Transformer-based 1D CNN with MFCC features classifies pediatric heart sounds at 93.69 percent accuracy on 5-second segments, identifying minimum effective length and RMSSD/ZCR quality threshold of 0.4.
In the experiment, the features' sizes are 39 X 155 for the 15-second signal, 39 X 51 for the 5-second signal, and 39 X 30 for the 3-second signal
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Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network
Transformer-based 1D CNN with MFCC features classifies pediatric heart sounds at 93.69 percent accuracy on 5-second segments, identifying minimum effective length and RMSSD/ZCR quality threshold of 0.4.