidemic. From 2018 to 2021, cardiovascular diseases are the leading cause of death for both women and men in the Dubrovnik- Neretva County. With regard to the COVID-19 pandemic, which has taken over, without forgetting how much these patients are additionally affected, we are still talking about the primary cause of sickness and death in the population of this county and region. In this record, we present collected data processed according to gender and disease classification. We also bring a kind of overview because, for years, we have been following how the population of one of the origins of the Mediterranean diet has been struggling with cardiovascular diseases.

Keywords: cardiovascular disease, burden, COVID-19, epidemiology, ishemic heart disease, cardiovascular medicine

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Authors: Sihyun Chae, Ryoto Arai, Waldo Concepcion, Paula Popescu

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The kidneys perform an important role in the human hormones that regulate the blood pressure, produce an active form of vitamin D and control the production of red blood cells. Kidney disease can cause health problems, such as heart disease. Also, increase the chance of having a stroke or heart attack. There are mainly to types of treatments for kidney disease, dialysis, and kidney transplant. For a better quality of life, the kidney transplant is desirable. However, kidney transplant can cause antibody reaction and patients’ body would be attacked by immune system of their own. For solving that issue, patients with transplanted kidney always take immunosuppressive drugs which can hurt kidney as side effects. Patients willing to do a kidney transplant have a waiting time of 3.6 years in average searching to find an appropriate kidney, considering there are almost 96,380 patients waiting for kidney transplant. There is a promising method to solve these issues: bioartificial kidney. Our membrane is specially designed with unique perforations capable to filter the blood cells separating the white blood cells from red blood cells. White blood cells will not pass through the encapsulated kidney preventing the immune system to attack the new organ and eliminating the need of a matching donor. It is possible to construct life-time long encapsulation without needing pumps or a power supply on the cell’s separation method preventing futures surgeries due the Cross-Channel Flow inside the device. This technology allows the possibility to use an animal kidney, prevent cancer cells to spread through the body, arm and leg transplants in the future. This project aims to improve the quality of life of patients with kidney disease.

Keywords: kidney encapsulation, immunosuppression filter, leukocyte filter, leukocyte

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Authors: S. P. Chintan, A. M. Khoja, M. Modi, R. K. Chopra, S. Garde, D. Jain, O. Kajale

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Pulmonary Hypertension is a rare but debilitating disease that affects individuals of all ages and walks of life. As recent as 15 years ago, a patient diagnosed with PH was given an average survival rate of 2.8 years. Recent advances in treatment options have allowed patients to improve quality o and quantity of life. Initial screening for PH is through echocardiography with final diagnosis confirmed through right heart catheterization. PH is now considered to have five major classifications with subgroups among each. The mild to moderate PH is common in chronic lung diseases like Chronic obstructive pulmonary diseases and Interstitial lung disease. But very severe PH is noted in few cases. In COPD patients, PH is associated with an increased risk of severe exacerbations and a reduced life expectancy. Similarly, in patients with ILD, the presence of PH correlates with a poor prognosis. Early diagnosis is essential to slow disease progression. We report here five cases of severe PH (Out of Proportion) of which four cases were of COPD and another one of IPF (UIP pattern). There echocardiography showed gross RA/RV dilatation, interventricular septum bulging to the left and mPAP of more than 100 mmHg in all the five cases. These patients were put on LTOT, pulmonary rehabilitation, combination pharmacotherapy of vasodilators and diuretics in continuation to the treatment of underlying disease. As these patients have grave prognosis close monitoring and follow up is required. Physicians associated with respiratory care and treating chronic lung disease should have knowledge in the diagnosis and management of patients with PH.

Keywords: COPD, pulmonary hypertension, chronic lung disease, India

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Authors: Santanu Chattopadhyay, Gautam Sarkar, Arabinda Das

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This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification.

Keywords: arrhythmia, congestive heart failure, discrete wavelet transform, electrocardiogram, myocardial ischemia, sleep apnea

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Authors: Goran Begović

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In recent years, many researchers are focusing on non-intrusive measuring methods when it comes to human biosignals. These methods provide solutions for everyday use, whether it’s health monitoring or finessing the workout routine. One of the biggest issues with these solutions is that the sensors’ accuracy is highly variable due to many factors, such as ambiental light, skin color diversity, etc. That is why we wanted to explore different outcomes under those kinds of circumstances in order to find the most optimal algorithm(s) for extracting heart rate (HR) information. The optimization of such algorithms can benefit the wider, cheaper, and safer application of home health monitoring, without having to visit medical professionals as often when it comes to observing heart irregularities. In this study, we explored the accuracy of infrared (IR), red, and green LED sensorics in a controlled environment and compared the results with a medically accurate ECG monitoring device.

