Evaluation of a Wearable Device to Determine Cardiorespiratory Parameters From Surface Diaphragm Electromyography

Abstract

Using wearable devices in clinical routines could reduce healthcare costs and improve the quality of assessment in patients with chronic respiratory diseases. The purpose of this study is to evaluate the capability of a Shimmer3 wearable device device to extract reliable cardiorespiratory parameters from surface diaphragm electromyography (EMGdi). Twenty healthy volunteers underwent an incremental load respiratory test whilst EMGdi was recorded with a Shimmer3 wearable device (EMGdiW). Simultaneously, a second EMGdi (EMGdiL), the inspiratory mouth pressure (Pmouth) and the lead-I electrocardiogram (ECG) were recorded via a standard wired laboratory acquisition system. Different cardiorespiratory parameters have been extracted from both EMGdiW and EMGdiL signals.: heart rate, respiratory rate, respiratory muscle activity and mean frequency of EMGdi signals. Alongside these, similar parameters were also extracted from reference signals (Pmouth and ECG). High correlations were found between the data extracted from the EMGdiW and the reference signal data: heart rate (R = 0.947), respiratory rate (R = 0.940), respiratory muscle activity (R = 0.877), and mean frequency (R = 0.895). Moreover, similar increments in EMGdiW and EMGdiL activity were observed when Pmouth was raised, enabling the study of respiratory muscle activation. In summary, the Shimmer3 device is a promising and cost-effective solution for ambulatory monitoring of respiratory muscle function in chronic respiratory diseases.

Noninvasive Assessment of Neuromechanical Coupling and Mechanical Efficiency of Parasternal Intercostal Muscle during Inspiratory Threshold Loading

“…The proposed noninvasive indices derived from P mo , sMMG para , and sEMG para may prove to be useful indices of NMC and MEff of parasternal intercostal muscles, particularly for the assessment of respiratory muscle function using wearable devices. Advances in sensor technologies have led to an increasing trend and interest in the use of wearable and wireless physiological monitoring devices in medicine [ 48 , 49 , 50 ]. These devices may contribute to improving the assessment of patients with chronic respiratory diseases by allowing home monitoring of respiratory muscle function in a wireless and noninvasive manner.…”

Performance Evaluation of Fixed Sample Entropy in Myographic Signals for Inspiratory Muscle Activity Estimation

“…Evaluating the amplitude of myographic signals recorded from different respiratory muscles using conventional amplitude estimators is difficult due to the cardiac muscle interference. In order to reduce crosstalk from the heart in myographic respiratory signals, the use of fSampEn was adopted in our previous studies [6,9,15,17,19]. This technique is based on SampEn, which has previously contributed to improving the understanding of the underlying mechanisms of physiological processes in a wide number of clinical applications [16,[32][33][34].…”

“…In myographic respiratory signals, fSampEn has proven to be less sensitive in quantifying amplitude variations of more deterministic signal components, such as ECG and MCG, than in quantifying amplitude variations of more complex signal components, such as inspiratory muscle EMG and MMG [9,15]. Due to this advantageous property of fSampEn, this technique has been used in several applications related to respiratory muscles in healthy subjects, such as estimation of neural respiratory drive [9] and respiratory muscle activity [6,15,17] from inspiratory muscle EMG and MMG signals acquired during incremental loaded breathing, or estimation of neural inspiratory time onset and offset from inspiratory muscle EMG signals [18]. fSampEn has also been proposed to estimate inspiratory muscle mechanical activation efficiency from inspiratory muscle MMG signals acquired in COPD patients [19].…”

“…fSampEn has also been proposed to estimate inspiratory muscle mechanical activation efficiency from inspiratory muscle MMG signals acquired in COPD patients [19]. Furthermore, fSampEn has been used for the analysis of non-respiratory muscle activity [20][21][22][23][24].Despite the potential for using fSampEn to analyse respiratory muscle EMG and MMG signals, there is no consensus standard for optimum fSampEn parameters, and several values have been suggested for window length, m and r. Previous studies [6,9,15,[17][18][19]25] on respiratory muscles set m either at 1 or 2, r ranging from 0.1 to 0.5 and window length ranging from 0.25 to 1 s. A recent study evaluated the influence of window length, m, r and the sampling frequency on the estimation of respiratory activity from sEMG lic signals using fSampEn [26], demonstrating that window length and r are the most critical parameters determining the shape and magnitude of fSampEn time-series.…”

Combining Bioimpedance and Myographic Signals for the Assessment of COPD During Loaded Breathing

“…We acquired bioimpedance and myographic signals using a wearable research device and a wired standard acquisition system, respectively. Nevertheless, nowadays the use of wearable devices to acquire physiological signals is increasing and these devices have already been used in research studies including bioimpedance or myographic signals [44]- [48]. The application of the methods described in the present study requires a multimodal approach to lighten the intrusiveness.…”