DABiG: Breath pattern classification using the hybrid deep learning with optimal feature selection.

Abstract

Background: A person's breathing pattern can be a reflection of their emotional and physical well-being because it shows the frequency, intensity, and rhythm of their breathing.

Objective: This research article presents a comprehensive approach to breathe pattern classification utilizing gyroscope and accelerometer readings obtained from individuals using two distinct sensors. The study encompasses the acquisition of six diverse breathing patterns, with a focus on data pre-processing through Min-Max normalization.

Methods: To select essential features from the normalized data, an innovative optimization algorithm, Adaptive Chimp Optimization (AdCO), is introduced. AdCO integrates an adaptive weighting strategy into the conventional Chimp optimization algorithm, enhancing convergence rates and enabling global optimal feature selection. Furthermore, the article introduces the application of the selected features in breath pattern classification using a hybrid deep learning mechanism, DABiG. DABiG leverages the Bidirectional Gated Recurrent Unit (BiGRU), a neural network architecture capable of processing sequential data bi-directionally.

Results: Spatial and temporal attention mechanisms are incorporated into DABiG to enhance its ability to focus on relevant spatial regions and time steps within the breath pattern data.

Conclusion: Spatial attention assigns weights to spatial regions, while temporal attention assigns weights to time steps, improving feature extraction and classification accuracy.