Keywords: data science, ECG, heart rate, holter monitor, LED sensors

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Authors: D. Šedivý, S. Fialová

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The main purpose of this study is to show differences between the numerical solution of the flow through the artificial heart valve using Newtonian or non-Newtonian fluid. The simulation was carried out by a commercial computational fluid dynamics (CFD) package based on finite-volume method. An aortic bileaflet heart valve (Sorin Bicarbon) was used as a pattern for model of real heart valve replacement. Computed tomography (CT) was used to gain the accurate parameters of the valve. Data from CT were transferred in the commercial 3D designer, where the model for CFD was made. Carreau rheology model was applied as non-Newtonian fluid. Physiological data of cardiac cycle were used as boundary conditions. Outputs were taken the leaflets excursion from opening to closure and the fluid dynamics through the valve. This study also includes experimental measurement of pressure fields in ambience of valve for verification numerical outputs. Results put in evidence a favorable comparison between the computational solutions of flow through the mechanical heart valve using Newtonian and non-Newtonian fluid.

Keywords: computational modeling, dynamic mesh, mechanical heart valve, non-Newtonian fluid

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Authors: Madeleine Nicole G. Bernardo, Katrina T. Feliciano, Marcelo Nonato A. Nacionales III, Diane Frances M. Peralta, Denise Nicole V. Profeta

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This study aims to find out if heart rate variability and valence of emotion have an effect on perceived intensity of emotion. Psychology undergraduates (N = 60) from the University of the Philippines Diliman were shown 10 photographs from the Japanese Female Facial Expression (JAFFE) Database, along with a corresponding questionnaire with a Likert scale on perceived intensity of emotion. In this 3 x 2 mixed subjects factorial design, each group was either made to do a simple exercise prior to answering the questionnaire in order to increase the heart rate, listen to a heart rate of 120 bpm, or colour a drawing to keep the heart rate stable. After doing the activity, the participants then answered the questionnaire, providing a rating of the faces according to the participants’ perceived emotional intensity on the photographs. The photographs presented were either of positive or negative emotional valence. The results of the experiment showed that neither an induced fast heart rate or perceived fast heart rate had any significant effect on the participants’ perceived intensity of emotion. There was also no interaction effect of heart rate variability and valence of emotion. The insignificance of results was explained by the Philippines’ high context culture, accompanied by the prevalence of both intensely valenced positive and negative emotions in Philippine society. Insignificance in the effects were also attributed to the Cannon-Bard theory, Schachter-Singer theory and various methodological limitations.

Keywords: heart rate variability, perceived intensity of emotion, Philippines , valence of emotion

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Authors: Chia-Pei Chen, Yu-Ju Chen, Yu-Juei Hsu

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The prevalence and incidence of end-stage renal disease in Taiwan ranks the highest in the world. According to the statistical survey of the Ministry of Health and Welfare in 2019, kidney disease is the ninth leading cause of death in Taiwan. It leads to autonomic dysfunction, inflammatory response and symptom distress, and further increases the damage to the structure and function of the kidneys, leading to increased demand for renal replacement therapy and risks of cardiovascular disease, which also has medical costs for the society. If we can intervene in a feasible manual to effectively regulate the autonomic nerve function of CKD patients, reduce the inflammatory response and symptom distress. To prolong the progression of the disease, it will be the main goal of caring for CKD patients. This study aims to test the effect of heart rate variability biofeedback (HRVBF) on improving autonomic nerve function (Heart Rate Variability, HRV), inflammatory response (Interleukin-6 [IL-6], C reaction protein [CRP] ), symptom distress (Piper fatigue scale, Pittsburgh Sleep Quality Index [PSQI], and Beck Depression Inventory-II [BDI-II] ) in patients with chronic kidney disease. This study was experimental research, with a convenience sampling. Participants were recruited from the nephrology clinic at a medical center in northern Taiwan. With signed informed consent, participants were randomly assigned to the HRVBF or control group by using the Excel BINOMDIST function. The HRVBF group received four weekly hospital-based HRVBF training, and 8 weeks of home-based self-practice was done with StressEraser. The control group received usual care. We followed all participants for 3 months, in which we repeatedly measured their autonomic nerve function (HRV), inflammatory response (IL-6, CRP), and symptom distress (Piper fatigue scale, PSQI, and BDI-II) on their first day of study participation (baselines), 1 month, and 3 months after the intervention to test the effects of HRVBF. The results were analyzed by SPSS version 23.0 statistical software. The data of demographics, HRV, IL-6, CRP, Piper fatigue scale, PSQI, and BDI-II were analyzed by descriptive statistics. To test for differences between and within groups in all outcome variables, it was used by paired sample t-test, independent sample t-test, Wilcoxon Signed-Rank test and Mann-Whitney U test. Results: Thirty-four patients with chronic kidney disease were enrolled, but three of them were lost to follow-up. The remaining 31 patients completed the study, including 15 in the HRVBF group and 16 in the control group. The characteristics of the two groups were not significantly different. The four-week hospital-based HRVBF training combined with eight-week home-based self-practice can effectively enhance the parasympathetic nerve performance for patients with chronic kidney disease, which may against the disease-related parasympathetic nerve inhibition. In the inflammatory response, IL-6 and CRP in the HRVBF group could not achieve significant improvement when compared with the control group. Self-reported fatigue and depression significantly decreased in the HRVBF group, but they still failed to achieve a significant difference between the two groups. HRVBF has no significant effect on improving the sleep quality for CKD patients.

