JMIR Publications

ABSTRACT

Background:

Since 2020, peoples’ lifestyles have been largely changed due to the coronavirus pandemic worldwide. In the medical field, although many patients prefer remote medical care, the physician cannot check the patient directly, so it is important for the patients themselves to accurately convey their condition to the physician. Thus, remote medical care should be implemented and adaptable home medical devices are required. However, only a few highly accurate home medical devices are available for automatic wheeze detection as an exacerbation sign.

Objective:

We developed a new handy home medical device with automatic wheeze recognition algorithm, which is available for clinical use in noisy environments, such as a pediatric consultation room or at home. Moreover, the examination time is only 30 s since young children cannot endure long examination time without crying or moving. This study aimed to validate automatic wheeze recognition algorithm developed as a clinical medical device in children at different institutions.

Methods:

In this study, 374 children aged 4–107 months in pediatric consultation rooms of 10 institutions were enrolled. All participants aged ≥6 years were diagnosed with bronchial asthma and ≤5 years had reported at least three episodes of wheezes. Wheezes were detected by auscultation with a stethoscope and recorded for 30 s using the wheeze recognition algorithm (HWZ-1000T) device developed based on wheeze characteristics following the Computerized Respiratory Sound Analysis guideline, where the dominant frequency and duration of a wheeze were >100 Hz and >100 ms, respectively. Files containing recorded lung sounds were assessed by each specialist physician and divided into two groups: 177 designated as “wheeze” files and 197 as “no-wheeze” files. Wheeze recognitions were compared between specialist physicians who recorded lung sounds using the wheeze recognition algorithm and calculated the sensitivity, specificity, positive predictive value, and negative predictive value for all recorded sound files to evaluate the influence of age and sex on the wheeze detection sensitivity.

Results:

Detection of wheezes was not influenced by age and sex. In all files, wheezes were differentiated from noise using the wheeze recognition algorithm. The sensitivity, specificity, positive predictive value, and negative predictive value of the wheeze recognition algorithm were 96.6%, 98.5%, 98.3%, and 97.0%, respectively. Wheezes were automatically detected, and heartbeat sounds, voices, and crying were automatically identified as no-wheeze sounds by the wheeze recognition algorithm.

Conclusions:

The wheeze recognition algorithm was verified to identify wheezing with high accuracy; therefore, it might be useful in the practical implementation of asthma management at home. Only a few home medical devices are available for automatic wheeze detection. The wheeze recognition algorithm was verified to identify wheezing with high accuracy and will be useful for wheezing management at home and in remote medical care.