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An Investigation on ECG-based Cardiological Diagnosis via Deep Learning Models
Published in Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot, Recent Advances in AI-enabled Automated Medical Diagnosis, 2022
Alex Meehan, Zhaonian Zhang, Bryan Williams, Richard Jiang
Acute Coronary Syndromes (ACS) are caused by an imbalance between the demand for oxygen and the blood flow. It may be caused by either an acute reduction of blood supply or an increase in demand that cannot be matched by the blood flow [4]. The main ACS are ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina. These ACS are an important cause of morbidity and mortality in the UK and worldwide [6], with the World Health Organization estimating that 17.9 million people died from cardiovascular diseases in 2016 worldwide, representing 31% of all global deaths. Rapid and accurate diagnosis can dramatically improve patient outcomes, with treatments ranging from surgical interventions for the more acute cases to medications for less severe cases.
Leukemia Detection Using Invariant Structural Cascade Segmentation Based on Deep Vectorized Scaling Neural Network
Published in Cybernetics and Systems, 2023
A. Arthi, V. Vennila, U. Arun Kumar
Chronic leukemia is a kind of leukemia that does not impact the normal functioning of early white blood cells and hence is not recognized early. This type of leukemia is more difficult to treat than acute leukemia. In such a scenario, the patient will be unable to recognize the signs of the condition. Chronic lymphocytic leukemia, also known as Chronic Lymphocytic Leukemia (CLL), and chronic myelogenous leukemia are the two subtypes of chronic leukemia (AML). In the early stages of acute leukemia, cells do not interfere with the proper functioning of WBC. This is because acute leukemia is a relatively rare form of the disease. However, once the following stage has been reached, leukemia cells can no longer be contained and instead spread rapidly. The most common forms of acute leukemia are acute lymphocytic leukemia (ALL) and acute myelogenous leukemia. Acute myelogenous leukemia is the more severe kind (AML).
When is a wearable defibrillator indicated?
Published in Expert Review of Medical Devices, 2021
Alexandre Bodin, Arnaud Bisson, Laurent Fauchier
However, the first randomized study was the VEST study. It included patients after acute myocardial infarction with LVEF below 35%. Patients were randomized in a 2:1 fashion between the WCD group (n = 1,524) or control group (n = 778). This study did not show any significant reduction of arrhythmic death in the WCD group (RR = 0.67; 95%CI, 0.37 to 1.21; p = 0.18) [18]. The main reason of this nonsignificant result is likely to be a bad compliance with suboptimal daily wearing time of the WCD. Indeed, in the VEST trial, median daily use time was 18 hours and among 48 patients that died in the WCD group, only 12 patients wore the vest. However, as-treated and per-protocol analyses were performed [9]. A reduction of arrhythmic death in the WCD group was observed with a rate ratio of 0.43 (95% CI 0.21–0.91, uncorrected p = 0.03; Bonferroni corrected p = 0.32). Interestingly, better WCD compliance was observed after cardiac arrest during index myocardial infarction (MI), in patients with higher creatinine, diabetes, prior heart failure, LVEF ≤ 25%, and patients enrolled in Poland, and it was correlated with the number of WCD alarms. Compliance was worse in divorced patients and in those of Asian race, with a higher body mass index, with prior percutaneous coronary intervention, or with a history of WCD shock.
Network modeling and Internet of things for smart and connected health systems—a case study for smart heart health monitoring and management
Published in IISE Transactions on Healthcare Systems Engineering, 2020
Hui Yang, Chen Kan, Alexander Krall, Daniel Finke
Further, we follow the experimental design in Section 5 to evaluate the performance of the proposed network method for change detection of signal variations in cardiac dynamics. First, we simulate disease-induced morphological variability by shifting three segments of ECG signals (i.e., ST, P, and QRS). Here, an elevated ST segment is used to simulate the acute myocardial infarction. Also, a larger P wave corresponds to the potential atrial enlargement. When the patient is associated with massive pericardial effusion, the voltage of QRS will be lower, which is reflected as a reduced QRS complex. The disease severity is controlled by the level of shift magnitude. In other words, a small shift indicates that the disease is in the early stage, whereas a larger shift is associated with late-stage disease conditions and calls for immediate medical interventions.