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Respiratory diseases are a leading cause of death worldwide. The prevalence of sleep apnea, its cardiovascular consequences, postoperative respiratory instability and severe respiratory syndromes further highlight the importance of respiratory monitoring. Typical methods, however, rely on obtrusive nasal cannulas and belts. Impedance pneumography (IP) is a promising bioimpedance application which aims to estimate respiratory parameters from the thorax impedance. Currently, IP configurations require large inter-electrode distances, diminishing its applicability in a wearable context. We propose an IP configuration with 55 mm spacing using our recently presented sensor patch. In a study including 10 healthy subjects, respiratory rate (RR) and flow are estimated in the supine, lateral and prone position. Using time-delay neural network regression, RR errors below 1 bpm, flow correlations of 0.81 and relative flow errors of 38 % with respect to a pneumotachometer reference were achieved. We conclude that high accuracy RR estimation is possible in a 55 mm IP configuration. Respiratory flow can be roughly estimated. Further research combining several biosignals for a more robust, wearable flow estimation is recommended.

In recent years, respiratory monitoring has gained attention due to the high prevalence and severe consequences of sleep apnea, post-anesthesia respiratory instability and respiratory diseases. Nevertheless, respiratory monitoring oftentimes relies on obtrusive masks and belts, which are unsuitable for wearable, long-term monitoring. Impedance pneumography (IP) is a bioimpedance method aiming to assess respiratory parameters unobtrusively. However, most IP configurations require far-spaced electrodes. Based on our recent work on wearable IP, we propose a dual-lead, wearable IP setup with 55 mm electrode spacing to estimate respiratory flow and rate (RR). Using our recently presented multimodal patch stethoscope as well as commercial systems, we conducted a study including 10 healthy subjects which were recorded in the supine, lateral and prone position. Using time-delay neural networks, we achieved RR estimation errors below 0.6 breaths per minute and flow correlations of 0.88 with relative errors of 25 % to a pneumotachometer reference. We conclude that dual-lead IP increases the performance of respiratory signal estimation compared to a single lead and recommend research in the area of subject position dependency and movement artefacts.