Classification of Short Segment Pediatric Heart Sounds Based on a
  Transformer-Based Convolutional Neural Network
Md Hassanuzzaman Nurul Akhtar Hasan Mohammad Abdullah Al Mamun Khawza I Ahmed Ahsan H Khandoker Raqibul Mostafa
Congenital anomalies arising as a result of a defect in the structure of the
heart and great vessels are known as congenital heart diseases or CHDs. A PCG
can provide essential details about the mechanical conduction system of the
heart and point out specific patterns linked to different kinds of CHD. This
study aims to investigate the minimum signal duration required for the
automatic classification of heart sounds. This study also investigated the
optimum signal quality assessment indicator (Root Mean Square of Successive
Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral
coefficients (MFCCs) based feature is used as an input to build a
Transformer-Based residual one-dimensional convolutional neural network, which
is then used for classifying the heart sound. The study showed that 0.4 is the
ideal threshold for getting suitable signals for the RMSSD and ZCR indicators.
Moreover, a minimum signal length of 5s is required for effective heart sound
classification. It also shows that a shorter signal (3 s heart sound) does not
have enough information to categorize heart sounds accurately, and the longer
signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is
obtained for the 5s signal to distinguish the heart sound.