Department of Electronics and Medical Signal Processing, Technische Universität Berlin, Einsteinufer 17, 10587 Berlin, Germany.
Department of Anesthesiology, Intensive Care Medicine and Pain Therapy, Evang. Kliniken Essen-Mitte, Huyssens-Stiftung/Knappschaft, Henricistr. 92, 45136 Essen, Germany.
Sensors (Basel). 2020 Apr 4;20(7):2033. doi: 10.3390/s20072033.
Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HD and Osypka ICON-Core. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.
心血管疾病是全球主要死因,睡眠呼吸紊乱是进一步加重的因素。呼吸道疾病是非传染性疾病中第三大致死原因。然而,当前的 COVID-19 大流行也突出了传染性呼吸道综合征的影响。在临床常规中,麻醉后呼吸不稳定时间延长会使患者预后恶化。尽管在许多情况下已经提出或甚至证明了早期和连续的长期心肺监测是有益的,但实施情况却很少。我们使用最近提出的多模态贴片听诊器,从单个 55mm 的心电图导联估计 Einthoven 心电图(ECG)导联 I 和 II。使用听诊器和 ECG 子系统,估计射前期(PEP)和左心室射血时间(LVET)。结合一种新型的心音图衍生呼吸方法,使用 ECG 衍生呼吸技术提取呼吸参数。医用级参考是 SOMNOmedics SOMNO HD 和 Osypka ICON-Core。在一项包括 10 名健康受试者的研究中,我们分析了仰卧、侧卧和俯卧位的性能。Einthoven I 和 II 的估计相关性超过 0.97。LVET 和 PEP 估计误差分别为 10%和 21%。呼吸率的平均绝对误差低于 1.2 bpm,呼吸信号的相关性为 0.66。我们得出结论,使用可穿戴的多模态采集设备可以实现 ECG、PEP、LVET 和呼吸参数的估计,并鼓励对多模态信号融合进行进一步的研究,以进行呼吸信号估计。