Keywords: heart rate variability biofeedback, autonomic nerve function, inflammatory response, symptom distress, chronic kidney disease

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Authors: Induja R. Nimma, Anand Reddy Maligireddy, Artur Schneider, Melissa Lyle

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Background: Although heart failure is one of the most common causes of hospitalization in adult patients, there is limited knowledge on outcomes following initial hospitalization for COVID-19 with heart failure (HCF-19). We felt it pertinent to analyze 30-day readmission causes and outcomes among patients with HCF-19 using the United States using real-world big data via the National readmission database. Objective: The aim is to describe the rate and causes of readmissions and morbidity of heart failure with coinciding COVID-19 (HFC-19) in the United States, using the 2020 National Readmission Database (NRD). Methods: A descriptive, retrospective study was conducted on the 2020 NRD, a nationally representative sample of all US hospitalizations. Adult (>18 years) inpatient admissions with COVID-19 with HF and readmissions in 30 days were selected based on the International Classification of Diseases-Tenth Revision, Procedure Code. Results: In 2020, 2,60,372 adult patients were hospitalized with COVID-19 and HF. The median age was 74 (IQR: 64-83), and 47% were female. The median length of stay was 7(4-13) days, and the total cost of stay was 62,025 (31,956 – 130,670) United States dollars, respectively. Among the index hospital admissions, 61,527 (23.6%) died, and 22,794 (11.5%) were readmitted within 30 days. The median age of patients readmitted in 30 days was 73 (63-82), 45% were female, and 1,962 (16%) died. The most common principal diagnosis for readmission in these patients was COVID-19= 34.8%, Sepsis= 16.5%, HF = 7.1%, AKI = 2.2%, respiratory failure with hypoxia =1.7%, and Pneumonia = 1%. Conclusion: The rate of readmission in patients with heart failure exacerbations is increasing yearly. COVID-19 was observed to be the most common principal diagnosis in patients readmitted within 30 days. Complicated hypertension, chronic pulmonary disease, complicated diabetes, renal failure, alcohol use, drug use, and peripheral vascular disorders are risk factors associated with readmission. Familiarity with the most common causes and predictors for readmission helps guide the development of initiatives to minimize adverse outcomes and the cost of medical care.

Keywords: Covid-19, heart failure, national readmission database, readmission outcomes

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Authors: Kaouther Selmi, Alaeddine Sridi, Mohamed Bouallegue, Kais Bouallegue

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Neurons are the fundamental units of the brain and the nervous system. The variable structure model of neurons consists of a system of differential equations with various parameters. By optimizing these parameters, we can create a unique model that describes the dynamic behavior of a single neuron. We introduce a neural network based on neurons with multiple dendrites employing an activation function with a variable structure. In this paper, we present a model for heart behavior. Finally, we showcase our successful simulation of the heart's ECG diagram using our Variable Structure Neuron Model (VSMN). This result could provide valuable insights into cardiology.

Keywords: neural networks, neuron, dendrites, heart behavior, ECG

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Authors: Linda Saili, Amel Hanini, Chiraz Smirani, Iness Azzouz, Amina Azzouz, Hafedh Abdemelek, Zihad Bouslama

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Electrocardiogram and arterial pressure measurements were studied under acute exposures to WIFI (2.45 GHz) during one hour in adult male rabbits. Antennas of WIFI were placed at 25 cm at the right side near the heart. Acute exposure of rabbits to WIFI increased heart frequency (+ 22%) and arterial blood pressure (+14%). Moreover, analysis of ECG revealed that WIFI induced a combined increase of PR and QT intervals. By contrast, the same exposure failed to alter the maximum amplitude and P waves. After intravenously injection of dopamine (0.50 ml/kg) and epinephrine (0.50ml/kg) under acute exposure to RF we found that WIFI alter catecholamines(dopamine, epinephrine) action on heart variability and blood pressure compared to control. These results suggest for the first time, as far as we know, that exposure to WIFI affect heart rhythm, blood pressure, and catecholamines efficacy on cardiovascular system; indicating that radio frequency can act directly and/or indirectly on the cardiovascular system.

