ABSTRACT
Breathing pattern parameters refer to the characteristic pattern parameters of respiratory movements, including the breathing amplitude and cycle, chest and abdomen contribution, coordination, etc. It is of great importance to analyze the breathing pattern parameters quantificationally when exploring the pathophysiological variations of breathing and providing instructions on pulmonary rehabilitation training. Our study provided detailed method to quantify breathing pattern parameters including respiratory rate, inspiratory time, expiratory time, inspiratory time proportion, tidal volume, chest respiratory contribution ratio, thoracoabdominal phase difference and peak inspiratory flow. We also brought in "respiratory signal quality index" to deal with the quality evaluation and quantification analysis of long-term thoracic-abdominal respiratory movement signal recorded, and proposed the way of analyzing the variance of breathing pattern parameters. On this basis, we collected chest and abdomen respiratory movement signals in 23 chronic obstructive pulmonary disease (COPD) patients and 22 normal pulmonary function subjects under spontaneous state in a 15 minute-interval using portable cardio-pulmonary monitoring system. We then quantified subjects' breathing pattern parameters and variability. The results showed great difference between the COPD patients and the controls in terms of respiratory rate, inspiratory time, expiratory time, thoracoabdominal phase difference and peak inspiratory flow. COPD patients also showed greater variance of breathing pattern parameters than the controls, and unsynchronized thoracic-abdominal movements were even observed among several patients. Therefore, the quantification and analyzing method of breathing pattern parameters based on the portable cardiopulmonary parameters monitoring system might assist the diagnosis and assessment of respiratory system diseases and hopefully provide new parameters and indexes for monitoring the physical status of patients with cardiopulmonary disease.
Subject(s)
Humans , Lung , Pulmonary Disease, Chronic Obstructive , Respiration , Tidal Volume , Wearable Electronic DevicesABSTRACT
Resumen Se desarrolló un sistema vestible para la detección simultánea y no invasiva de dos señales provenientes de la actividad cardiaca: el ECG y el flujo sanguíneo. Se utilizaron dos electrodos secos para detectar la derivación I del ECG. La detección del flujo sanguíneo se realizó de forma no invasiva mediante un sensor magnetorresistivo tipo Túnel (TMR) basándose en la firma magnética de la sangre sobre un campo magnético constante proveniente de un imán permanente. Los electrodos, el sensor TMR y el imán se montaron en un brazalete tipo pulsera para la comodidad del usuario. La instrumentación desarrollada para detectar el ECG y el flujo sanguíneo se diseñó en base a una configuración diferencial con una relación de rechazo en modo común superior a 87 dB a 60 Hz y con una tensión de ruido de tal forma que las señales cardiacas presentaron una relación señal a ruido superior a 41 dB, siempre y cuando el sujeto no se mueva durante la medida. La calidad de las señales fue suficiente como para extraer información de dichas señales mediante algoritmos sencillos de implementar. El sistema propuesto es una alternativa a los sistemas vestibles presentados hasta ahora, del cual es posible valorar la salud cardiovascular del sujeto en ambientes no hospitalarios.
Abstract A wearable system was developed for the simultaneous and non-invasive detection of two signals from cardiac activity: the ECG and blood flow. Two dry electrodes were used to detect the lead I of the ECG. The detection of blood flow was performed non-invasively using a tunnel-magnetoresistance (TMR) sensor based on the magnetic signature of the blood over a constant magnetic field from a permanent magnet. The electrodes, the TMR sensor and the magnet were mounted on a wristband-type bracelet for the user comfort. The instrumentation developed to detect the ECG and blood flow was designed based on a differential configuration with a common-mode rejection ratio higher than 87 dB at 60 Hz and with a noise voltage in such a way that the cardiac signals presented a signal-to-noise ratio greater than 41 dB, as long as the subject stays still during the measurement. The quality of the signals was enough to extract information from these signals by means of simple to implement algorithms. The proposed system is an alternative to the wearable systems presented so far, from which it is possible to assess the cardiovascular health of the subject in non-hospital environments.