A 75 ms Gaussian window for segmenting phonocardiography signals yields the highest biLSTM classification accuracy among tested shapes and lengths, outperforming rectangular windows and a baseline method.
Recognizing abnormal heart sou nds using deep learning
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Comparison of window shapes and lengths in short-time feature extraction for classification of heart sound signals
A 75 ms Gaussian window for segmenting phonocardiography signals yields the highest biLSTM classification accuracy among tested shapes and lengths, outperforming rectangular windows and a baseline method.