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

摘要 这项研究旨在开发和测试一种可穿戴的、始终在线的听诊器，以提供一种解决 对量化呼吸道症状的需求尚未得到满足。这样的设备对于准确的诊断是必要的。 680万婴幼儿和其他人群的哮喘患病情况和哮喘控制评估 智障人士不能报告其呼吸系统症状或进行肺功能测试 美国。有了准确的诊断和控制评估，就可以开始适当的哮喘治疗。 没有延误，将哮喘的不良后果降至最低。推荐的可穿戴设备需要哪些关键要素 听诊器(即，没有笨重声学耦合器的无线听诊器)是(1)谐振麦克风阵列 具有前所未有的100-800赫兹以上的声音检测能力和(2)超低功率信号处理能力。我们建议 开发一组基于高Q(品质因数)谐振的声学过滤麦克风 麦克风隔膜，用于准确检测异常的肺音。一组Q-滤波和Q-增强 提出了麦克风，以便(1)可以避免特征提取滤波器和(2)极小的肺 在没有声学耦合器的情况下，可以从胸部检测到声音。 如果没有笨重的声学耦合器，肺部的声音很难从胸部探测到，因为声音 在100-800赫兹的频率范围内，自由空间的压级(SPL)仅为22-30分贝SPL。这一种 通过商用微型麦克风无法准确地检测到SPL。因此，我们将开发和使用一个 8个谐振麦克风阵列，Q为40-60(采用微加工工艺制造)，用于检测肺部 声音降至22分贝SPL，比人耳可以检测到的最低声音低4分贝，并自动 将声音的频率分量分割为8个不同的窄带通频段 -800赫兹。 我们假设对肺音的持续动态监测 哮喘将提高儿童哮喘患者的诊断准确率和治疗水平。目前，哮喘是 主要通过照顾者病史和简短的办公室检查作为直接患者病史在幼儿中诊断 而呼吸功能测试不适用于这些年轻患者。提议的装置是完全新颖的。 由于目前没有能够对哮喘征兆进行全天候监测的设备。而当 传统的麦克风可以检测咳嗽和明显的喘息，但由于灵敏度不够，其用途受到限制， 而且不适用于持续的、不定期的监测。 建议的谐振麦克风阵列将与超低功率电子设备集成在一起，以实现可穿戴式 持续跟踪肺部声音的听诊器，用于检测哮喘的迹象和症状，如咳嗽 以及在锻炼或睡眠期间(当照顾者不在场时)可能发生的喘息，并可能 被幼童误报或忽视。无线传输的异常肺音将通过 互联网。拟议中的听诊器将为那些没有能力提供 准确的病史或自我管理方面的困难，例如有语言障碍的儿童和个人 智力残疾，避免严重的哮喘发病。该设备检测到哮喘失控的迹象，并 通过互联网通知家长、照顾者和医疗专业人员。这样做，我们可能会改善 通过更准确地跟踪环境和行为触发因素来管理哮喘患者 可以用来改善管理。 为了测试可穿戴听诊器的潜在临床实用价值，我们将首先用 来自儿科患者的常规电子听诊器，并将其注释为“正常”、“喘息”、“咳嗽”、 等，由专家评审员组成的小组。经注释的声音将经过选择平行的频谱滤波 所提出的谐振式麦克风阵列的频率响应。模式识别算法将是 应用于这些声音文件，并用于确定可穿戴听诊器的识别准确性。 四套可穿戴听诊器将在研究的第18、30、42和54个月交付 期间，并将用于记录肺音和测试自动分类准确率，将进行比较 根据共识诊断标准诊断为哮喘控制良好或控制不良的患者。 最后，这种可穿戴听诊器将在一年多的时间里连续在非卧床哮喘儿童患者中进行测试。 30天为可穿戴听诊器检测哮喘发作的预测能力。

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.