Keywords: heart rate (HR), arterial pressure (PA), electrocardiogram (ECG), the efficacy of catecholamines, dopamine, epinephrine

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Authors: Giulia Colonna, Jocelyn Hoye, Bart de Laat, Gelsina Stanley, Jose Key, Alaaddin Ibrahimy, Sule Tinaz, Evan D. Morris

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Parkinson’s disease (PD) is the second most common neurodegenerative disease and affects approximately 1% of the world’s population. Increasing evidence suggests that aerobic physical exercise can be beneficial in mitigating both motor and non-motor symptoms of the disease. In a recent pilot study of the role of exercise on PD, we sought to confirm exercise intensity by monitoring heart rate (HR). For this purpose, we asked participants to wear a chest strap heart rate monitor (Polar Electro Oy, Kempele). The device sometimes proved uncomfortable. Looking forward to larger clinical trials, it would be convenient to employ a more comfortable and user friendly device. The Fitbit Charge 4 (Fitbit Inc) is a potentially comfortable, user-friendly solution since it is a wrist-worn heart rate monitor. Polar H10 has been used in large trials, and for our purposes, we treated it as the gold standard for the beat-to-beat period (R-R interval) assessment. In previous literature, it has been shown that Fitbit Charge 4 has comparable accuracy to Polar H10 in healthy subjects. It has yet to be determined if the Fitbit is as accurate as the Polar H10 in subjects with PD or in clinical populations, generally. Goal: To compare the Fitbit Charge 4 to the Polar H10 for monitoring HR in PD subjects engaging in an intensive exercise program. Methods: A total of 596 exercise sessions from 11 subjects (6 males) were collected simultaneously by both devices. Subjects with early-stage PD (Hoehn & Yahr <=2) were enrolled in a 6 months exercise training program designed for PD patients. Subjects participated in 3 one-hour exercise sessions per week. They wore both Fitbit and Polar H10 during each session. Sessions included rest, warm-up, intensive exercise, and cool-down periods. We calculated the bias in the HR via Fitbit under rest (5min) and intensive exercise (20min) by comparing the mean HR during each of the periods to the respective means measured by the Polar (HRFitbit – HRPolar). We also measured the sensitivity and specificity of Fitbit for detecting HRs that exceed the threshold for intensive exercise, defined as 70% of an individual’s theoretical maximum HR. Different types of correlation between the two devices were investigated. Results: The mean bias was 1.68 bpm at rest and 6.29 bpm during high intensity exercise, with an overestimation by Fitbit in both conditions. The mean bias of Fitbit across both rest and intensive exercise periods was 3.98 bpm. The sensitivity of the device in identifying high intensity exercise sessions was 97.14 %. The correlation between the two devices was non-linear, suggesting a saturation tendency of Fitbit to saturate at high values of HR. Conclusion: The performance of Fitbit Charge 4 is comparable to Polar H10 for assessing exercise intensity in a cohort of PD subjects. The device should be considered a reasonable replacement for the more cumbersome chest strap technology in future similar studies of clinical populations.

Keywords: fitbit, heart rate measurements, parkinson’s disease, wrist-wearable devices

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Authors: Lokvendra S. Budania, Yogesh K Gaude, Vamsidhar Chamala

