Python-FAViTNet: An Deep Learning Framework Non-Invasive Fetal Arrhythmia Diagnosis-MyProjectBazaar

Analyzing fetal electrocardiograms (fECG) to classify fetal arrhythmia is a challenging task;
still, it is indispensable for evaluating fetal cardiac health status. This study intends to develop a framework
for the effective discernment of fetal arrhythmia that assists obstetricians in diagnosing whether the fetuses
have any abnormality in cardiac rhythm. In this work, authors aim to develop a three-stage fetal arrhythmia
detection framework comprised of 1) fECG extraction from abdominal Electrocardiogram (aECG) signal
based on functional link neural network adaptive filter, 2) denoising of extracted fECG exploiting a generative
adversarial network where the generator and the discriminator follow robust architectures, and 3) fetal
arrhythmia detection based on FAViTNet architecture. The proposed work deeply focuses on the last
stage, where we have unfolded a deep convolutional architecture that detects fetal arrhythmia utilizing
high-resolution spectral images acquired from the Stockwell transform. The classification stage demonstrates
an architecture involving spectral image tokenization and transformer encoder blocks. The spectral image
tokenization module involves ghost bottleneck blocks, which are utilized to generate feature maps from the
spectral images, subsequently transformed into 1D token embeddings. Channel-wise calibration is utilized
to attain more attention to the deep feature maps acquired from the ghost network. The transformer encoder
block effectively attains the long-term dependencies from the 1D embedded features with gated linear
unit (GLU)-based multi-head self-attention where deep features are learned to discern fetal arrhythmia
effectively. The proposed algorithm shows an accuracy of 96.85%, sensitivity of 96.69%, specificity of
96.98%, and precision of 96.48%

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