1. Evaluating respiratory muscle activity using a wireless sensor platform
- Author
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Luis Estrada, Leonardo Sarlabous, Raimon Jane, Abel Torres, Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, and Universitat Politècnica de Catalunya. BIOSPIN - Biomedical Signal Processing and Interpretation
- Subjects
Male ,electromyography ,wireless sensor platform ,Electromyography ,01 natural sciences ,Signal ,medical signal detection ,0302 clinical medicine ,noninvasive electrical respiratory muscle activity recording ,Heart Rate ,Pearson correlation coefficient ,breathing movement evaluation ,Respiratory system ,medical signal processing ,medicine.diagnostic_test ,body sensor networks ,Enginyeria biomèdica [Àrees temàtiques de la UPC] ,healthcare ,Signal Processing, Computer-Assisted ,health care ,Healthy Volunteers ,Respiratory Muscles ,diaphragm ,Breathing ,patient health status monitoring ,Enginyeria biomèdica ,EMGdi signal acquisition ,Wireless Technology ,Biomedical engineering ,cardio-respiratory parameter estimation ,Respiratory rate ,Diaphragm ,03 medical and health sciences ,Respiratory Rate ,physiological parameters ,statistical analysis – telemedicine ,Heart rate ,Pressure ,medicine ,Respiratory muscle ,pneumodynamics ,Humans ,neural respiratory ,business.industry ,010401 analytical chemistry ,inspiratory mouth pressure recording ,0104 chemical sciences ,030228 respiratory system ,Control of respiration ,cardiovascular system ,business - Abstract
Wireless sensors are an emerging technology that allows to assist physicians in the monitoring of patients health status. This approach can be used for the non-invasive recording of the electrical respiratory muscle activity of the diaphragm (EMGdi). In this work, we acquired the EMGdi signal of a healthy subject performing an inspiratory load test. To this end, the EMGdi activity was captured from a single channel of electromyography using a wireless platform which was compared with the EMGdi and the inspiratory mouth pressure (Pmouth) recorded with a conventional lab equipment. From the EMGdi signal we were able to evaluate the neural respiratory drive, a biomarker used for assessing the respiratory muscle function. In addition, we evaluated the breathing movement and the cardiac activity, estimating two cardio-respiratory parameters: the respiratory rate and the heart rate. The correlation between the two EMGdi signals and the Pmouth improved with increasing the respiratory load (Pearson's correlation coefficient ranges from 0.33 to 0.85). The neural respiratory drive estimated from both EMGdi signals showed a positive trend with an increase of the inspiratory load and being higher in the conventional EMGdi recording. The respiratory rate comparison between measurements revealed similar values of around 16 breaths per minute. The heart rate comparison showed a root mean error of less than 0.2 beats per minute which increased when incrementing the inspiratory load. In summary, this preliminary work explores the use of wireless devices to record the muscle respiratory activity to derive several physiological parameters. Its use can be an alternative to conventional measuring systems with the advantage of being portable, lightweight, flexible and operating at low energy. This technology can be attractive for medical staff and may have a positive impact in the way healthcare is being delivered.