Electromyography-Based Respiratory Onset Detection in COPD Patients on Non-Invasive Mechanical Ventilation

Figures

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  Figure 1. (a) EMGdi signal segments from a COPD patient with high quality signal recorded during non-invasive ventilation at night. Inspiratory cycles are representing by dotted line boxes. EMGdi inspiratory and expiratory segments without ECG interference (solid magenta and green traces, respectively), and ECG inspiratory and expiratory segments (solid red and cyan traces, respectively) are also shown. Solid blue traces shown not included segments. (b) PSD corresponding to ECG inspiratory and expiratory and (c) PSD corresponding to EMGdi inspiratory and expiratory phases.

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  Figure 2. Representative examples of EMGdi signal (a1,a2) recorded in two patients with low (left panels) and high (right panels) signal quality, respectively. Derived root mean square (b1,b2) envelopes given by the EMG recording system (RMSp). Derived fixed sample entropy (fSE) envelopes over the EMGdi signal (fSE-EMGdi) (c1,c2). fSE was calculated using m = 1, r = 0.2 × standard deviation of EMGdi free of electrocardiographic interference and overlapping sliding window of 0.25 s. The EMGdi signal was adaptively filtered (d1,d2) using an LMS-based adaptive algorithm (EMGdi-LMS) and fSE (e1,e2) was also calculated (fSE-EMGdi-LMS). Rinex is an expression of increment of inspiratory EMGdi activity with respect to basal expiratory EMGdi activity, while Rcardio is an expression of the ECG interference over the EMGdi signals. Expert onset detections are shown in black solid lines (black and red lines). The onsets of neural respiratory activity (blue dotted lines) were detected through a dynamic threshold over the fSE-EMGdi and fSE-EMGdi-LMS using the Gaussian kernel density estimation approach.

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  Figure 3. Root-mean-square error (RMSE) between the visual onset detection and automatic onset detections. Fixed sample entropy (fSE) was estimated over both the EMGdi signal (fSE-EMGdi) and filtered EMGdi signal using an LMS-based adaptive algorithm (fSE-EMGdi-LMS). fSE was calculated for different settings: m = 1 and 2, and tolerance values r = 0.1, 0.2 and 0.3 × standard deviation of EMGdi free of electrocardiographic interference, and overlapping sliding windows of 0.25, 0.3, 0.4 and 0.5 s. Each RMSE represents the geometric mean of obtained values for the 9 analyzed patients.

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  Figure 4. Comparison between the onset average detection of two visual scorers (tonV) and the automatic onset detection, including all studied methods, in two representative patients with low (left panels) and high (right panels) quality signal, respectively. Automatic detections were estimated from RMS-based (a1,a2) envelopes provided by the acquisition device (RMSp), fSE-based envelopes (b1,b2) over the EMGdi signal (fSE-EMGdi), and fSE-based envelopes (c1,c2) from the LMS-based adaptively filtered EMGdi signal (fSE-EMGdi-LMS). Upper red dots represent early detection and lower blue dots represent late detection of the automatic methods.