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.
ππΉπΆπΆΰ― = ΰ· πΰ― ΰ―ΰ― ΰ―ିଡ ΰ―ΰ଴ (π) βπππ cosαππ(πβ 0.5) π α, π= 1,2, β¦ β¦ β¦ ,πΏ (13) where L is the order of the MFCC coefficient, and M denotes the number of filter banks
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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.