Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals

Abstract

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RR(MMG)) was compared with that measured from inspiratory pressure signal (RR(P)). Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RR(MMG) and RR(P) measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

Respiratory signal derived from the smartphone built-in accelerometer during a Respiratory Load Protocol

“…They applied and algorithm based on smoothing and detrending the recorded signals and then estimating the RR by finding the highest peak of the power spectrum. In the present work, the chosen anatomical region to place the smartphone was the right chest, based on a previous study that used an uniaxial accelerometer [10]. Among the three acceleration components of the smartphone we selected the acc z signal as it gives the most representative information of the chest excursion.…”

“…EMD decomposes a signal into a sum of zero-mean oscillating components referred to as intrinsic mode functions (IMFs). These IMFs are obtained in decreasing order of frequency, with the first IMFs containing high frequency components (related to the muscle vibratory activity) and the last IMFs containing the low frequency components (more associated to the breathing activity) [10]. However, a major drawback of the EMD is the mode-mixing phenomenon, that is, a single IMF either including signals of dramatically disparate scales, or a signal of the same scale appearing at different IMFs, and thus, with the consequence of losing its physical interpretation [11].…”

Smart Vest for Respiratory Rate Monitoring of COPD Patients Based on Non-Contact Capacitive Sensing

“…For continuous respiratory monitoring, numerous sensorization technologies have been proposed: The processing of information captured by an accelerometer can be used to derive the respiratory rate from the movements of the rib cage [ 19 ]. Spire [ 20 ] and MonBaby [ 21 ], the latter designed for babies, are examples of commercial devices based on accelerometers.…”