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.
T., Balasubramanian, P., & Umapathy, S
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.SD 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.