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.
Notably, the highest accuracy of heart sound classification is 93.67% at ZCR = 0.3 and RMSSD in the range of 0.4 - 1
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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.