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Humanitarian engineers responded to the pandemic ventilator shortage of March, 2020 by beginning over 100 open source ventilator projects [Robert L. Read et al. COVID-19 Vent List. Oct. 2020. url: https://docs.google.com/spreadsheets/d/1inYw5H4RiL0AC_J9vPWzJxXCdlkMLPBRdPgEVKF8DZw/edit#gid=0 , Joshua M. Pearce. A review of open source ventilators for COVID-19 and future pandemics. In: F1000Research 9 (2020).]. By ventilator, we mean both an invasive ventilator (requiring intubation of the patient) and non-invasive ventilator (generally supporting spontaneously breathing). Inexpensive ventilator test equipment can facilitate projects forced to be geographically distributed by lockdowns. The VentMon is a modular, open source, IoT-enabled tester that plugs into a standard 22 mm airway between a ventilator and a physical test lung to test any ventilator. The VentMon measures flow, pressure, fractional oxygen, humidity, and temperature. Data is stored and graphed at a data lake accessible to all devlopment team members, and, eventually, clinicians. The open source design of the VentMon, its firmware, and cloud-based software may allow it to be used as a component of modular ventilators to provide a clinical readout. The software system surrounding VentMon has been designed to be as modular and composable as possible. By combining new, openly published standards for data with composable and modifiable hardware, the VentMon forms the beginning of an open system or eco-system of ventilation devices and data. Thanks to grants, 20 VentMons have been given away free of charge to pandemic response teams building open source ventilators.

Extracting clinical information from one single measurement represents a step forward in the assessment of the respiratory muscle function. This attracting idea entails the reduction of the instrumentation and fosters to develop new medical integrated technologies. We present the use of the fixed sample entropy (fSampEn) as a more direct method to non-invasively derive the breathing activity from the diaphragm electromyographic (EMGdi) signal, and thus to extract the respiratory rate, an important vital sign which is cumbersome and time-consuming to be measured by clinicians. fSampEn is a method to evaluate the EMGdi activity that is less sensitive to the cardiac activity (ECG) and its application has proven to be useful to evaluate the load of the respiratory muscles. The behavior of the proposed method was tested in signals from two subjects that performed an inspiratory load protocol, which consists of increments in the inspiratory mouth pressure (Pmouth). Two respiratory signals were derived and compared to the Pmouth signal: the ECG-derived respiration (EDR) signal from the lead-I configuration, and the EMG-derived respiration (EMGDR) signal by applying the fSampEn method over the EMGdi signal. The similitude and the lag between signals were calculated through the cross-correlation between each derived respiratory signal and the Pmouth. The EMGDR signal showed higher correlation and lower lag values (= 0.91 and = 0.70 s, respectively) than the EDR signal (= 0.83 and = 0.99 s, respectively). Additionally, the respiratory rate was estimated with the Pmouth, EDR and EMGDR signals showing very similar values. The results from this preliminary work suggest that the fSampEn method can be used to derive the respiration waveform from the respiratory muscle electrical activity.