Acoustic biomarkers in asthma: a systematic review

Figures & data

Figure 1. Workflow representing phases of the systematic review process (PRISMA criteria (43)).

Table 1. Study details and patient population characteristics.

No	PMID/doi	Author	Year	Country	Study setting	Study aim	Study measure	Study (days)	Endpoint	n	M/F	Patient population characteristics
1	33363391	Tinschert et al.	2020	Switzerland	Real-world	Validate cough frequency algorithm to identify and predict asthma control	Uncontrolled asthma detection and prediction of attacks using cough frequency	29	Primary endpoint–Asthma Control test–weekly, secondary–self reported asthma attacks	79	M = 41, F = 53	N = 79 (self-reported asthma)
2	23000725	Habukawa et al.	2013	Japan	Clinic-based study	Determine if breath sound analysis could be used to detect changes in transfer characteristics according to the control level of asthma	Pediatric respiratory sounds analysis	1	Asthma control assessment	139	M = 61, F = 78	N = 80 (physician reported asthma)
3	31167662	Porter et al.	2019	Australia	Clinic-based study	Validate an algorithm for pediatric respiratory diagnoses including asthma and reactive airway disease (RAD)	Pediatric cough sounds analysis	1	Automated diagnosis algorithm	585		N = 149
4	30439599	Nabi et al.	2019	Pakistan	Inpatient hospital appointment	Investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate, severe).	Analysis of spectral integrated features	NR	Wheeze sound classification	55	M = 42, F = 21	MILD asthma = 17, moderate = 18, severe = 20
5	33018992	Nemati et al.	2020	USA	Hospital	Analyze cough sounds to estimate lung function and severity of the disease	Voluntary cough recording	NR	Analysis of cough sounds	70	M = 28, F = 42	N = 70 (asthmatic = 25)
6	33484516	Nabi et al.	2021	Pakistan	In-patient hospital	Investigate behavior of frequency related features in three severity levels of asthma patients (mild, moderate and severe)	Frequency based features and mean frequency	NR	Severity of asthma (clinical judgement)	55	M = 34, F = 21	Asthmatic patients (physical reported)
7	23366456	Holmes et al.	2012	Ireland	Real-world	An automatic detection algorithm to identify inhalations from acoustic recordings of inhaler use.	Inhalation recordings	92	Inhaler use over 3 months	20	M = 9, F = 11	Asthmatic patients
8	27170883	Holmes et al.	2014	Ireland	Real-word	An algorithm to automatically analyze patient inhaler use in order to evaluate adherence.	Inhalation recordings	30	Adherence over 1 month.	12		Asthmatic patients
9	10.1109/ICASSP40776.2020.9054062	Yadav et al.	2020	India	Inpatient hospital	Validity of an algorithm detecting asthma in spontaneous speech	Analysis of speech to recognize asthma patients	NR	Compare the use of speech and non-speech sound in asthmatic vs. healthy subject classification	95	M = 28, F = 19	N = 48 asthmatic, n = 47 healthy patients
10	10.1109/TED.2019.2951580	Khan et al.	2020	Saudi Arabia	Laboratory	Test the design of a body-worn sensor detecting wheezing	Analysis of wheezing episodes	NR	Wheeze detection (automated count)	4	M = 2 F = 2	N = 3 prerecorded wheeze sounds from asthmatic patients
11	10.1109/WH.2016.7764567	Ra et al.	2021	South Korea	Laboratory	Investigate and classify wheezing in respiratory sounds using an automated system	Wheezing sounds analysis	NR	Wheeze detection (automated count)	NR	NR	N = 31 wheezing asthmatic patients, n = 32 healthy
12	10.1016/j.bspc.2015.05.002	Mazić et al.	2015	Croatia	Inpatient hospital	Investigate and classify wheezing in children’s respiratory sounds	Children’s respiratory sounds	NR	Respiratory sound analysis	16	NR	N = 16 asthmatic and obstructive bronchitis patients
