Evaluation of the Usefulness of Spectral Analysis of Inspiratory Lung Sounds Recorded with Phonopneumography in Patients with Interstitial Pneumonia

Ono, Hiroshi; Taniguchi, Yasuyuki; Shinoda, Kinya; Sakamoto, Tetsu; Kudoh, Shoji; Gemma, Akihiko

doi:10.1272/jnms.76.67

Cited by 19 publications

(11 citation statements)

References 9 publications

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“…These include the Wheezometer [28], Wholter [29], VRI [30], LSA-2000 [31], LEOSound [32], Multichannel STG [33], STG for PC [34], and Handheld STG [35]. …”

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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“…These include the Wheezometer [28], Wholter [29], VRI [30], LSA-2000 [31], LEOSound [32], Multichannel STG [33], STG for PC [34], and Handheld STG [35]. …”

Section: Resultsmentioning

confidence: 99%

Automatic adventitious respiratory sound analysis: A systematic review

2017

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“…Three studies specifically conducted computerized lung sound analysis for classification of wheezing (9-11), two analyzed crackles (12,14) and three analyzed both wheezes and crackles (1,10,13). Murphy et al .…”

Section: Resultsmentioning

confidence: 99%

“…Five studies used microphones for electronic auscultation (1, 8, 10, 12, 14) and two used piezo-electric sensors (13, 17). Riella et al .…”

Section: Resultsmentioning

confidence: 99%

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Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis

Gurung

Scrafford

Tielsch

et al. 2011

Respiratory Medicine

192

107

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Rationale The standardized use of a stethoscope for chest auscultation in clinical research is limited by its inherent inter-listener variability. Electronic auscultation and automated classification of recorded lung sounds may help prevent some these shortcomings. Objective We sought to perform a systematic review and meta-analysis of studies implementing computerized lung sounds analysis (CLSA) to aid in the detection of abnormal lung sounds for specific respiratory disorders. Methods We searched for articles on CLSA in MEDLINE, EMBASE, Cochrane Library and ISI Web of Knowledge through July 31, 2010. Following qualitative review, we conducted a meta-analysis to estimate the sensitivity and specificity of CLSA for the detection of abnormal lung sounds. Measurements and Main Results Of 208 articles identified, we selected eight studies for review. Most studies employed either electret microphones or piezoelectric sensors for auscultation, and Fourier Transform and Neural Network algorithms for analysis and automated classification of lung sounds. Overall sensitivity for the detection of wheezes or crackles using CLSA was 80% (95% CI 72–86%) and specificity was 85% (95% CI 78–91%). Conclusions While quality data on CLSA are relatively limited, analysis of existing information suggests that CLSA can provide a relatively high specificity for detecting abnormal lung sounds such as crackles and wheezes. Further research and product development could promote the value of CLSA in research studies or its diagnostic utility in clinical setting.

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“…Similarly, after two years later, Kahya et al (2006) also included customized parameters such as crackles parameters based on the duration of crackles during a breath cycle, and employed k-nearest neighbor algorithm for automatic classification, and archiving acceptable prediction accuracy. Ono et al (2009) focused on interstitial pneumonia (IP) and investigated inspiratory lung sounds with Fast Fourier transformation to convert the data into a machine usable digitized format. Mohammed (2009), in order to classify pulmonary sounds, used cepstral analysis and Gaussian mixture models and achieved prediction accuracy close to 90%.…”

Section: Introductionmentioning

confidence: 99%

A comparative analysis of machine learning methods for classification type decision problems in healthcare

et al. 2014

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Advanced analytical techniques are gaining popularity in addressing complex classification type decision problems in many fields including healthcare and medicine. In this exemplary study, using digitized signal data, we developed predictive models employing three machine learning methods to diagnose an asthma patient based solely on the sounds acquired from the chest of the patient in a clinical laboratory. Although, the performances varied slightly, ensemble models (i.e., Random Forest and AdaBoost combined with Random Forest) achieved about 90% accuracy on predicting asthma patients, compared to artificial neural networks models that achieved about 80% predictive accuracy. Our results show that noninvasive, computerized lung sound analysis that rely on low-cost microphones and an embedded real-time microprocessor system would help physicians to make faster and better diagnostic decisions, especially in situations where x-ray and CT-scans are not reachable or not available. This study is a testament to the improving capabilities of analytic techniques in support of better decision making, especially in situations constraint by limited resources.

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Evaluation of the Usefulness of Spectral Analysis of Inspiratory Lung Sounds Recorded with Phonopneumography in Patients with Interstitial Pneumonia

Cited by 19 publications

References 9 publications

Automatic adventitious respiratory sound analysis: A systematic review

Automatic adventitious respiratory sound analysis: A systematic review

Computerized lung sound analysis as diagnostic aid for the detection of abnormal lung sounds: A systematic review and meta-analysis

A comparative analysis of machine learning methods for classification type decision problems in healthcare

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