## Abstract

Mechanical ventilators are advanced life-supporting machines in this century. The ventilator needs to be safe, flexible, and easy for competent clinicians to use. Since ventilators supply the patient with gas, they need pneumatic components to be present. First technology ventilators were typically powered by pneumatic energy. Gas pressure is used to power ventilators as well as ventilate patients. Nowadays, ventilators are operated electronically with the useful microprocessor tool. This proposal aims to design a simple portable mechanical ventilator that includes measuring some important physiological variables such as respiratory rate, heart rate, and O2 saturation, which can be utilized in hospital and at home. The proposed system includes Arduino, Raspberry pi4, touch screen, and graphical user interface. This study showed a significant individual performance for measuring some important parameters such as flow rate, tidal volume, and minute ventilation. The accuracy of measuring the flow rate was 72%. The Cohen's kappa (CK) was estimated to be 0.61. The accuracy of calculated the tidal volume was estimated at 83% with 0.80 CK. The accuracy of measuring the O2 saturation was estimated at 99% with 0.99 CK. The advantages of the proposed design are cost-effective, safe, flexible, and easy to use. Also, this system is smart and can control its transactions, so it can be used at home without the need for professional help. The operating parameters can also be set by the user with a simple user interface.

## Introduction

There is no doubt that the coronavirus 2019 (COVID 19) pandemic [1,2] has detrimentally affected the health care system around the world. The outbreak of COVID-19 has been identified as an international public health emergency. Hospitals need a plan to manage their resources, personnel, and supplies to prepare for a pandemic so that patients can be provided with optimal treatment. Therefore, in-hospital transmission can be minimized by infection prevention steps. Due to this pandemic, there is a shortage of vital equipment in most countries such as mechanical ventilator and remote devices that allow the physicians, nurses, respiratory therapists, and other specialists to communicate with the patient to provide the necessary information. Also, the number of patients affected by COVID 19 in Saudi Arabia has witnessed a dramatic increase, which requires more effort from engineers as well as the health care specialties to overcome these dire pandemic times. Thus, the collaboration between biomedical engineering and all medical specialties as one body is required.

The initial usage of the ventilators dates back to an ancient era of biblical times. It was in the 1800s [3,4], where the first use of negative-pressure ventilation surfaced. In the 1900s [57], the positive-pressure devices began to see spurge as devices for pneumatic compensation in hospitals, clinics, and intensive care unit; this method did not start to be developed until the 1940s [8].

Since then, until today, there have been four eras that have witnessed tremendous growth in the field of positive mechanical ventilators manufacturing, the ones often used in intensive care unit. Each industrial generation has characteristics that vary from the previous and their next ones. The rapid industrial development which accompanied these generations laid the foundation stone for the smart currently used generation.

The first generation of intensive care ventilators appeared in the fifties of the last century. It only controlled the air volume [9]. The ventilation device was a simple electronic circuit with a compressor, the respiratory rate, and tidal volumes are determined mandatorily (inspiration/exhalation ratio is 1: 2.12).

The second generation of intensive care ventilators included additional alarms such as high pressure, high respiratory rate, or low tidal volume. The method of mandatory intermittent ventilation was also introduced in adult ventilation and the method of controlling the air and oxygen pressure by using a Servocame into clinical practice [10]. In the late 1970s [11], the concept of closed-loop ventilation appeared. Although their method of mandatory fine ventilation was purely mechanical, it functioned as a closed-loop control unit and modeled many of the day's situations. An inflator was also used, from which the patient could breathe automatically; if the airways were completely inflated, the gas would be redirected to the second valve (valve pop), where the capacity of the airway will be normalized, and the patient's automatic minute volume, were adjusted.

