An Improved Deep Learning Model for Automated Detection of BBB Using S-T Spectrograms of Smoothed VCG Signal

Objective: Bundle Branch Block (BBB) is the most serious asymptomatic, cardiovascular complication caused due to obstruction or delay across the pathways of electrical signals. Vectorcardiography (VCG) is the most useful method for measuring heart activities, containing information about the different physiological states of the heart. It is a powerful tool for detecting blockage inside the lower chamber of the heart. Clinically, posterior sensing electrodes are used to record VCG signals. Manual inspection of BBB-induced VCG changes is a time-consuming, labor-intensive, and error-prone procedure. This is the first work that integrates a deep learning technique with S-T spectrograms of smoothed VCG signals for the accurate diagnosis of BBB. A novel filter-fusion technique has also been proposed, to remove the various artifacts from the signals, which considerably increased the BBB diagnosis performance.
Methods:
VCG signals are divided into fragments of 4-
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. A newly introduced filter-fusion technique is utilized to remove baseline wander and smooth the signal. Stock-well transform (S-T) is applied to extract spectrograms from de-noised smoothed VCG fragments. Obtained S-T spectrograms are fed to pre-trained Alex-Net, Squeeze-Net, and a newly developed less complex deep learning model with data augmentation technique utilizing a 10-fold cross-validation scheme.
Results:
Proposed improved deep learning model (IDLM) yielded the highest detection accuracy of 98.80%, true positive rate of 98.74%, and true negative rate of 98.80%.
Conclusion:
The findings show that developed DLM is computationally fast and efficient than benchmark models. The proposed IDLM is available for testing with other datasets.