Α Respiratory Sound Database for the Development of Automated Classification

Rocha, Bruno; Filos, Dimitrios; Mendes, Luís; Vogiatzis, Ioannis; Perantoni, Eleni; Kaimakamis, Evangelos; Natsiavas, Pantelis; Oliveira, Ana; Jácome, Cristina; Marques, Alda; Paiva, Rui Pedro; Chouvarda, Ioanna; Carvalho, Paulo; Maglaveras, Nicos

doi:10.1007/978-981-10-7419-6_6

Cited by 152 publications

(142 citation statements)

References 10 publications

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“…The obtained accuracy score was 94.6%. In [6], the authors used the ICBHI 2017 challenge database which has normal, wheezes, crackles and wheezes plus crackles class labels. The ICBHI 2017 is a challenging database, since there are noises, background sounds and different sampling frequencies (4 kHz, 10 kHz, 44.1 kHz).…”

Section: Related Workmentioning

confidence: 99%

Convolutional neural networks based efficient approach for classification of lung diseases

Demir

Şengür

Bajaj

2019

Health Inf Sci Syst

125

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Treatment of lung diseases, which are the third most common cause of death in the world, is of great importance in the medical field. Many studies using lung sounds recorded with stethoscope have been conducted in the literature in order to diagnose the lung diseases with artificial intelligence-compatible devices and to assist the experts in their diagnosis. In this paper, ICBHI 2017 database which includes different sample frequencies, noise and background sounds was used for the classification of lung sounds. The lung sound signals were initially converted to spectrogram images by using time-frequency method. The short time Fourier transform (STFT) method was considered as time-frequency transformation. Two deep learning based approaches were used for lung sound classification. In the first approach, a pre-trained deep convolutional neural networks (CNN) model was used for feature extraction and a support vector machine (SVM) classifier was used in classification of the lung sounds. In the second approach, the pre-trained deep CNN model was fine-tuned (transfer learning) via spectrogram images for lung sound classification. The accuracies of the proposed methods were tested by using the ten-fold cross validation. The accuracies for the first and second proposed methods were 65.5% and 63.09%, respectively. The obtained accuracies were then compared with some of the existing results and it was seen that obtained scores were better than the other results.

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Section: Related Workmentioning

confidence: 99%

Convolutional neural networks based efficient approach for classification of lung diseases

Demir

Şengür

Bajaj

2019

Health Inf Sci Syst

125

View full text Add to dashboard Cite

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“…The ICBHI Challenge dataset [11] was built in the context of a challenge on respiratory data analysis organized in conjunction with the 2017 Int. Conf.…”

Section: The Icbhi Challengementioning

confidence: 99%

“…The former group includes sensitivity and specificity, and their average, named ICBHIscore. Following the procedure described in [11,14]: Sensitivity = C crackles or wheezes N crackles or wheezes , for the 2-class testbed,…”

Section: Evaluation and Assessment Criteriamentioning

confidence: 99%

“…We also contribute with a preprocessing methodology for a flexible extraction of core groups of cepstral features to feed the inputs to an RNN model. Remarkably, our RNN models were trained and tested using the ICBHI Challenge dataset, which provides an unprecedented, reproducible and standardized benchmark on which new algorithms can be fairly evaluated and compared [11]. Results obtained on the ICBHI benchmark, according to different assessment criteria, highlight the superiority of our RNN-based methods against all selected competitors that participated to the ICBHI Challenge, as well as against a further competitor based on a DL framework.…”

Section: Introductionmentioning

confidence: 98%

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Deep Auscultation: Predicting Respiratory Anomalies and Diseases via Recurrent Neural Networks

Perna

Tagarelli

2019

2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)

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Respiratory diseases are among the most common causes of severe illness and death worldwide. Prevention and early diagnosis are essential to limit or even reverse the trend that characterizes the diffusion of such diseases. In this regard, the development of advanced computational tools for the analysis of respiratory auscultation sounds can become a game changer for detecting disease-related anomalies, or diseases themselves. In this work, we propose a novel learning framework for respiratory auscultation sound data. Our approach combines state-of-the-art feature extraction techniques and advanced deep-neural-network architectures. Remarkably, to the best of our knowledge, we are the first to model a recurrent-neural-network based learning framework to support the clinician in detecting respiratory diseases, at either level of abnormal sounds or pathology classes. Results obtained on the ICBHI benchmark dataset show that our approach outperforms competing methods on both anomaly-driven and pathology-driven prediction tasks, thus advancing the state-of-the-art in respiratory disease analysis. arXiv:1907.05708v1 [eess.AS]

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“…Although smoking is the most common cause of respiratory pathologies, sometimes they are caused by genetics, as well as environmental exposure [1]. The ICBHI (International Conference on Biomedical and Health Informatics) respiratory dataset [2] includes seven pathologies, such as chronic obstructive pulmonary disease (COPD), asthma, upper respiratory tract infection (URTI), lower respiratory tract infection (LRTI), bronchiectasis, pneumonia, and bronchiolitis.…”

Section: Introductionmentioning

confidence: 99%

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

García-Ordás

Benítez-Andrades

García-Rodríguez

et al. 2020

Sensors

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The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.

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Α Respiratory Sound Database for the Development of Automated Classification

Cited by 152 publications

References 10 publications

Convolutional neural networks based efficient approach for classification of lung diseases

Convolutional neural networks based efficient approach for classification of lung diseases

Deep Auscultation: Predicting Respiratory Anomalies and Diseases via Recurrent Neural Networks

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

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