Publications

Objective: Accurate detection of inspiratory onset and offset in the diaphragm electromyographic signal (EMGdi) is clinically relevant to assess patient-ventilator interaction in COPD patients undergoing non-invasive ventilation (NIV). Manual annotations are time-consuming and subject to inter-observer variability, highlighting the need for reliable automatic methods. Method: We developed a fully automatic algorithm to detect EMGdi activity cycles and their onset/offset timing in overnight NIV recordings. Four ECG suppression approaches were combined with root mean square (RMS) and fixed sample entropy (fSE) envelopes, and a novel bias correction strategy based on inspiratory-to-basal signal-to-noise ratio (I2BSNR) was introduced. Performance was compared with double-blind annotations from two independent experts. Results: In a cohort of 10 severe COPD patients (9212 annotated cycles), the best configuration (adaptive filtering with fSE exponential envelope) achieved F $1=0.96$ , with onset bias -28 ms (SD 270 ms) and offset bias + 120 ms (SD 292 ms). We show that fSE-based envelopes consistently outperform RMS in onset/offset detection, and that I2BSNR-based correction reduces systematic bias to within accepted clinical timing windows. Conclusions: The proposed method provides accurate and robust onset/offset detection of EMGdi during NIV in COPD patients. This enables reliable quantification of patient-ventilator asynchronies such as ineffective efforts and delayed cycling, offering direct clinical value for optimizing nightly ventilator settings in severe COPD. Clinical and Impact: Reliable detection of patient inspiratory activity offers a practical tool to guide real-time ventilator adjustments and reduce patient-ventilator asynchronies

JTD Keywords: Annotations, Asynchrony, Chronic obstructive pulmonary disease, Chronic obstructive pulmonary disease (copd), Electromyography, Emg, Filtering, Fixed sample entropy (fse)., Non-invasive ventilation (niv), Patient-ventilator asynchrony (pva), Recording, Reliability, Root mean square, Surface diaphragm electromyography (emgdi), Time, Timing, Ventilation, Ventilators

The use of 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 capacity of a Shimmer3 wearable 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), inspiratory mouth pressure (Pmouth) and lead-I electrocardiogram (ECG) were recorded via a standard wired laboratory acquisition system. Different cardiorespiratory parameters were 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 the ambulatory monitoring of respiratory muscle function in chronic respiratory diseases.

JTD Keywords: Cardiorespiratory monitoring, Chronic respiratory diseases, Fixed sample entropy, Non-invasive respiratory monitoring, Surface diaphragm electromyography, Wearable wireless device

Fixed sample entropy (fSampEn) is a robust technique that allows the evaluation of inspiratory effort in diaphragm electromyography (EMGdi) signals, and has potential utility in sleep studies. To appropriately estimate respiratory effort, fSampEn requires the adjustment of several parameters. The aims of the present study were to evaluate the influence of the embedding dimension m, the tolerance value r, the size of the moving window, and the sampling frequency, and to establish recommendations for estimating the respiratory activity when using the fSampEn on surface EMGdi recorded for different inspiratory efforts. Values of m equal to 1 and r ranging from 0.1 to 0.64, and m equal to 2 and r ranging from 0.13 to 0.45, were found to be suitable for evaluating respiratory activity. fSampEn was less affected by window size than classical amplitude parameters. Finally, variations in sampling frequency could influence fSampEn results. In conclusion, the findings suggest the potential utility of fSampEn for estimating muscle respiratory effort in further sleep studies.

JTD Keywords: Fixed sample entropy (fSampEn), Non-invasive respiratory monitoring, Respiratory activity, Respiratory effort, Surface diaphragm electromyography