Wheeze Signal Feature Engineered Deep Neural Network Model for Wheeze Segmentation and Wheeze Sound Detection from Lung Sound Data Signals

Abstract

A study of the lung sound plays an essential role in diagnosing respiratory diseases, with wheezing being one of the most important features indicating respiratory diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). The classical methods of detecting wheezes used in traditional techniques of auscultation are dependent on the experience of the physician and thus caused inconsistency and disparity in diagnosing the symptoms. Computerized Respiratory Sound Analysis (CORSA) and sophisticated machine learning (ML) algorithms have been used to achieve higher accuracy in detecting wheezing. But, ML methods have limitations based on data size and might have difficulties with big data. A promising alternative to that is deep learning (DL) methods, but there are still issues with overfitting and class imbalance. Other optimized models such as AlexNet and VGG16 had improved performance, and harmonic and percussion features can complicate classification. To deal with the challenges, the present research suggests a Wheeze Signal Feature Engineered Deep Neural Network (WSFEDNN) model that can identify and categorize lung sounds, i.e., wheezing and so on, in a multi-stage approach. First, it uses Empirical Mode Decomposition (EMD) to break down respiratory sound signals into Intrinsic Mode Functions (IMFs), which contain non-linear and non-stationary oscillating patterns. These IMFs are then processed with a Power-Phase Harmonic-Percussive Source Separation (PP-HPSS) which breaks these IMFs down into harmonic (Power IMFs) and percussive (Phase IMFs) parts, maintaining amplitude and phase information that is important to use when features are to be processed. Subsequently, feature engineering identifies significant patterns in the processed data including lagged observations as a way of capturing temporal dependence and temporal features as a way of analysing timing and cyclic changes and statistical features as a way of quantifying sound intensity, variability and distribution. Lastly, the model utilises a CNN-LSTM to apply a hybrid combining spatial feature extraction together with a temporal pattern recognition with a softmax classification of the features as either wheezing or normal. The combined methodology makes sure that it adequately detects wheezing using the frequency domain and time domain information. The analysis of the experiment revealed that the WSFEDNN performed better as compared to other existing baseline models, HPSS and RNN-LSTM with a precision of 93.48% and 91.23, respectively. This highlights the prospects of WSFEDNN model on real world clinical applications, leading to timely identification and treatment of respiratory disabilities and ultimately patient recovery.