Accurate detection of end-diastole (ED) and end-systole (ES) frames is a crucial step in cardiac function analysis, enabling precise measurement of ventricular volume, ejection fraction (EF), and stroke volume (SV). H...
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Accurate detection of end-diastole (ED) and end-systole (ES) frames is a crucial step in cardiac function analysis, enabling precise measurement of ventricular volume, ejection fraction (EF), and stroke volume (SV). However, this task is challenging due to variations in cardiac structure, heart rate fluctuations associated with clinical conditions, and the low-resolution nature of echocardiographic sequences. This study addresses these challenges by introducing three preprocessing steps - noise reduction via heart rate formulation, video frame synchronization, and non-oscillating mean absolute frame difference - to denoise and enhance the EchoNetDynamic dataset. Additionally, the echo phase detection problem is reformulated as a frame-level binary classification task to mitigate class imbalance between diastole and systole phases. The proposed architecture employs a time-distributed convolutional neural network (cnn) to extract spatial features, followed by a bidirectional long short-term memory (BiLSTM) network to capture temporal dynamics, and a classification layer for phase prediction. The model achieves an average absolute frame distance of 1.02 and 1.04 frames for ED and ES frames, respectively, on the preprocessed EchoNet-Dynamic dataset. To ensure better generalization, the model was also validated on the CAMUS dataset and private data, where it demonstrated consistent performance and robust results. These findings significantly enhance the reliability of cardiac metrics, offering clinicians a precise and efficient tool for echocardiographic analysis.
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