Table 1 provides a summary of normal breath sounds, while adventitious sounds are summarised in Table 2. This work, 77 articles, focused on automatic adventitious respiratory sound analysis.Ī standardised approach was used for this systematic review ( Fig 1 and S1 File)., 2011, 8 articles, focused on abnormal lung sound detection., 2012, 21 articles, focused on wheeze analysis for asthma patients., 2013, 55 articles, focused on computer-based respiratory sound analysis., 2013, 34 articles, focused on machine learning techniques in lung sound analysis., 2015, 7 articles, focused on COPD patients., 2015, 49 articles, focused on respiratory sound analysis.The comparison of this work with the previously mentioned reviews can be seen below. This systematic review adds to these existing reviews by providing more thorough information in a standardised format, with more works being reviewed, and more recent developments included. This obtained an average of 80% sensitivity and 85% specificity in abnormal sound detection. Four of the selected articles were then used for meta-analysis. The NOS is normally used for assessing non-randomised studies including control-studies. The quality of each study was assessed using the Newcastle-Ottawa Score (NOS). The review included the number of subjects, age range, gender ratio, methodology, case, recording device, algorithm, and type of sounds analysed. A total of 8 articles were selected for this systematic review which consisted of studies on wheeze, crackle, and other adventitious sounds for specific diseases such as asthma and COPD. The study recommended placing the stethoscope on the trachea as this preserves more frequency information when compared to the chest wall.Ī systematic review and meta-analysis of computerised lung sound analysis to aid in the diagnosis of diseases was presented in. The survey in focused on automated wheeze detection for asthmatic patients, and provided a review on instrumentation, placement, processing methods, features used, and the outcome, of a total of 27 studies. This work provided several recommendations for sensor type, position, and the use of more advanced machine learning techniques. The review included analysis type, sensor used, data set used, sensor location, and method of analysis. Another review, published by the same group, provided a summary of 55 studies on computer-based respiratory sound analysis. The review concluded that artificial intelligence techniques are needed to improve accuracy and enable commercialisation as a product. This covered types of analysis, sensor type, number of subjects, machine learning techniques used, and the outcome of each reference. The review in provided information on machine learning techniques used in lung sound analysis. The focus of this review was studies that tried to find the characteristics of adventitious sounds in COPD (wheeze, crackle, and rhonchi), including occurrence timing and the power spectrum. The conclusion of this work was that a multi domain feature has advantages in characterising different types of lung sounds.Ī review of computerised respiratory sounds specifically in patients with COPD was done in. Information on analysis type, approach, and data management was not reviewed. Signal pre-processing techniques such as de-noising, resampling, and analogue pre-filtering were also presented, as well as the number of sensors and their positioning. The review categorised features into time-domain, frequency-domain, wavelet-domain, and a combination of different domains. The article in provided a review of 49 articles which included the type of sensor, the data set, the features, the analysis techniques, and also the performance metrics used. Several reviews related to automatic adventitious sounds analysis have been published. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has been the focus of an increasing number of studies recently, a standardised approach and comparison has not been well established. However, the correct detection of these sounds relies on both, the presence of an “expert”, and their degree of expertise.

An expert can perform auscultation using a stethoscope to detect abnormalities in sounds and use this information when making a diagnosis. The latter are referred to as adventitious sounds. Examples of this could be the absence of sounds or additive unusual ones. Airway abnormalities can cause breathing sounds to be abnormal.

These include asthma, COPD, and pneumonia amongst others. Most diseases related to an obstructed or restricted respiratory system can be characterised from the sounds generated while breathing.

Sibilant rhonchi lung sounds