13	33299332	Rassouli et al.	2020	Switzer-land	Inpatient hospital recording	Investigate the prevalence and characteristics of nocturnal cough in asthmatic patients	Manual cough counting, pattern recognition	29	Primary–nocturnal cough rate manually labeled by human annotators. Secondary–cough severity.	94	M = 41, F = 53	N = 94 asthmatic patients
14	10.1109/EMBC.2018.8512452	Azam et al.	2018	Pakistan	Hospital recording	Analyze breath sounds to diagnose abnormalities present	Respiratory sound analysis	NR	Respiratory sound analysis	50	NR	N = 50 patients with COPD, asthma, or bronchitis
15	30927097	Grzywalski et al.	2019	Poland	Inpatient hospital	Analyze auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles	Analysis of breath sounds	NR	Automated analysis of breath sounds	50	NR	NR
16	29346430	Taylor et al.	2018	Ireland	Inpatient hospital	Estimation of inhalation technique	Analysis of sound to assess inhaler use	NR	Estimation of inhalation flow profile to assess inhaler medication adherence	20	NR	N = 20 healthy participants
17	36699651	Kuhn et al.	2023	Switzer-land	Hospital, outpatient	Evaluate the performance of the cough detector	Number of time-stamped cough epochs	8	Cough counting	5	NR	N = 5 diagnosed with asthma by pulmonologist
18	35989223	Kuluozturk et al.	2022	Turkey	Clinic-based study	Training and validating a novel model designed for cough detection.	Accuracy for cough sound-based multiclass classification of diseases.	153	Automated cough detection	110	NR	Physician diagnosed
19	33875413	Habukawa et al.	2021	Japan	Clinic-based study	Validate an automatic wheeze recognition algorithm as a clinical medical device in children.	Wheeze sound analysis	153	Recognize wheeze from sound	374	M = 241, F = 133	N = 374 with mild bronchial asthma
20	33019267	Vatanparvar et al.	2020	USA	NA	Investigate unique features in a cough sound and validate a model for cough detection.	Cough sound detection	NR	Validation of algorithm to recognize coughs	69		N = 60 physician diagnosed asthma
21	35263208	Porter et al.	2023	Australia	Hospital	Develop and test the accuracy of a smartphone-based diagnostic algorithm that detects exacerbations.	Cough sound detection	NR	Exacerbation prediction compared with actual admissions	119	M = 33, F = 86	N = 119 physician diagnosed asthma
22	32459641	Barata et al.	2020	Switzerland	Home monitoring	Early recognition of exacerbations	Recognition and segmentation of nocturnal asthmatic coughs and epochs	28	Automated cough counting as predictor for exacerbations	94	M = 40, F = 54	NR
23	29990246	Oletic et al.	2018	Malaysia	NA	Algorithm description for wheeze quantification	Wheeze sound analysis	NR	Asthmatic wheeze detection	16		N = 8 asthmatic, n = 8 healthy patients
24	29650306	Islam et al.	2018	India	Research center	Classification of normal and asthmatic subjects using signal processing of posterior lung sound signals	Breath sounds analysis	NR	Classification of normal and asthmatic subjects	60		N = 30 healthy patients, n = 30 asthma (n = 8 mild, n = 11 moderate, n = 11 severe)
25	33400647	Sen et al.	2021	Turkey	Hospital	Developing a method to differentiate between asthma and COPD using breath sounds.	Breath sounds analysis	1	Classification of asthma and COPD	30		Diagnosed by FEV1/FVC values below 0.7 on the day.
26	19085584	Guntupalli et al.	2008	USA	Hospital	Evaluate the utility of vibration response imaging in differentiating between asthma and COPD.	Vibration response imaging validation (percussion-like device measurements)	1	Differentiating between COPD and asthma using vibration imaging	66		Diagnosed with medical history, physical exam, spirometry
27	10.1002/ppul.23125	Hirai et al.	2015	Japan	Hospital	Develop a new cough monitoring system for children.	Validate cough monitoring system	2	Cough counting algorithm	3	M = 6, F = 4	Clinical decision made based on previous records

NR: not reported.

