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Nanodevices for Early Diagnosis of Cardiovascular Disease: Advances, Challenges, and Way Ahead
Published in Alok Dhawan, Sanjay Singh, Ashutosh Kumar, Rishi Shanker, Nanobiotechnology, 2018
Alok Pandya, Madhuri Bollapalli
Recently, heart-type fatty acid binding protein (H-FABP), a small (15 kDa) cytoplasmic protein, was found to be a very specific biomarker that is abundant in tissues with active fatty acid metabolism, including the heart (Das, 2016). Following ischemic injury, H-FABP is quickly released into the circulation via porous endothelial membranes of cardiac myocytes. Myocardial damage can be evaluated in plasma within 1–3 hours after symptom onset. Compared to troponins, the more specific but slower-release markers of cardiac injury, H-FABP is a useful tool for rapid evaluation of ischemic injury and size. The concentration of FABP in skeletal muscle is 20 times lower than that of cardiac tissue. Myoglobin occurs in the same content for cardiac and skeletal tissue. This makes H-FABP more cardiac specific than myoglobin, and a useful biochemical marker for the early assessment or exclusion of AMI. Previous studies have suggested that H-FABP can be used as a reliable marker for hypertrophic and dilated cardiomyopathy, heart failure, early estimation of infarct size, early detector of postoperative myocardial tissue loss in patients undergoing coronary bypass surgery, stroke, and obstructive sleep apnea syndrome. In recent years, different biosensor platforms have been designed for detection of available cardiac disease biomarkers. Here, we summarize the most prominently used cardiac biomarkers and their detection by different biosensor platforms.
Assessment of Myocardial Metabolism with Magnetic Resonance Spectroscopy
Published in Robert J. Gropler, David K. Glover, Albert J. Sinusas, Heinrich Taegtmeyer, Cardiovascular Molecular Imaging, 2007
It is becoming increasingly apparent that perturbations in myocardial substrate metabolism are key to the pathogenesis of a variety of cardiac disorders such as coronary artery disease, dilated cardiomyopathy, and diabetic heart disease. Radionuclide approaches such as positron emission tomography can provide quantitative measurements of myocardial oxygen, glucose, and fatty acid metabolism. However, radionuclide methods are limited by relatively low spatial resolution, incomplete characterization at the subsequent metabolic fates of extracted radiolabeled substrate and expensive complex technology requiring highly specialized personnel. NMR spectroscopy can provide highly sensitive and quantitative measurements of multiple metabolic processes nearly simultaneously. Indeed a number of different nuclei are NMR visible, including hydrogen, sodium, fluorine, phosphorus, and carbon, permitting the interrogation of diverse metabolic processes ranging from substrate uptake, turnover, intermediate formation, and storage to energy production. However, at the current time the ability to obtain many of these measurements, particularly non-invasively, is limited. In this chapter, both the current capabilities of NMR spectroscopy and its future potential for quantifying myocardial metabolism will be discussed. Emphasis will be placed on in vivo methods. In addition, for the purposes of this chapter, NMR spectroscopy will be called magnetic resonance spectroscopy or MRS.
IoHT with Wearable Devices–Based Feature Extraction and a Deep Neural Networks Classification Model for Heart Disease Diagnosis
Published in K. Shankar, Eswaran Perumal, Deepak Gupta, Artificial Intelligence for the Internet of Health Things, 2021
K. Shankar, Eswaran Perumal, Deepak Gupta
Internet of Things (IoT) concepts are employed in diverse fields transforming the way that business processes are made [1]. Health informatics is a promising multidisciplinary domain that focuses on employing information engineering concepts to healthcare. The information usually originates from a diversity of sources such as healthcare information technology systems, but lately it is being saved in distinct IoT devices [2, 3]. The application of IoT concepts is becoming the norm, giving rise to the Internet of Health Things (IoHT). In general, heart disease (HD) is said to be a more serious disease that affects the function of the human heart and tends to increase the chance of a coronary artery or lower blood vessel event. Such complications lead to a heart attack or stroke. Based on the study of [1], around 610,000 people are affected by HD in the United States. Although HD affects males and females, males are more positive for heart attacks. The study reveals that the signs of HD [2] are chest tightness, pain, pressure, breathing issues, leg chills, neck pain, abdominal pain, tachycardia, light headedness, bradycardia, dizziness, syncope, change in skin color, leg swelling, weight loss, and fatigue. Sometimes, the symptoms differ based on the nature of HD such as arrhythmia, myocardia, heart attack, congenital HD, mitral regurgitation, and dilated cardiomyopathy. Some of the risk factors involved in HD are age, genetics, smoking, sex habits, drug abuse, higher cholesterol, high BP, external inactivity, obesity, diabetes, stress, and poor diet and hygiene. The severity of HD requires the disease analyzing process to be focused on diagnosing at an early stage.
Arrhythmia detection by extracting hybrid features based on refined Fuzzy entropy (FuzEn) approach and employing machine learning techniques
Published in Waves in Random and Complex Media, 2020
Lal Hussain, Wajid Aziz, Sharjil Saeed, Imtiaz Ahmed Awan, Adeel Ahmed Abbasi, Neelum Maroof
The temporal dynamics are quantified using time domain methods such as standard deviation (SDNN) to measure the global variation, standard deviation of the average interval (SDANN) to measure the long-term variations, square root of mean squared differences of consecutive NN intervals (RMSSD) to evaluate short-term variations. The time domain techniques based on temporal variations have most widely been used in different application including heart rate variation and brain variations such as coronary artery disease [68,69] to diagnose the increased mortality risk in patients, dilated cardiomyopathy [70], congestive heart failure [71,72], and post infarction patients [73–75]. The brain dynamics and detecting epileptic seizures the automated methods in time domain [76–78], Fourier spectral analysis for extracting features [79], frequency domain methods [79], DWT-based methods [80], and fast-Fourier transforms [81,82] were employed.
Detecting congestive heart failure by extracting multimodal features with synthetic minority oversampling technique (SMOTE) for imbalanced data using robust machine learning techniques
Published in Waves in Random and Complex Media, 2022
Lal Hussain, Kashif Javed Lone, Imtiaz Ahmed Awan, Adeel Ahmed Abbasi, Jawad-ur-Rehman Pirzada
According to studies of [9,10], there are approximately 26 million people around the globe that are suffering from CHF. In this condition, heart cannot provide sufficient blood according to the requirements of the body, which reduce the ventricle ability of pumping blood [11]. Moreover, dilated cardiomyopathy, myocardial infarction, diseases related to heart valves [12], fatigue, edema, dyspnea are considered as most common and well-known symptoms for CHF. Patients suffering from CHF are more susceptive to sudden heart attack and even cardiac death [13]. However, the detection of CHF at early stages can increase the survival rate of the patients suffering from the disease.
Overview of Impella and mechanical devices in cardiogenic shock
Published in Expert Review of Medical Devices, 2018
Hymie Habib Chera, Menachem Nagar, Nai-Lun Chang, Carlos Morales-Mangual, George Dous, Jonathan D. Marmur, Muhammad Ihsan, Paul Madaj, Yitzhak Rosen
Acute decompensated heart failure (ADHF) is the leading cause of CS due to the abrupt impairment of cardiac function, and results in various signs and symptoms. Like most conditions, there are warning signs that can be managed early to prevent pernicious casualties. Atrial fibrillation or flutter, valvular disease, and dilated cardiomyopathy are more prevalent in patients with ADHF and patients usually have a history of coronary artery disease (60% cases), hypertension (70% cases), or impairment of renal system (20% to 30% cases) [2].