Non-Uniform Filter Bank Visualization based Prediction Models for Respiratory Signals

Abstract

Chronic respiratory disorders (CRDs) and lower res piratory illness are the common widespread infection emphasized by obstruction of flow of air in alveoli. Listening to breathe sounds of the respiratory system is a traditional technique used for diagnosing chronic disorders in patients. However, the outcomes of diagnosis can be sensed only by skilled therapist which imposes limitations on quantifiable results. This urged to the development of technology driven tools for detecting the disorders. Most of the earlier studies focused on analyzing the breathe sounds using different filter banks (FBs) such as Discrete cosine transform (DCT) FBs, Wavelet FBs, Mel FBs and s pectrograms for classification using CNN deep learning models. Owing to the nature of exactly matching the frequency characteristics of FBs with that of human ears, Octave FBs and Cross over FBs are proposed in this work. The filter coefficients extracted using proposed FBs are transformed into s pectrogram time Frequency Visualization and are classified using three Transfer learning (TL) viz. GoogLeNet, S queezeNet and ResNet-50. The comparative results with existing method reveal that the proposed FB produces significant improvement in accuracy of 90.63% and 90.89% for ResNet-50 classifier.