Two-level coarse-to-fine classification algorithm for asthma wheezing recognition in children's respiratory sounds

Mazić, Igor; Bonković, Mirjana; Džaja, Barbara

doi:10.1016/j.bspc.2015.05.002

Cited by 49 publications

(18 citation statements)

References 26 publications

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“…Wheeze detection using Mel Frequency Cepstral Coefficients (MFCC), kurtosis, and entropy as features was developed in [ 53 ]. 45 recordings for the analysis were obtained using an accelerometer (BU-3173).…”

Section: Resultsmentioning

confidence: 99%

Automatic adventitious respiratory sound analysis: A systematic review

2017

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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 on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis.LimitationsDirect comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions.ConclusionA review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventiona...

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Section: Resultsmentioning

confidence: 99%

Automatic adventitious respiratory sound analysis: A systematic review

2017

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“…It entails identification of an unknown number of intermittently appearing, temporally evolving frequency lines, embedded in respiratory noise [4]. Algorithm implementing this processing-intensive task most commonly combines spectrotemporal features drawn from the short-term Fourier transform (STFT) [10][11][12][13][14], Mel-frequency cepstral domain (MFC) [15,16], wavelet transform [17,18], empirical mode decomposition [19], and a variety of classification schemes, including decision trees [10,12], neural networks [18], and support vector machine [13][14][15]18]. Detailed reviews given in [12,18,20] report classification performance ranging on average from 90 to 95% of sensitivity and specificity.…”

Section: Introductionmentioning

confidence: 99%

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

Oletić

Bilas

2018

Journal of Sensors

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Long-term quantification of asthmatic wheezing envisions an m-Health sensor system consisting of a smartphone and a body-worn wireless acoustic sensor. As both devices are power constrained, the main criterion guiding the system design comes down to minimization of power consumption, while retaining sufficient respiratory sound classification accuracy (i.e., wheeze detection). Crucial for assessment of the system-level power consumption is the understanding of trade-off between power cost of computationally intensive local processing and communication. Therefore, we analyze power requirements of signal acquisition, processing, and communication in three typical operating scenarios: (1) streaming of uncompressed respiratory signal to a smartphone for classification, (2) signal streaming utilizing compressive sensing (CS) for reduction of data rate, and (3) respiratory sound classification onboard the wearable sensor. Study shows that the third scenario featuring the lowest communication cost enables the lowest total sensor system power consumption ranging from 328 to 428 μW. In such scenario, 32-bit ARM Cortex M3/M4 cores typically embedded within Bluetooth 4 SoC modules feature the optimal trade-off between onboard classification performance and consumption. On the other hand, study confirms that CS enables the most power-efficient design of the wearable sensor (216 to 357 μW) in the compressed signal streaming, the second scenario. In such case, a single low-power ARM Cortex-A53 core is sufficient for simultaneous real-time CS reconstruction and classification on the smartphone, while keeping the total system power within budget for uncompressed streaming.

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“…During the last few years, considerable work has been done regarding automated lung sound classification. More specifically, Mazić et al [ 8 ] applied feature sets containing MFCC, kurtosis and entropy features on a cascade SVM structure consisted of two parallel SVMs that use different values (5.0 and 2.0, respectively) for the gamma parameter of the radial basis function (RBF) kernel and fused their predictions to separate respiratory sounds into wheezes and non-wheezes, while Matsutake et al [ 9 ] classified normal and abnormal respiratory sounds using HMMs and maximum likelihood estimation. A study by Sen et al [ 10 ] compared the performances of a Gaussian mixture model (GMM) and an SVM classifier on recognizing normal and pathological lung sounds, while another study by Mendes et al [ 11 ] proposed a logistic regression (LR) and a random forest (RF) classifier to evaluate the performance of 30 different features in detecting wheezes from respiratory signals.…”

Section: Related Workmentioning

confidence: 99%

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

Petmezas

Cheimariotis

Stefanopoulos

et al. 2022

Sensors

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Respiratory diseases constitute one of the leading causes of death worldwide and directly affect the patient’s quality of life. Early diagnosis and patient monitoring, which conventionally include lung auscultation, are essential for the efficient management of respiratory diseases. Manual lung sound interpretation is a subjective and time-consuming process that requires high medical expertise. The capabilities that deep learning offers could be exploited in order that robust lung sound classification models can be designed. In this paper, we propose a novel hybrid neural model that implements the focal loss (FL) function to deal with training data imbalance. Features initially extracted from short-time Fourier transform (STFT) spectrograms via a convolutional neural network (CNN) are given as input to a long short-term memory (LSTM) network that memorizes the temporal dependencies between data and classifies four types of lung sounds, including normal, crackles, wheezes, and both crackles and wheezes. The model was trained and tested on the ICBHI 2017 Respiratory Sound Database and achieved state-of-the-art results using three different data splitting strategies—namely, sensitivity 47.37%, specificity 82.46%, score 64.92% and accuracy 73.69% for the official 60/40 split, sensitivity 52.78%, specificity 84.26%, score 68.52% and accuracy 76.39% using interpatient 10-fold cross validation, and sensitivity 60.29% and accuracy 74.57% using leave-one-out cross validation.

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Two-level coarse-to-fine classification algorithm for asthma wheezing recognition in children's respiratory sounds

Cited by 49 publications

References 26 publications

Automatic adventitious respiratory sound analysis: A systematic review

Automatic adventitious respiratory sound analysis: A systematic review

System-Level Power Consumption Analysis of the Wearable Asthmatic Wheeze Quantification

Automated Lung Sound Classification Using a Hybrid CNN-LSTM Network and Focal Loss Function

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