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

Abstract

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.

Computerized Adventitious Respiratory Sounds as Outcome Measures for Respiratory Therapy: A Systematic Review

“…7 Thus, this approach, through objective data collection and management, generation of permanent records of the measurements made with easy retrievability, and graphic representations, assists with the diagnosis and monitoring of respiratory diseases. [8][9][10][11] Research on this topic has been focusing on the use of CORSA as a diagnostic aid 12 and the findings reporting its potential to be used as an outcome measure, that is, to monitor respiratory treatments, are widespread in the literature. Thus, this systematic review synthesizes the evidence on the use of computerized ARSs as outcome measures.…”

Identification of Pulmonary Disorders by Using Different Spectral Analysis Methods

“…Because of advanced technological developments in computer and electronic environments, it is possible to process sound-based signal reliably in different pulmonary conditions. Computer-based respiratory sound analysis contains recording the patient's respiratory sounds by an electronic device, followed by analysis of respiratory sound signals with signal processing techniques and classification of respiratory sounds according to their specific characteristics [4].…”

“…This type of sounds is divided into two main classes, wheeze and crackle [3]. The frequency of wheezes lies within 100Hz and 2500 Hz [4] with a dominant frequency between 100 Hz and 1000 Hz [4]. According to American Thoracic Society (ATS) wheezes have a dominant frequency greater than 400 Hz [5,6].…”

An alternative respiratory sounds classification system utilizing artificial neural networks

“…[2,11] Gurung et al conducted a review and an in-depth analysis of relevant classification studies using a computerized lung sound analysis in which the overall sensitivity and specificity of implemented algorithms were estimated. [12] Nowadays, the use of computerized diagnostic tools enjoys wide popularity in the healthcare facilities. [13,14] The present study aims at introducing a novel, fully automated, accurate, and easy-to-use RS classification system that helps physicians to perform diagnosis without the need for invasive medical imaging techniques.…”