Table 2. Data collection methods, study results, conclusions and risk of bias (SN: sensitivity; SP: specificity; ACC: accuracy; NR: not reported).

No	PMID/doi	Author	Data collection method	Device	Signal: type, number		Algorithm performance: SN, SP, ACC			Conclusion
1	33363391	Tinschert et al.	Smartphone	Smartphone	Cough	2291	54	67	61	Statistically significant association of nocturnal cough and sleep quality with asthma control
2	23000725	Habukawa et al.	Custom device	Trademarked device	Breath sounds	139	83.6	84.2	NR	Breath sound analysis was able to identify patients without asthma, with mild asthma or severe asthma
3	31167662	Porter et al.	Smartphone	Smartphone	Breath sounds	2925		*		Breath sound analysis was able to identify patients with asthma. Positive percent agreement in three age groups: 97% (29 days–12 years), 80% (29 days–2 years), 98% (2–12 years). Negative Percent Agreement was 91, 97, and 88%, respectively.
4	30439599	Nabi et al.	Audio auscultation device	Trademarked device (contact device)	Wheeze	775	95	99		The classifier exhibited good performance in terms of sensitivity, specificity, and PPV.
5	33018992	Nemati et al.	Smartphone, smartwatch, microphone	Smartphone, smartwatch, microphone	Cough	3695	NR	NR	95	Accurate estimation of lung obstruction severity based on acoustic features of cough.
6	33484516	Nabi et al.	Digital stethoscope	Trademark device	Wheeze	775		*		All wheeze samples indicate significant differences in severity levels.
7	23366456	Holmes et al.	Custom device	Trademarked device	Inhaler	255	95	94	89	The algorithm was able to detect inhalations from an inhaler.
8	27170883	Holmes et al	Custom device	Trademarked device	Inhaler	407	98.3	98.4	92.1	Cohen’s kappa agreement score was >0.81
9	10.1109/ICASSP40776.2020.9054062	Yadav. et al	Microphone	Microphone	Speech	2938	NR	NR	77.8	Features of speech can be used for asthmatic and healthy subject classification.
10	10.1109/TED.2019.2951580	Khan et al.	Custom device	Trademarked device (contact device)	Wheeze, speech, cough	NR		*		A flexible acoustic sensor can be used to detect wheezing in asthma. The gage repeatability and reproducibility calculates the repeatability as 92.12%.
11	10.1109/WH.2016.7764567	Ra et al.	Digital stethoscope	Trademarked device (contact device)	Breath sounds	103	100	95	98	An ecosystem that allows healthcare professionals to collect physiological data with good results.
12	10.1016/j.bspc.2015.05.002	Mazić et al.	Microphone	Microphone and computer	Wheeze	45	99.67	99.94	NR	The two-level algorithm for wheezing detection in children’s respiratory sounds can discriminate wheeze signals from non-wheeze sounds.
13	33299332	Rassouli et al.	Smartphone	Smartphone	Cough	704,698		*		Patients coughed in 53% of 2212 nights, The highest counts were measured in the first 30 min in bed.
14	10.1109/EMBC.2018.8512452	Azam et al.	Smartphone	Smartphone	Breath sounds	255	NR	NR	75.21	Human breath sound can be used to detect irregular patterns occurring in respiratory cycles due to asthma.
15	30927097	Grzywal-ski et al.	Digital stethoscope	Trademarked device (contact device)	Breath sounds	522	76.5	83.6	NR	The implementation of automatic sound analysis can significantly improve the efficiency of interpretation of auscultation sounds.
16	29346430	Taylor et al.	Custom device	Trademarked device	Inhalator	300	NR	NR	90.89	The method generated sufficient accuracy in estimating inhalation parameters, such as peak expiratory flow and inspiratory capacity.
17	36699651	Kuhn et al.	Smartphone	Smartphone	Cough	648	88.55	99.97	NR	The cough monitoring system was successful and showed similar metrics as other automated cough detection systems.
18	35989223	Kuluo-zturk et al.	Smartphone	Smartphone	Cough	787	NR	NR	99.39	The model possesses an excellent multiclass classification accuracy of 99.39% for cough sound-based diagnosis.
19	33875413	Habukawa et al.	Custom device	Custom device	Wheeze	1201	96.6	98.5	NR	The wheeze recognition algorithm was verified to identify wheezing with high accuracy.
20	33019267	Vatanpa-rvar et al.	Smartphone	Smartphone	Cough	5380	90.3	96.39	93.34	The method enables subject-specific collection of coughs with limited error.
21	35263208	Porter et al.	smartphone	NR	Cough	2925	63.8	94.9	73.95	Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing.
22	32459641	Barata et al.	Smartphone	NR	Cough	30304	99.9	91.5	99.8	Our study showed longitudinal nocturnal cough and cough-epoch recognition from nightly recorded smartphone-based audio from adults with asthma.
23	29990246	Oletic et al.	Sound database recordings	Microphone and smartphone	Wheeze	7480	89.34	96.28	94.91	The proposed algorithm identifies occurrence and tracks multiple individual wheeze frequencies.
24	29650306	Islam et al.	Custom	4-channel data acquisition system	Breath sound	NR	NR	NR	93.3	The proposed multichannel asthma detection method yields good results in cough identification.
25	33400647	Sen et al.	Custom	14-channcel pulmonary sounds data acquision device	Breath sound	9600	NR	NR	100	Pulmonary sounds analysis may be a complementary tool in clinical practice for differential diagnosis of asthma and COPD, especially in the absence of reliable spirometry testing.
26	19085584	Guntupalli et al.	Vibration device	VRI Device System (contact device)	Vibration	NR	NR	NR	84	Combined qualitative and quantitative evaluations of lung sounds are sensitive in distinguishing between lung sound recordings of COPD and asthma individuals.
27	10.1002/ppul.23125	Hirai et al.	Video camera and microphone with accelerometer	Microphone: EM167NM2	Cough	NR	98,8	97,8	NR	Our method demonstrated good agreement with the video-audio recording method in children and is considered to be highly useful for the objective monitoring of overnight cough in children with respiratory diseases.

