Machine Learning-Based Classification of Pulmonary Diseases through Real-Time Lung Sounds

    The study presents a computer-based automated system that employs machine learning to classify pulmonary diseases using lung sound data collected from hospitals. Denoising techniques, such as discrete wavelet transform and variational mode decomposition, are applied to enhance classifier performance. The system combines cepstral features, such as Mel-frequency cepstrum coefficients and gammatone frequency cepstral coefficients, for classification. Four machine learning classifiers, namely the decision tree, k-nearest neighbor, linear discriminant analysis, and random forest, are compared. Evaluation metrics such as accuracy, recall, specificity, and f1 score are employed. This study includes patients affected by chronic obstructive pulmonary disease, asthma, bronchiectasis, and healthy individuals. The results demonstrate that the random forest classifier outperforms the others, achieving an accuracy of 99.72% along with 100% recall, specificity, and f1 scores. The study suggests that the computer-based system serves as a decision-making tool for classifying pulmonary diseases, especially in resource-limited settings

Classification of Normal and Crackles Respiratory Sounds into Healthy and Lung Cancer Groups

Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer

A novel method for automatic identification of respiratory disease from acoustic recordings.

This paper evaluates the use of breath sound recordings to automatically determine the respiratory health status of a subject. A number of features were investigated and Wilcoxon Rank Sum statistical test was used to determine the significance of the extracted features. The significant features were then passed to a feature selection algorithm based on mutual information, to determine the combination of features that provided minimal redundancy and maximum relevance. The algorithm was tested on a publicly accessible respiratory sounds database. With the testing dataset, the trained classifier achieved accuracy of 87.1%, sensitivity of 86.8% and specificity of 93.6%. These are promising results showing the possibility of determining the presence or absence of respiratory disease using breath sounds recordings

NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals

Cardiovascular diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signals, compromising their essential distinctive characteristics. The prevalence of this issue in overcrowded and resource-constrained hospitals can compromise the accuracy of medical diagnoses. Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise robust network to improve the CVDs classification performance.For the identification of the optimal transformation method for noisy heart sound data mel-frequency cepstral coefficients (MFCCs), short-time Fourier transform (STFT), constant-Q nonstationary Gabor transform (CQT) and continuous wavelet transform (CWT) has been used with VGG16. Furthermore, we propose a novel convolutional recurrent neural network (CRNN) architecture called noise robust cardio net (NRC-Net), which is a lightweight model to classify mitral regurgitation, aortic stenosis, mitral stenosis, mitral valve prolapse, and normal heart sounds using PCG signals contaminated with respiratory and random noises. An attention block is included to extract important temporal and spatial features from the noisy corrupted heart sound.The results of this study indicate that,CWT is the optimal transformation method for noisy heart sound signals. When evaluated on the GitHub heart sound dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95% better than the second-best CQT transformation technique. Moreover, our proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher than the VGG16

Measurement and analysis of breath sounds

Existing breath sound measurement systems and possible new methods have been critically investigated. The frequency response of each part of the measurement system has been studied. Emphasis has been placed on frequency response of acoustic sensors; especially, a method to study a diaphragm type air-coupler in contact use has been proposed. Two new methods of breath sounds measurement have been studied: laser Doppler vibrometer and mobile phones. It has been shown that these two methods can find applications in breath sounds measurement, however there are some restrictions. A reliable automatic wheeze detection algorithm based on auditory modelling has been developed. That is the human’s auditory system is modelled as a bank of band pass filters, in which the bandwidths are frequency dependent. Wheezes are treated as signals additive to normal breath sounds (masker). Thus wheeze is detectable when it is above the masking threshold. This new algorithm has been validated using simulated and real data. It is superior to previous algorithms, being more reliable to detect wheezes and less prone to mistakes. Simulation of cardiorespiratory sounds and wheeze audibility tests have been developed. Simulated breath sounds can be used as a training tool, as well as an evaluation method. These simulations have shown that, under certain circumstance, there are wheezes but they are inaudible. It is postulated that this could also happen in real measurements. It has been shown that simulated sounds with predefined characteristics can be used as an objective method to evaluate automatic algorithms. Finally, the efficiency and necessity of heart sounds reduction procedures has been investigated. Based on wavelet decomposition and selective synthesis, heart sounds can be reduced with a cost of unnatural breath sounds. Heart sound reduction is shown not to be necessary if a time-frequency representation is used, as heart sounds have a fixed pattern in the time-frequency plane

Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels