Fixed Sample Entropy to Remove Cardiac Noise for Improved Assessments of Diaphragm Muscle Electrical Activity
Authors: Khurram Obaid U. , Gransee Heather M. , Sieck Gary C. , Mantilla Carlos B. 
Published: April 2020

The diaphragm (DIA) muscle, which separates the abdominal and thoracic cavities, is the primary inspiratory muscle in mammals. Both transdiaphragmatic pressure (Pdi) and DIA electromyography (EMG) are used to assess respiratory activity across motor behaviors of varying levels of force. However, due to the proximity of the heart to the DIA, cardiac electrical activity can severely impact the quality of the EMG signal, especially in real‐time settings when cardiac activity cannot be removed. Classically, either boxcar functions of root mean square (RMS) type calculations or low‐pass filters are used to quantify DIA EMG activity; however, these techniques have the drawback of being highly influenced by cardiac noise, which occurs approximately four times more frequently than DIA EMG activity during quiet breathing (eupnea). In humans with surface DIA EMG, fixed sample entropy (fSE)—a tool for assessing the complexity of a time series—has been proposed as a method to quantify the intensity of DIA EMG activity across varying levels of muscle pressure generation. The parameters of fSE can be adjusted to be resistant to relatively slow changes in a signal resulting from cardiac activity while being highly susceptible to the relatively quick changes in motor unit action potentials in the time domain. Unsurprisingly, cardiac noise is a problem in non‐human animal studies as well, particularly in disease models when the DIA EMG activity levels are reduced. In the present study, we assessed the effectiveness of fSE in accurately reflecting changes in EMG activity in healthy rats with in‐dwelling DIA EMG electrodes. We hypothesized that fSE EMG would be resistant to changes in the signal caused by cardiac noise while reflecting changes caused by DIA EMG. We evaluated 24 different parameter settings for fSE assessments to optimize the correlation coefficient between the fSE and Pdi signals. In addition, we analyzed the RMS, the average rectified value (ARV), and the fSE of DIA EMG events during eupnea, exposure to an hypercapnic (5% CO2)‐hypoxic (10% O2) environment, and during airway occlusion by performing detailed comparisons of individual respiratory events. In total, approximately 900 individual respiratory events per analysis technique were assessed across all subjects. Our results show that fSE EMG is extremely effective at eliminating cardiac noise and has a high correlation coefficient with the Pdi signal (.92). In contrast, the correlation coefficient between RMS EMG and Pdi is 0.83 and the correlation coefficient between ARV EMG and Pdi is 0.88, likely due to the high level of cardiac noise in the DIA EMG. Additionally, we show that ventilatory parameters such as instantaneous burst duration and respiratory frequency are similar between Pdi, RMS, ARV, and fSE EMG. These findings suggest that fSE is a reliable and effective alternative to using traditional RMS and ARV EMG techniques to estimate the magnitude of respiratory muscle electrical activity in muscles that are susceptible to cardiac noise.