| Autor: |
Yilmaz G; Swiss Center for Electronics and Microtechnology (CSEM), 2002 Neuchâtel, Switzerland., Rapin M; Swiss Center for Electronics and Microtechnology (CSEM), 2002 Neuchâtel, Switzerland., Pessoa D; University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal., Rocha BM; University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal., de Sousa AM; Swiss Center for Electronics and Microtechnology (CSEM), 2002 Neuchâtel, Switzerland., Rusconi R; Swiss Center for Electronics and Microtechnology (CSEM), 2002 Neuchâtel, Switzerland., Carvalho P; University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal., Wacker J; Swiss Center for Electronics and Microtechnology (CSEM), 2002 Neuchâtel, Switzerland., Paiva RP; University of Coimbra, Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, 3030-290 Coimbra, Portugal., Chételat O; Swiss Center for Electronics and Microtechnology (CSEM), 2002 Neuchâtel, Switzerland. |
| Abstrakt: |
Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itself as a factor impeding the digital transformation of this valuable diagnostic tool, which can improve patient outcomes by continuous long-term respiratory monitoring under real-life conditions. Particularly patients suffering from respiratory diseases with progressive nature, such as chronic obstructive pulmonary diseases, are expected to benefit from long-term monitoring. Recently, the COVID-19 pandemic has also shown the lack of respiratory monitoring systems which are ready to deploy in operational conditions while requiring minimal patient education. To address particularly the latter subject, in this article, we present a sound acquisition module which can be integrated into a dedicated garment; thus, minimizing the role of the patient for positioning the stethoscope and applying the appropriate pressure. We have implemented a diaphragm-less acousto-electric transducer by stacking a silicone rubber and a piezoelectric film to capture thoracic sounds with minimum attenuation. Furthermore, we benchmarked our device with an electronic stethoscope widely used in clinical practice to quantify its performance. |
