A Novel Multi-Head Self-Organized Operational Neural Network Architecture for Chronic Obstructive Pulmonary Disease Detection Using Lung Sounds

Chronic obstructive pulmonary disease (COPD) is one of the most severe respiratory diseases, which can be diagnosed by several clinical modalities such as spirometric measures, lung function tests, parametric response mapping, wheezing events of lung sounds, etc. Since lung sounds are related to the respiratory irregularities caused by pulmonary illnesses, examining these sounds is more effective for identifying respiratory issues. In this paper, we propose a triplet time-frequency representation (TFR) driven multi-head self-organized operational neural network (MHSONN) for efficient detection of COPD-affected lung sound signals, which exploits the complex non-linear neural architecture in contrary to the linear neural perceptron model used by convolutional neural networks. The proposed framework consists of three stages: (a) pre-processing, (b) triplet TFR extraction, and (c) classification using the proposed MHSONN architecture. Upon experimental evaluation, the proposed work outperforms the existing noteworthy research works by achieving the highest performance rates of 99.81%, 99.85%, and 99.73% for accuracy, sensitivity, and specificity, respectively. The onboard implementation of the proposed framework on a Raspberry Pi-4 microcontroller also exhibits its viability for developing a point-of-care COPD detection system in real-world clinical scenarios.