Wearable, Always-on Stethoscope for Early Detection of Asthma Attack

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ABSTRACT This research aims to develop and test a wearable, always-on stethoscope to provide a solution to the unmet need for the quantification of respiratory symptoms. Such a device is necessary for the accurate diagnosis of asthma and assessment of asthma control in 6.8 million infants, young children and other populations with intellectual disability who are unable to report their respiratory symptoms or perform lung function testing in the United States. With accurate diagnosis and assessment of control, appropriate asthma therapy can be initiated without delays to minimize adverse asthma outcomes. The key elements needed in the proposed wearable stethoscope (i.e., a wireless stethoscope without a bulky acoustic coupler) are (1) resonant microphone array with unprecedented sound detectability over 100 – 800 Hz and (2) ultra-low power signal processing. We propose to develop a bank of acoustically-filtering microphones that are based on a high Q (quality factor) resonance of a microphone diaphragm, for accurate detection of abnormal lung sounds. A bank of Q-filtered and Q-enhanced microphones is proposed so that (1) feature-extracting filters may be avoided and (2) extremely small lung sounds can be detected from the chest without an acoustic coupler. Lung sounds are very difficult to detect from the chest without a bulky acoustic coupler, as the sound pressure level (SPL) is only 22 – 30 dB SPL, in free space, over a frequency range of 100 – 800 Hz. This kind of SPL cannot be accurately detected by a commercial miniature microphone. Thus, we will develop and use an array of 8 resonant microphones with Q of 40 – 60 (fabricated with a microfabrication process) to detect lung sounds down to 22 dB SPL, 4 dB lower than the lowest sound a human ear can detect, and to automatically segment the sound’s frequency components into 8 different narrowly-band-passed frequency regions over 100 – 800 Hz. We hypothesize that continuous ambulatory monitoring of lung sounds for acoustic characteristics of asthma will improve the diagnostic accuracy and treatment in pediatric asthma patients. Currently, asthma is diagnosed in small children primarily through caregiver history and brief in-office exam as direct patient history and respiratory function testing are not available for these young patients. The proposed device is entirely novel as currently there is no device capable of providing round-the-clock monitoring for signs of asthma. While conventional microphones can detect cough and overt wheezing, their utility is limited by insufficient sensitivity, and are not amenable for continuous, ambulatory monitoring. The proposed resonant microphone array will be integrated with ultralow power electronics for a wearable stethoscope that continuously tracks lung sounds for the detection of asthma signs and symptoms such as cough and wheeze which may occur during exercise or sleep (when caregivers are not present) and may be misreported or ignored by young children. Wirelessly transmitted abnormal lung sounds will be accessed through the internet. The proposed stethoscope will provide an unprecedented means for those incapable of providing an accurate history or difficulty with self-management, such as pre-verbal children and individuals with intellectual disability, to avoid serious asthma morbidity. The device detects signs of uncontrolled asthma and notifies the parent, caregiver and medical professionals through the internet. In doing so, we may improve the management of asthma patients through more accurate tracking of environmental and behavioral triggers which can be used to improve management. To test the potential clinical utility of the wearable stethoscope, we will first record lung sounds with conventional electronic stethoscopes from pediatric patients, and annotate them as “normal,” “wheeze,” “cough,” etc. by a panel of expert reviewers. The annotated sounds will be subjected to spectral filtering chosen to parallel the frequency response of the proposed resonant microphone array. A pattern recognition algorithm will be applied to these sound files and be used to determine the recognition accuracy of the wearable stethoscopes. Four sets of the wearable stethoscopes will be delivered at the 18th, 30th, 42nd and 54th month of the research period, and will be used to record lung sounds and test automatic classification accuracy, which will be compared to patients diagnosed with well- or poorly-controlled asthma as determined by consensus diagnostic criteria. Finally, the wearable stethoscopes will be tested in ambulatory asthmatic pediatric patients continuously over a 30 day period, for the predictive ability of the wearable stethoscope in detecting asthma attacks.