6 results found
Pramono RXA, Imtiaz SA, Rodriguez-Villegas E, 2019, Evaluation of mel-frequency cepstrum for wheeze analysis, Engineering in Medicine and Biology Society (EMBC), Annual International Conference of the IEEE, Publisher: IEEE, Pages: 4686-4689, ISSN: 1557-170X
Monitoring of wheezes is an integral part of managing Chronic Respiratory Diseases such as asthma and Chronic Obstructive Pulmonary Disease (COPD). Recently, there is a growing interest in automatic detection of wheezes and the use of Mel-Frequency Cepstral Coefficients (MFCC) have been shown to achieve encouraging detection performance. While the successful use of MFCC for identifying wheezes has been demonstrated, it is not clear which MFCC coefficients are actually useful for detecting wheezes. The objective of this paper is to characterize and study the effectiveness of individual coefficients in discriminating between wheezes and normal respiratory sounds. The coefficients have been evaluated in terms of histogram dissimilarity and linear separability. Further, a comparison between the use of single coefficient against other combinations of coefficients is also presented. The results demonstrate MFCC-2 coefficient to be significantly more effective than all the other coefficients in discriminating between wheezes and normal respiratory sounds sampled at 8000 Hz.
Adhi Pramono RX, Anas Imtiaz S, Rodriguez-Villegas E, 2019, Automatic cough detection in acoustic signal using spectral features., 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 7153-7156, ISSN: 1557-170X
Cough is a common symptom that manifests in numerous respiratory diseases. In chronic respiratory diseases, such as asthma and COPD, monitoring of cough is an integral part in managing the disease. This paper presents an algorithm for automatic detection of cough events from acoustic signals. The algorithm uses only three spectral features with a logistic regression model to separate sound segments into cough and non-cough events. The spectral features were derived using simple calculation from two frequency bands of the sound spectrum. The frequency bands of interest were chosen based on its characteristics in the spectrum. The algorithm achieved high sensitivity of 90.31%, specificity of 98.14%, and F1-score of 88.70%. Its low-complexity and high detection performance demonstrate its potential for use in remote patient monitoring systems for real-time, automatic cough detection.
Pramono RXA, Imtiaz SA, Rodriguez-Villegas E, 2019, Automatic identification of cough events from acoustic signals., 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Pages: 217-220
Cough is a common symptom of numerous respiratory diseases. In certain cases, such as asthma and COPD, early identification of coughs is useful for the management of these diseases. This paper presents an algorithm for automatic identification of cough events from acoustic signals. The algorithm is based on only four features of the acoustic signals including LPC coefficient, tonality index, spectral flatness and spectral centroid with a logistic regression model to label sound segments into cough and non-cough events. The algorithm achieves sensitivity of of 86.78%, specificity of 99.42%, and F1-score of 88.74%. Its high performance despite its small size of feature-space demonstrate its potential for use in remote patient monitoring systems for automatic cough detection using acoustic signals.
Pramono RXA, Imtiaz SA, Rodriguez-Villegas E, 2019, Evaluation of features for classification of wheezes and normal respiratory sounds, PLoS ONE, Vol: 14, Pages: e0213659-e0213659, ISSN: 1932-6203
Chronic Respiratory Diseases (CRDs), such as Asthma and Chronic Obstructive Pulmonary Disease (COPD), are leading causes of deaths worldwide. Although both Asthma and COPD are not curable, they can be managed by close monitoring of symptoms to prevent worsening of the condition. One key symptom that needs to be monitored is the occurrence of wheezing sounds during breathing since its early identification could prevent serious exacerbations. Since wheezing can happen randomly without warning, a long-term monitoring system with automatic wheeze detection could be extremely helpful to manage these respiratory diseases. This study evaluates the discriminatory ability of different types of feature used in previous related studies, with a total size of 105 individual features, for automatic identification of wheezing sound during breathing. A linear classifier is used to determine the best features for classification by evaluating several performance metrics, including ranksum statistical test, area under the sensitivity--specificity curve (AUC), F1 score, Matthews Correlation Coefficient (MCC), and relative computation time. Tonality index attained the highest effect size, at 87.95%, and was found to be the feature with the lowest p-value when ranksum significance test was performed. Third MFCC coefficient achieved the highest AUC and average optimum F1 score at 0.8919 and 82.67% respectively, while the highest average optimum MCC was obtained by the first coefficient of a 6th order LPC. The best possible combination of two and three features for wheeze detection is also studied. The study concludes with an analysis of the different trade-offs between accuracy, reliability, and computation requirements of the different features since these will be highly useful for researchers when designing algorithms for automatic wheeze identification.
Pramono R, Bowyer S, Rodriguez Villegas E, 2017, Automatic adventitious respiratory sound analysis: A systematic review, PLOS One, Vol: 12, ISSN: 1932-6203
BackgroundAutomatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established.ObjectiveTo provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works.Data sourcesA systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification.Study selectionOnly articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated.Data extractionInvestigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved.Data synthesisA total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused
Pramono R, Imtiaz SA, Rodriguez Villegas ESTHER, 2016, A Cough-Based Algorithm for Automatic Diagnosis of Pertussis, PLOS One, Vol: 11, ISSN: 1932-6203
Pertussis is a contagious respiratory disease which mainly affects young children and can be fatal if left untreated. The World Health Organization estimates 16 million pertussis cases annually worldwide resulting in over 200,000 deaths. It is prevalent mainly in developing countries where it is difficult to diagnose due to the lack of healthcare facilities and medical professionals. Hence, a low-cost, quick and easily accessible solution is needed to provide pertussis diagnosis in such areas to contain an outbreak. In this paper we present an algorithm for automated diagnosis of pertussis using audio signals by analyzing cough and whoop sounds. The algorithm consists of three main blocks to perform automatic cough detection, cough classification and whooping sound detection. Each of these extract relevant features from the audio signal and subsequently classify them using a logistic regression model. The output from these blocks is collated to provide a pertussis likelihood diagnosis. The performance of the proposed algorithm is evaluated using audio recordings from 38 patients. The algorithm is able to diagnose all pertussis successfully from all audio recordings without any false diagnosis. It can also automatically detect individual cough sounds with 92% accuracy and PPV of 97%. The low complexity of the proposed algorithm coupled with its high accuracy demonstrates that it can be readily deployed using smartphones and can be extremely useful for quick identification or early screening of pertussis and for infection outbreaks control.
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