Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring

Non-invasive health monitoring has recently gained a lot of consideration in the modern healthcare system, because it has the potential to diagnose diseases earlier and can monitor patients in a remote manner. This research presents a hybrid approach in healthcare monitoring by integrating vocal and lung abnormality detection using a multinetwork model. The model utilizes multiple data sources and Mel Frequency Cepstral Coefficients (MFCCs) to capture the frequency spectrum of the signal. A multinetwork model developed for disease identification is made up of hybrid deep learning networks, which consist of Convolutional Neural Networks (CNN) and Bi-directional Recurrent Neural Networks (BiRNN) referred as the Convolutional Bi-directional Recurrent Neural Network (CBiRNN). These CBiRNN models process both the vocal and lung datasets in parallel and feed the predicted results into the ensemble model for comprehensive evaluation. The experimental results show that the proposed CBiRNN model achieves 92% accuracy in voice disorder detection and 98% accuracy in respiratory disorder detection, while the ensemble model attains 98% accuracy for both voice and lung prediction. This innovative multimodal processing technique demonstrates significant potential in advancing health monitoring systems, offering a pathway to more accurate and reliable diagnostic tools.