*Results in conclusions.

Table 3. QUADAS-2 risk of bias and applicability of studies.

	Year	Author	Risk of bias				Applicability concerns
			Patient selection	Index test	Reference standard	Flow and timing	Patient selection	Index test	Reference standard
1	2020	Tinschert et al.
2	2013	Habukawa et al.
3	2019	Porter et al.
4	2019	Nabi et al.
5	2020	Nemati et al.
6	2021	Nabi et al.
7	2012	Holmes et al.
8	2014	Holmes et al
9	2020	Yadav et al
10	2020	Khan et al.
11	2021	Ra et al.
12	2015	Mazić et al.
13	2020	Rassouli
14	2018	Azam et al.			?	?
15	2019	Grzywalski et al.
16	2018	Taylor et al.
17	2023	Kuhn et al.	?				?
18	2022	Kuluozturk
19	2021	Habukawa et al.
20	2020	Vatanparvar et al.			?	?
21	2023	Porter et al.
22	2020	Barata et al.	?				?
23	2018	Oletic et al.			?	?
24	2018	Islam et al.
25	2021	Sen et al.			?	?
26	2008	Guntupalli
27	2015	Hirai et al.			?	?

Note. Orange—high risk, green—low risk, question mark—unclear risk.

Figure B1. Data extraction strategy followed by authors for the data extraction.

Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JPA, Clarke M, Devereaux PJ, Kleijnen J, Moher D, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009;339(jul21 1):b2700–b2700. doi:10.1136/bmj.b2700.

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