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Introduction: Congenitally corrected transposition of great arteries (CCTGA) is a complex congenital heart disease where there are both atrioventricular and ventriculoarterial discordances, usually accompanied by other cardiovascular malformations. Case Report: A 24-year-old primigravida known case of CCTGA at 37 weeks of gestation was referred to our hospital for safe delivery. Her electrocardiogram showed HR-40/pm, echocardiography showed Ejection Fraction of 65% and CCTGA. Temporary pacemaker was inserted by cardiologist in catheterization laboratory, before giving trial of labour in view of complete heart block. She was planned for normal delivery, but emergency Caesarean section was planned due to non-reassuring foetal Cardiotocography Pre-op vitals showed PR-50 bpm with temporary pacemaker, Blood pressure-110/70 mmHg, SpO2-99% on room air. Nil per oral was inadequate. Patency of two peripheral IV cannula checked and left radial arterial line secured. Epidural Anaesthesia was planned, and catheter was placed at L2-L3. Test dose was given, Anaesthesia was provided with 5ml + 5ml of 2% Lignocaine with 25 mcg Fentanyl and further 2.5Ml of 0.5% Bupivacaine was given to achieve a sensory level of T6. Cesarean section was performed and baby was delivered. Cautery was avoided during this procedure. IV Oxytocin (15U) was added to 500 mL of ringer’s lactate. Hypotension was treated with phenylephrine boluses. Patient was shifted to post-operative care unit and later to high dependency unit for monitoring. Post op vitals remained stable. Temporary pacemaker was removed after 24 hours of surgery. Her post-operative period was uneventful and discharged from hospital. Conclusion: Rare congenital cardiac disorders require detail knowledge of pathophysiology and associated comorbidities with the disease. Meticulously planned and carefully titrated neuraxial techniques will be beneficial for such cases.

Keywords: congenitally corrected transposition of great arteries, complete heart block, emergency LSCS, epidural anaesthesia

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Shital Patil, Surendra Bhosale

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Cardiac diseases are the leading causes of mortality and morbidity in the world, from recent few decades accounting for a large number of deaths have emerged as the most life-threatening disorder globally. Machine learning and Artificial intelligence have been playing key role in predicting the heart diseases. A relevant set of feature can be very helpful in predicting the disease accurately. In this study, we proposed a comparative analysis of 4 different features selection methods and evaluated their performance with both raw (Unbalanced dataset) and sampled (Balanced) dataset. The publicly available Z-Alizadeh Sani dataset have been used for this study. Four feature selection methods: Data Analysis, minimum Redundancy maximum Relevance (mRMR), Recursive Feature Elimination (RFE), Chi-squared are used in this study. These methods are tested with 8 different classification models to get the best accuracy possible. Using balanced and unbalanced dataset, the study shows promising results in terms of various performance metrics in accurately predicting heart disease. Experimental results obtained by the proposed method with the raw data obtains maximum AUC of 100%, maximum F1 score of 94%, maximum Recall of 98%, maximum Precision of 93%. While with the balanced dataset obtained results are, maximum AUC of 100%, F1-score 95%, maximum Recall of 95%, maximum Precision of 97%.

Keywords: cardio vascular diseases, machine learning, feature selection, SMOTE

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Authors: Caje F. Pinto, Jivan S. Parab, Gourish M. Naik

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This paper is about the development of non-invasive heart rate and oxygen saturation in human blood using Altera NIOS II soft-core processor system. In today's world, monitoring oxygen saturation and heart rate is very important in hospitals to keep track of low oxygen levels in blood. We have designed an Embedded System On Peripheral Chip (SOPC) reconfigurable system by interfacing two LED’s of different wavelengths (660 nm/940 nm) with a single photo-detector to measure the absorptions of hemoglobin species at different wavelengths. The implementation of the interface with Finger Probe and Liquid Crystal Display (LCD) was carried out using NIOS II soft-core system running on Altera NANO DE0 board having target as Cyclone IVE. This designed system is used to monitor oxygen saturation in blood and heart rate for different test subjects. The designed NIOS II processor based non-invasive heart rate and oxygen saturation was verified with another Operon Pulse oximeter for 50 measurements on 10 different subjects. It was found that the readings taken were very close to the Operon Pulse oximeter.

Keywords: heart rate, NIOS II, oxygen saturation, photoplethysmography, soft-core, SOPC

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Authors: Sanjaya Humagain, Rajendra Koju

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Background: Rheumatic heart disease (RHD) is the most common cardiovascular disease in children and young adults. Though declined and almost non-existent in developed nations, RHD is still one of the leading cause for premature death and disability in developing countries. Prevalence of RHD is high in both rural as well as urban area of Nepal. Present study is designed to look at the pattern of valvular involvement and demographic features in RHD. Methods: 326 patients indicated for inj. Benzathine penicillin were selected and echocardiograph performed to see the pattern of vavular involvement. Data analysis was done using SPSS 17. Result: The most common type of lesion was mixed type with mitral valve involvement. MR was the most common isolated lesion. MS was more commonly seen in females whereas AS was more common in males. Secondary prophylaxis was more common than primary prophylaxis. Conclusion: RHD still being a major problem and a preventable disease so extensive screening program is required to identify them early and prevent the complication.

Keywords: acute rheumatic fever, RHD, MS, MR, AS, AR, Inj benzathine penicillin

Procedia PDF Downloads 317