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Image Classification and Retrieval
Published in R. Suganya, S. Rajaram, A. Sheik Abdullah, Big Data in Medical Image Processing, 2018
R. Suganya, S. Rajaram, A. Sheik Abdullah
Signals can be used for the identification of diseases such as myocardial infarction which refers to the death of cardiac tissue (i.e., blood flow stops for a particular time), myocardial ischemia which occurs due to the lack of oxygen and leads to the coronary artery obstruction whereas synus rthyum represents the normal signal. Any abnormalities in the heart will be easily and instantly picked up by ECG so that treatment for the patients can be taken without any delay. RR interval determines the heart rate. ST elevation indicates that there is myocardial infarction whereas ST depression indicates that there is myocardial ischemia. Q wave inversion indicates old myocardial infarction. Location of this changes in lead I, lead II and lead III, avF, avL and avR, V1, V2, V3, V4, V5 and V6 indicates the location of the pathology whether it is an anterior wall, inferior wall, etc. in the heart.
Clinical Effects of Pollution
Published in William J. Rea, Kalpana D. Patel, Reversibility of Chronic Disease and Hypersensitivity, Volume 5, 2017
William J. Rea, Kalpana D. Patel
Increases in outdoor airborne carbon monoxide concentrations were found to significantly increase systolic and diastolic blood pressure in 48 healthy traffic controllers in Sao Paulo.599 A study of 56 German males with IHD found that exposure to PM2.5 was associated with significant decreases in T-wave amplitude on EKGs and significant increases in T-wave complexity.600 Exposure to organic carbon was associated with significant increases in QT wave duration.601 A Massachusetts study found that exposure to higher levels of black carbon was associated with significantly greater postexercise ST-depression in 24 elderly subjects.602 Such ST depression is often related to ischemia of myocardial tissue.
Evaluations of cardiovascular diseases with hybrid PET-CT imaging
Published in Yi-Hwa Liu, Albert J. Sinusas, Hybrid Imaging in Cardiovascular Medicine, 2017
Antti Saraste, Sami Kajander, Juhani Knuuti
Figure 15.1 shows an example of microvascular disease in a 59-year-old man with risk factors of family history and smoking for CAD detected by PET-CT. The patient had atypical chest pain during exercise and 2-mm horizontal ST depression in lateral chest leads. In the PET-CT study, the coronaries were normal but myocardial perfusion was diffusely reduced (anterior view: Figure 15.1a; posterior view: Figure 15.1b). The absolute MBF during adenosine stress was below 2 mL/g/min (normal value >2.3 mL/g/min) in all myocardial regions as shown in green in Figure 15.1. No epicardial disease was detected in invasive angiography and effective treatment of risk factors was recommended.
Light weight multi-branch network-based extraction and classification of myocardial infarction from 12 lead electrocardiogram images
Published in The Imaging Science Journal, 2023
Jothiaruna Nagaraj, Anny Leema
MI changes can be mainly seen in ST segment like ST-Depression or ST-Elevation [4]. And also these changes are indicated other types of heart diseases, so expertise in this field is needed for accurate diagnosis of heart diseases. For this purpose, Computer-Aided Detection (CAD) methods are used. Usually, ECG is time-series data which is one-dimensional voltage amplitude data [5]. Using CAD with time-series data classification is performed by using machine learning algorithms [6] are Hidden Markov Model [7], Support Vector Machine [8], Adaboost [9], and Naive Bayes [10]. At first, preprocessing of the ECG signal is carried out for removing the noise from the signal using filtering methods. Filtering methods are low pass, high pass, Weiner filter, and butter worth filter. After preprocessing the signal, the detection of the peak in the weaves is carried out using the algorithms like z-scores. Then extraction of features is processed using the methods like statistic parameters. Using the extracted information classification is performed.
A Public Key Authentication and Privacy Preserving Model for Securing Healthcare System
Published in IETE Journal of Research, 2021
In order to evaluate the performance analysis of the proposed model with existing models, we have used the heart disease dataset from the UCI Machine Learning repository [35]. The creators of this dataset are A. Janosi from Hungarian Institute of Cardiology, Budapest, W. Steinbrunn, University Hospital Zurich, M. Pfisterer, University Hospital, Basel, and R. Detrano from Long Beach and Cleveland Clinic Foundation. The Creative Commons Attribution 4.0 International (CCBY 4.0) license has proven this dataset. The features of the dataset are multivariate, while the attributes are categorical. This dataset contains 76 attributes and 14 attributes viz. age in years, sex, four values of chest pain type, resting blood pressure (pressure on the walls of blood vessels caused by the circulating blood), serum cholesterol in mg/dl, the slope of a peak exercise ST segment, number of main vessels (0–3) colored with fluoroscopy, the ideal blood pressure (between 90/60 mmHg and 120/80 mmHg), fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, old peak (that is, the ST depression induced by exercises relative to rest), along with thal (that is, 7 – reversible defect; 6 – fixed defect; 3 – normal), number of major vessels, diagnosis of heart disease are generally used. In addition, the dataset gives the presence of heart disease in integer values ranging from 0 to 4, where 0 represents no heart disease, and 4 represents the highest probability of heart disease.
Spatiotemporal regularization for inverse ECG modeling
Published in IISE Transactions on Healthcare Systems Engineering, 2020
Modern healthcare systems are increasingly investing in advanced sensing and imaging to facilitate the effective modeling, monitoring, and management of complex dynamics in the patients’ health conditions. For example, body-area sensor network helps capture multi-directional information pertinent to the heart electrical activity. The 12-lead electrography (ECG) provides 12 directional views of the cardiac electrodynamics, i.e. 12 ECG time series (Yang et al., 2012; Yang & Leonelli, 2016). Such ECG time series enable medical scientists to investigate cardiac electrical activity and further identify heart diseases by checking waveform abnormalities (Penzel et al., 2016; Yang et al., 2013). For example, the patterns of ST depression/elevation, significant Q waves, or inverted T-waves in ECG cycles often indicate different stages in the progression of myocardial infarction.