Machine Learning-Based Analysis of Echocardiography Images for Cardiac Disease Diagnosis
Keywords:
Electrocardiogram, photoplethysmography, valvular heart diseases, image acquisitionAbstract
Echocardiography, often known as cardiac ultrasonography, is the imaging technique that evaluates heart function and structure that is used the most frequently and is easily accessible. Echocardiography is one of the most often used imaging tests because it does not expose the patient to the dangers associated with ionizing radiation, in addition to its portability, quick picture collection, high temporal resolution, and rapid image acquisition. Echocardiography is both essential and sufficient for the diagnosis of various cardiovascular disorders. This includes conditions ranging from heart failure to diseases that affect the heart's valves. The primary goal of this study is to build a Hyperparameter Tuned (HPT) Xception emulate using Adagrad optimizer with Class attention the layer and BiLSTM model for MV diagnosis and classification. This will be accomplished by classifying arrhythmia disease with electrocardiogram (ECG) and photoplethysmography (PPG) signals using a machine learning approach.
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