Real time detection and forecasting technique for asthma disease using speech signal and DENN classifier

Abstract

Abnormal sound of the lungs associated with asthma and respiratory disease. On the spectrogram, it exhibits continuous sinusoidal qualities across time as well as significant computer properties. In this article, a voice signal and an optimum classifier is used to present a real-time detection and forecasting (RTDF) approach for Asthma illness. For asthma diagnosis and forecasting, the suggested RTDF approach employs the improved whale optimization (IWO) algorithm. RTDF technology is used to classify normal and asthmatic diseases based on changes in voice signals. RTDF technology integrates a variety of working Differential evolutionary neural network (DENN) classifiers that are superior to current SVM classifiers and help prevent the development of asthma suppression. In addition, the digital detection gateway-based secondary output can detect or reject the primary output, making breath detection more reliable for weak respiratory sounds. Implementation is done in conjunction with MATLAB tools and performs performance analysis based on the probability of correct classification. The presentation of the planned process was evaluated using a special signal-to-noise ratio (SNR) using lung sounds from patients and normal subjects.