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Heart failure
Published in Henry J. Woodford, Essential Geriatrics, 2022
An electrocardiogram (ECG) will usually be abnormal with signs of previous ischaemia or chamber enlargement (seeFigure 17.1). A chest X-ray may show ventricular enlargement, oedema, effusions, Kerley lines or venous congestion. Thyroid disease and anaemia should be excluded by standard blood tests. Renal blood tests should be performed. Serum albumin should be measured to exclude hypalbuminaemia as a contributory cause of oedema. Consider HbA1C testing to exclude co-morbid diabetes.
Performance of Diverse Machine Learning Algorithms for Heart Disease Prognosis
Published in Ayodeji Olalekan Salau, Shruti Jain, Meenakshi Sood, Computational Intelligence and Data Sciences, 2022
Dhruv Kaliraman, Gauri Kamath, Suchitra Khoje, Prajakta Pardeshi
Heart failure is the prime cause of death. It is one of the most chronic illnesses, and it can lead to disabilities and pose financial problems to patients. As per World Health Organization records, 17.5 million individuals die every year from cardiovascular disease [1]. The prognosis of heart disease is challenging for doctors as some of the symptoms experienced can be related to other illnesses or may be indicators of aging [2]. When the arteries of the heart lose the ability to transport blood that is rich in oxygen, heart disease is likely to occur. A common cause is plaque buildup in the lining of larger coronary arteries. It may partially or entirely block the blood flow in the heart’s large arteries. This condition may occur as a result of an illness or accident that changes the way the heart arteries function [3]. Electrocardiogram (ECG), Holter screening, echocardiogram, stress examination, cardiac catheterization, cardiac computerized tomography (CT) scan, and cardiac magnetic resonance imaging are some of the medical tests that doctors and experts run to detect cardiovascular disease [4].
Pre-Procedural Risk Assessment and Optimization
Published in Vikram S. Kashyap, Matthew Janko, Justin A. Smith, Endovascular Tools & Techniques Made Easy, 2020
Sami Kishawi, Matthew Janko, Vikram S. Kashyap, Teresa Carman
Electrocardiogram (ECG): An ECG (often called EKG) is a non-invasive, inexpensive, and painless way to assess basic cardiac status. It is reasonable to establish a baseline ECG among any patient deemed to be at moderate to high risk. An ECG should be obtained to identify interval clinical changes if one is not available from the prior 6 months, or if any event has occurred since the most recent cardiac study. In the pre-procedural setting, ECGs are typically performed at rest and carry little value outside of providing additional context to a patient's baseline status (2). However, the ECG may be highly valuable post-procedure if there is a concern for cardiac event (3, 4).
Comparison of different QT correction methods for nonclinical safety assessment in ketamine-anesthetized Indian rhesus monkeys (Macaca mulatta)
Published in Toxicology Mechanisms and Methods, 2023
Laxit K. Bhatt, Chitrang R. Shah, Rajesh J. Patel, Shital D. Patel, Sudhir R. Patel, Vipul A. Patel, Jitendra H. Patel, Pankaj Dwivedi, Niraj A. Shah, Rajesh S. Sundar, Mukul R. Jain
Electrocardiograms (ECGs) are an important noninvasive tool for the diagnosis of cardiovascular diseases such as myocardial infarction, cardiomyopathy and cardiac arrhythmias. Torsades de Pointes (TdP) is an important cardiac arrhythmia type of concern that can lead to sudden death. TdP, a polymorphic ventricular tachycardia, is characterized by a long QT interval. Lengthening of QT interval causes prolongation of repolarization followed by early after-depolarizations, ultimately, leading to arrhythmias. Drug-induced TdP is caused by certain classes of antiarrhythmic drugs (Class Ia, Ic or III), tricyclic antidepressants, certain antivirals and antifungals (Yap and Camm 2003; Al-Khatib et al. 2003; Li and Ramos 2017). The ability of a drug to induce TdP in humans is difficult to predict in animal studies (Chaves et al. 2006; Guth 2007). Hence, electrocardiographic evaluation of QT interval prolongation is used as an indicator of arrhythmic risk in preclinical testing (Guideline ICH S7A 2001; Guideline ICH S7B 2005).
A review of arrhythmia detection based on electrocardiogram with artificial intelligence
Published in Expert Review of Medical Devices, 2022
Jinlei Liu, Zhiyuan Li, Yanrui Jin, Yunqing Liu, Chengliang Liu, Liqun Zhao, Xiaojun Chen
Electrocardiogram (ECG) is one of the recommended techniques for detecting arrhythmia. The complete ECG heartbeat contains three key waves: P-wave, QRS-wave, and T-wave, as shown in Figure 1. Different types of arrhythmia correspond to various wave shapes and periods [4], and an experienced cardiologist can quickly make a diagnosis based on the ECG. The widespread application of various ECG devices, especially wearable ECG monitors, has resulted in a large number of ECGs waiting to be diagnosed. However, it is a time-consuming and challenging task to perform arrhythmia analysis on ECG manually. The rapid application of artificial intelligence (AI) technology provides new opportunities for automatic detection of arrhythmia and assists physicians in accurately diagnosing cardiac diseases. Meanwhile, AI methods have also been applied to other physiological signals, such as Electromyogram (EMG), Electroencephalogram (EEG), and Electrooculogram (EOG) [5]. It is comparatively convenient to obtain ECG and the theory of ECG diagnosis of cardiac disease is relatively well established. Aiming at thousands of ECG data, AI methods can automatically learn the inherent patterns between features to achieve rapid and reliable disease classification [6]. Hence, the investigation and application of arrhythmia detection based on AI are of great importance.
Genetic particle filter improved fuzzy-AEEMD for ECG signal de-noising
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Electrocardiogram (ECG) signal records the electrical conduction activity of heart. These are very small signals in strength with narrow bandwidth of 0.05–120 Hz. Physicians especially cardiologists use these signals for diagnosis of the heart’s condition or heart diseases. ECG signal is contaminated with various artifacts such as power-line interference (PLI), Patient-electrode motion artifacts, electrode-pop or contact noise, and baseline wandering and electromyographic (EMG) noise during acquisition. Analysis of ECG signals becomes difficult to inspect the cardiac activity in the presence of such unwanted signals. So, de-noising of ECG signal is extremely important to prevent misinterpretation of patient’s cardiac activity. Various methods are available for de-noising the ECG signal such as hybrid technique (Pradeep Kumar et al. 2009), empirical mode decomposition (Chacko and Ari 2012), un-decimated wavelet transform (Naga Prudhvi Raj and Venkateswarlu 2011), Hilbert-Hung transform (Zhang et al. 2010), morphological filtering (Liu et al. 2011), noise invalidation techniques (Nikvand et al. 2010), non-local means technique (Tracey and Miller 2012), S-transform (Alarka et al. 2012), adaptive filtering (Ying and An 2011), and FIR filtering (Ying and An 2011), etc.