Pulmonary breathing sound plays a key role in the prevention and diagnosis of the lung diseases. Its correlation with pathology and physiology has become an important research topic in the pulmonary acoustics and the clinical medicine. However, it is difficult to fully describe lung sound information with the traditional features because lung sounds are complex and nonstationary signals. And the traditional convolutional neural network cannot also extract the temporal features of the lung sounds. To solve the problem, a lung sound recognition algorithm based on VGGish-BiGRU is proposed on the basis of transfer learning, which combines VGGish network with the bidirectional gated recurrent unit neural network (BiGRU). In the proposed algorithm, VGGish network is pretrained using audio set, and the parameters are transferred to VGGish network layer of the target network. The temporal features of the lung sounds are extracted through retraining BiGRU network with the lung sound data. During retraining BiGRU network, the parameters in VGGish layers are frozen, and the parameters of BiGRU network are fine-tuned. The experimental results show that the proposed algorithm effectively improves the recognition accuracy of the lung sounds in contrast with the state-of-the-art algorithms, especially the recognition accuracy of asthma.

/pdf/lung-sound-recognition-algorithm-based-on-vggish-bigru-3o2ql1t8q3.pdf

Lung Sound Recognition Algorithm Based on VGGish-BiGRU

A novel person identification (PID) technique is developed in this study, which exploits a new biometric called bronchial breath sound and speech signal acquired by a stethoscope. In addition to investigating the acoustic characteristics of breath sounds for PID, we evaluate three identification methods, including support vector machines (SVM), artificial neural networks (ANN), and i-vector approach. Recognizing the requirement that the amount of sound data collected from each person should be as small as possible, this work studies data augmentation (DA) techniques that avoid the system training process from the overfitting problem when the training sound data is insufficient. In addition, we apply feature engineering techniques to find the informative subset of breath sound features which is beneficial for PID. Our experiments were conducted using a dataset composed of 16 subjects, including an equal number of male and female participants. In the test phase, both Support Vector Machine combined with feature selection and Artificial Neural Networks approaches yielded the promising accuracies of 98%.

Stethoscope-Sensed Speech and Breath-Sounds for Person Identification With Sparse Training Data

AbstractPulmonary diseases are the third biggest cause of deaths worldwide. A prominent method to detect these diseases is the observation of lung sound signals. There is an increasing need for an efficient technique that can automatically diagnose such diseases with high accuracy. In this paper, two popular public datasets are considered, and every lung sound signal is decomposed into 8 frequency bands using rectangular zero-phase filters. Features are extracted from every band, including energy, kurtosis, mean absolute deviation and Lp norm. The extracted features are utilized for classification using machine learning schemes. The proposed method achieves 99.9% accuracy for multi-class classification on the combined dataset, and for binary classification, we have evaluated normal signal versus pathogenic signal, which is found to be 100% accurate for most of the diseases. High accuracy is obtained for the individual datasets as well. Top 20 features selected using minimum redundancy maximum relevance algorithm also yield 98.6% accuracy. Therefore, the proposed method can be easily deployed in real-time systems.KEYWORDS: Feature rankingfrequency bandsminimum redundancy maximum relevancepulmonary diseasessignal decompositionzero-phase filters Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsU. HassanU Hassan received her BTech degree in electronics and communication engineering from Kurukshetra university, Haryana, India in 2016 and MTech degree in electronics and communication engineering from Kurukshetra university, Haryana, India. She is currently pursuing PhD at the department of electronics and communication engineering, NSUT, Delhi, India. Her areas of interest include medical signal processing, speech signal processing, artificial intelligence, and machine learning. Email: umaisathakur@gmail.comA. SinghalA Singhal completed his dual degree with BTech in electrical engineering and MTech in information and communication technology from IIT Delhi in 2009. He completed his PhD in molecular communication from IIT Delhi in 2016. He has a total teaching experience of more than 13 years. He is currently working as assistant professor in the department of electronics and communication engineering in NSUT, Delhi, India. His areas of interest are Image Processing, Molecular Communications, Next generating communication technologies, Image retrieval, Theory and Applications of Fourier methods. Corresponding author. Email: amit@nsut.ac.in 

Automated Diagnosis of Pulmonary Diseases Using Lung Sound Signals

Research focuses on the efficacy of Multi-Task Autoencoder (MTAE) models in signal classification due to their ability to handle many tasks while improving feature extraction. However, researchers have not thoroughly investigated the study of lung sounds (LSs) for pulmonary disease detection. This paper introduces a new framework that utilizes an MTAE model to detect lung diseases based on LS signals. The model integrates an autoencoder and a supervised classifier, simultaneously optimizing both classification accuracy and signal reconstruction. Furthermore, we propose a hybrid approach that combines an MTAE and a Support Vector Machine (MTAE-SVM) to enhance performance. We evaluated our model using LS signals from a publicly available database from King Abdullah University Hospital. The model attained an accuracy of 89.47% for four classes (normal, pneumonia, asthma, and chronic obstructive pulmonary disease) and 90.22% for three classes (normal, pneumonia, and asthma cases). Using the MTAE-SVM, the accuracy was further improved to 91.49% for four classes and 93.08% for three classes, respectively. The results indicate that the MTAE and MTAE-SVM have a considerable potential for detecting pulmonary diseases from lung sound signals. This could aid in the creation of more user-friendly and effective diagnostic tools. 

A Framework for Detecting Pulmonary Diseases from Lung Sound Signals Using a Hybrid Multi-Task Autoencoder-SVM Model

pdf/discriminacion-de-la-fibrosis-pulmonar-idiopatica-y-sindrome-45sqti2plp.pdf

Discriminación de la fibrosis pulmonar idiopática y síndrome combinado de fibrosis-enfisema mediante el análisis acústico multicanal lineal y no lineal

A computer aided diagnostic system capable of analyzing respiratory sounds can be very helpful in detection of pneumonia, asthma and tuberculosis as the Respiratory sound signal carries information about the underlying physiology of the lungs and is used to detect presence of adventitious lung sounds which are an indication of disease. Respiratory sound analysis helps in distinguishing normal respiratory sounds from abnormal respiratory sounds and this can be used to accurately diagnose respiratory diseases as is done by a medical specialist via auscultation. This process has subjective nature and that is why simple auscultation cannot be relied upon.In this paper we present a novel method for automated detection of crackles and bronchial breath sounds which when coupled together indicate presence and severity of Pneumonia. The proposed system consists of four modules i.e., pre-processing in which noise is filtered out, followed by feature extraction. The proposed system then performs classification to separate crackles and bronchial breath sounds from normal breath sounds.

Separation and Classification of Crackles and Bronchial Breath Sounds from Normal Breath Sounds Using Gaussian Mixture Model

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Separation and Classification of Crackles and Bronchial Breath Sounds from Normal Breath Sounds Using Gaussian Mixture Model