Castillo Yolanda, Blanco-Almazan Dolores, Whitney James, Mersky Barry, Jane Raimon
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:1543-1546. doi: 10.1109/EMBC.2017.8037130.
Obstructive sleep apnea (OSA) is a highly prevalent chronic disease, especially in elderly and obese populations. Despite constituting a serious health, social and economic problem, most patients remain undiagnosed and untreated due to limitations in current equipment. In this work, we propose a novel method to diagnose OSA and monitor therapy adherence and effectiveness at home in a non-invasive and inexpensive way: combining acoustic analysis of breathing and snoring sounds with oral appliance therapy (OA). Audiodontics has introduced a new sensor, a tooth microphone coupled to an OA device, which is the main pillar of this system. The objective of this work is to characterize the response of this sensor, comparing it with a commercial tracheal microphone (Biopac transducer). Signals containing OSA-related sounds were acquired simultaneously with the two microphones for that purpose. They were processed and analyzed in time, frequency and time-frequency domains, in a custom MATLAB interface. We carried out a single-event approach focused on breaths, snores and apnea episodes. We found that the quality of the signals obtained by both microphones was quite similar, although the tooth microphone spectrum concentrated more energy at the high-frequency band. This opens a new field of study about high-frequency components of snores and breathing sounds. These characteristics, together with its intraoral position, wireless option and combination with customizable OAs, give the tooth microphone a great potential to reduce the impact of sleep disorders, by enabling prompt detection and continuous monitoring of patients at home.
阻塞性睡眠呼吸暂停(OSA)是一种高度流行的慢性疾病,尤其在老年人和肥胖人群中。尽管它构成了严重的健康、社会和经济问题,但由于现有设备的局限性,大多数患者仍未得到诊断和治疗。在这项工作中,我们提出了一种新颖的方法,以非侵入性且低成本的方式在家中诊断OSA并监测治疗依从性和有效性:将呼吸和打鼾声音的声学分析与口腔矫治器治疗(OA)相结合。听觉正畸学引入了一种新的传感器,即与OA设备耦合的牙齿麦克风,这是该系统的主要支柱。这项工作的目的是表征该传感器的响应,并将其与商用气管麦克风(Biopac换能器)进行比较。为此,使用这两个麦克风同时采集包含与OSA相关声音的信号。在自定义的MATLAB界面中,对这些信号进行时域、频域和时频域的处理与分析。我们采用了一种专注于呼吸、打鼾和呼吸暂停事件的单事件方法。我们发现,尽管牙齿麦克风的频谱在高频带集中了更多能量,但两个麦克风获得的信号质量相当相似。这开辟了一个关于打鼾和呼吸声音高频成分的新研究领域。这些特性,连同其口腔内位置、无线选项以及与可定制OA的结合,赋予了牙齿麦克风巨大的潜力,通过实现对患者在家中的及时检测和持续监测,来减少睡眠障碍的影响。