The performances of SDC-L were evaluated with three machine learning techniques (support vector machine (SVM), k-nearest neighbor (k-NN) and random forest (RF)) and two deep learning algorithms (multilayer perceptron (MLP) and convolutional neural network (cNN)) and one hybrid deep learning algorithm combining cNN with long short term memory (LSTM) in terms of accuracy, precision, recall and F1-score. We modified EMD algorithm by adding a stopping rule to objectively select a finite number of intrinsic mode functions (IMFs).
It preserves temporal dependency information of multiple frames nearby same to original SDC, and improves feature extraction by reducing the hyperspectral dimension. Therefore, we propose shifted δ\documentclass-cepstral coefficients in lower-subspace (SDC-L) as a novel feature for lung sound classification. However, they are modeled in high-dimensional hyperspectral space, and also lose temporal dependency information. Static cepstral coefficients such as Mel-frequency cepstral coefficients (MFCCs), have been used for classification of lung sound signals. Respiratory sounds are expressed as nonlinear and nonstationary signals, whose unpredictability makes it difficult to extract significant features for classification. We have found that our newly investigated features are more robust than existing features and show better recognition accuracy even in low signal-to-noise ratios (SNRs). Finally, we evaluate the features for noisy lung sound recognition. Further, we experimentally optimize different control parameters of the proposed feature extraction algorithm. Results show that the statistical features extracted from mel-frequency cepstral coefficients (MFCCs) of lung sounds outperform commonly used wavelet-based features as well as standard cepstral coefficients including MFCCs. Experiments are conducted on a dataset of 30 subjects using the artificial neural network (ANN) as a classifier. Subsequently for fast and efficient classification, we propose a new feature set computed from the statistical properties of cepstral coefficients. Motivated by the success of cepstral features in speech signal classification, we evaluate five different cepstral features to recognize three types of lung sounds: normal, wheeze and crackle. In this paper, short-term spectral characteristics of lung sounds are studied to characterize the lung sounds for the identification of associated diseases. Lung sounds convey useful information related to pulmonary pathology.