Non-invasive detection of respiratory muscles activity during assisted ventilation

Mixed-integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator-induced lung injury. Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are often invasive or complex. We herein propose a robust and flexible estimation of respiratory effort better than current methods. We developed a method for non-invasively estimating breathing effort using only flow and pressure signals. Mixed-integer quadratic programming (MIQP) was employed, and the binary variables were the switching moments of the respiratory effort waveform. Mathematical constraints, based on ventilation physiology, were set for some variables to restrict feasible solutions. Simulated and patient data were used to verify our method, and the results were compared to an established estimation methodology. Our algorithm successfully estimated the respiratory effort, resistance, and elastance of the respiratory system, resulting in more robust performance and faster solver times than a previously proposed algorithm that used quadratic programming (QP) techniques. In a numerical simulation benchmark, the worst-case errors for resistance and elastance were 25% and 23% for QP versus <0.1% and <0.1% for MIQP, whose solver times were 4.7 s and 0.5 s, respectively. This approach can estimate several breathing effort profiles and identify the respiratory system's mechanical properties in invasively ventilated critically ill patients.

Real-time Computation of a Patient's Respiratory Effort During Ventilation

OBJECTIVE

In this paper, a new algorithm is proposed to compute the spontaneously generated respiratory effort during ventilation.

METHODS

The algorithm computes a ventilated patient's respiratory effort in real-time by analyzing the respiratory pressure and flow signals that are acquired from the ventilator. The method requires an initial period where the patient's respiratory muscles are fully relaxed, for example during or shortly after surgery. During this period the patient's inspiratory airway resistance R(in), the expiratory airway resistance R(ex), the lung-thorax compliance C(lt) and the residual pressure after an infinitely long expiration P(0) are estimated by fitting the measured flow onto the measured pressure at the mouth using a model of the patient's respiratory system. When the patient starts breathing, the relation between the measured pressure and the flow changes, from which the respiratory effort of the patient P(mus) can be computed.

RESULTS

The pressure P(mus) can be computed in real-time by using an equivalent model of the respiratory system of the patient. The estimation can be done with a recursive least squares (RLS) method. Further, the resulting P(mus) signal appears to have a constant shape, in which the main changing factor is the maximum amplitude per breath.

CONCLUSION

The respiratory effort increases over time until the patient is disconnected from the ventilator. We hope the maximum amplitude can be used as an indicator of the pressure the muscles of the patient are able to produce. This amplitude of the (mus)-signal in combination with the standard deviation (SD) may eventually lead to a new indicator to determine the moment that the patient can be weaned from the ventilator. This will have to be examined in the future.