Objective. The respiratory rate (RR) is considered one of the most informative vital signals. A well-validated standard for RR measurement in mechanically ventilated patient is capnography; a noninvasive technique for expiratory CO2measurements. Reliable RR measurements in spontaneously breathing patients remains a challenge as continuous mainstream capnography measurements are not available. This study aimed to assess the accuracy of RR measurement using electrical impedance tomography (EIT) in healthy volunteers and intensive care unit (ICU) patients on mechanical ventilation and spontaneously breathing post-extubation. Comparator methods included RR derived from both capnography and bioimpedance electrocardiogram (ECG) measurements.Approach. Twenty healthy volunteers wore an EIT belt and ECG electrodes while breathing through a capnometer within a 10-40 breaths per minute (BPM) range. Nineteen ICU patients underwent similar measurements during pressure support ventilation and spontaneously breathing after extubation from mechanical ventilation. Stable periods with regular breathing and no artefacts were selected, and agreement between measurement methods was assessed using Bland-Altman analysis for repeated measurements.Main result. Bland-Altman analysis revealed a bias less than 0.2 BPM, with tight limits of agreement (LOA) ±1.5 BPM in healthy volunteers and ventilated ICU patients when comparing EIT to capnography. Spontaneously breathing ICU patients had wider LOA (±2.5 BPM) when comparing EIT to ECG bioimpedance, but gold standard comparison was unavailable. RR measurements were stable for 91% of the time for capnography, 68% for EIT, and 64% of the ECG bioimpedance signals. After extubation, the percentage of stable periods decreased to 48% for EIT signals and to 55% for ECG bioimpedance.Significance. In periods of stable breathing, EIT demonstrated excellent RR measurement accuracy in healthy volunteers and ICU patients. However, stability of both EIT and ECG bioimpedance RR measurements declined in spontaneously breathing patients to approximately 50% of the time.

The accuracy of diaphragm electromyogram (EMGdi) derived parameters, as used in critically ill intensive care unit (ICU) patients, can be compromised due to electrocardiographic (ECG) interference in the EMGdi signal. Removal of ECG contamination from the esophageal recordings of the EMGdi is challenging due to spectral overlapping of EMG and ECG signals and because of variability in ECG shape and amplitude. Therefore, we designed an Estimated ECG Subtraction (EES) method, based on three steps: (1) identification of the timing of the ECG artifact without an ECG reference channel, (2) estimation of the normalized ECG, considering the EMGdi as noise, and (3) subtraction of the denormalized ECG estimate from the EMGdi recordings. We evaluated the EES method against the use of a single wavelet-based adaptive filter. Using EMGdi signals of ten ICU patients and simulated contaminated EMG, we demonstrated that the EES method yields uncontaminated EMGdi, and showed that it is more effective than a wavelet-based adaptive filter only. Implementation of this technique may offer means to improve diaphragm activity monitoring and control in clinical practice.