COPD Scope: Smart Clinical Pathways for Respiratory Illness Diagnosis and Management

Abstract Internet of Things (IoT) is playing a vital role in healthcare by automating the real time monitoring of patients seamlessly with the help of a variety of sensors. In the current scenario of smart healthcare, IoT with the integration of machine learning and deep learning algorithms is instrumental in the management of chronic diseases like chronic obstructive pulmonary diseases (COPD). IoT when deployed for COPD, supports in the monitoring of lung functioning, uninterrupted patient monitoring with personalised treatment planning and also facilitates in the implementation of telemedicine technology. This paper presents a comprehensive insight on the diagnosis and treatment strategies of COPD from multiple modalities viz. smart sensors for respiratory monitoring, auscultations and imaging-based diagnosis. An exhaustive experimentation of various machine learning and deep learning algorithms for the diagnosis of respiratory illness from the auscultation recordings corresponding to four classes: normal, crackle, Rhinchi and wheeze has been presented as a case study. Lung sounds were analysed in different dimensions by considering the signal as one-dimensional (1D) vector and multiple time frequency representations derived from the 1D signals. Statistical, spectral and cepstral features derived from the signals were classified using decision tree, support vector machine, K- nearest neighbor and random forest classifiers. In a different spectrum, convolutional neural networks were employed to perform classification of lung sounds from 1D signals as such and spectrogram, melspectrogram and scalogram representations derived from 1D signals. From the outcomes of experimentation, it is inferred that visual representations of signal frequency spectrum in the form of melspectrogram representations play a significant role in categorizing the patterns belonging to four classes of respiratory diseases. The proposed model COPDScope upon experimenting with different optimizers, it is found that Melspectrogram representation records a better performance over the other two representations.