The third generation of intensive care ventilators utilized the concept of the microprocessor for controlling the mechanical ventilation that appeared in the third generation, such as Servo 300 (Siemens, Germany) ventilators. The microprocessor approach allows control and monitoring of any gas delivery through the device, thus reducing the effort and time for patients to activate gas delivery. Almost all ventilators of this era included pressure support, pressure control, volume control, and synchronized intermittent mandatory ventilation. All ventilator of this generation also included smart alarms. The devices of this generation allowed monitoring of the patient's condition and every parameter volume of the ventilator tool [12,13].

The fourth generation of intensive care ventilators used the smartest tool of the mechanical ventilation system generation [14]. In general, most of these new modes include the pressure-controlling approach. As for choosing the adaptive ventilation support pattern, which tries to create a ventilation pattern based on the respiratory action model from (Otis), the clinician must enter the patient's ideal body weight, the required volume size, and the maximum airway pressure, also the respiratory rate and tidal volume, which results in the least breathing effort. After entering all these parameters, the mechanical ventilation device adjusts the control of the device to provide a therapeutic intervention value that suits the patient [1517].

There is another method of closed-loop control called SmartCare, which is used to support the pressure. After every few minutes, the device automatically adjusts the pressure support level to control the respiratory rate, tidal volume, and end-tidal PCO2 (PECO2) [18]. The smart care is based on programming algorithms for chronic obstructive pulmonary disease patients. When the pressure support is less than the required level, the ventilator automatically performs an experimental breathing maneuver. If the patient fails the experiment, the ventilator automatically continues ventilation. Many of the ventilators in the fourth generation include noninvasive ventilation modes, and a smart management software to evaluate the system and patient parameters. The clinician could program the performance of a pressure–volume loop. Other software allows the operator to reprogram and control the positive end expiratory pressure valve [19].

As aforementioned, the COVID-19 pandemic caused a failure of the health care system in many countries around the world. The collaboration between biomedical engineering and all medical specialties as one body is required. This study main aim is to design a simple hybrid mechanical ventilator system to be used as a mobile system in hospitals. Also, to avoid the problem of electronic complexity, the system is designed based on a touch screen with a user interface and monitoring system.

## Materials and Methods

The design of the mechanical ventilator prototype is based on Arduino, Raspberry Pi 4, and a graphical user interface. The QuickLung® (Pittsburgh, PA) is used for evaluating lung conditions and ventilator testing and training. The advantage of QuickLung is easy to use and suitable for simulation purposes, such as evaluating the patient conditions and the effort of inspiration for the patient. Also, 40 subjects of volunteers (20 males and 20 females) participated in testing and training plus heart rate and pulse oximeter. The average age is between 20 and 40.

Figure 1 show the schematic diagram of the mechanical ventilator prototype. It is included 2-Arduino, oxygen sensor, Raspberry Pi 4, 2-pneumatic valve, plus rate, oxygen saturation, 2-LCD, and power supply (Fig. 1).

Fig. 1
Fig. 1
Close modal

In general, the block diagram describes the idea of how the system work, for example, how does the electromechanical components work (tow solenoid valves)? At first, the user touches the screen to setup the respiratory rate (RR) via the graphical user interface; then the information from the touch screen is sent to the Raspberry via a USB cable, and the Raspberry main program calculates the operating time of opening/closing the solenoid valves; the Raspberry sends this information to the main Arduino, then the Arduino sends +5 volts to the relay, which control 12-volt circuit, which supplies the two solenoid valves. The relay controls one electrical circuit by opening and closing contacts in another circuit (12v supply). When a relay contact is normally closed, there is a closed contact when the relay is not energized; when the relay is activated, the 12-volt circuit will turn on the two valves; at the same time, this causes the inspiration valve to open because it is normally closed type, while the expiration valve will close because it is normally opened type. Therefore, when the relay is activated, the inspiration air will flow to the patient mask while the expiration air channel will be closed, and this causes the positive pressure to rise in the area under the mask. In the reverse status (when the relay is off), the inspiration air will be closed while the expiration will be open (see Fig. 1).

Regarding the Arduino microcontroller, it receives different data types, such as sensors data and the data come from Raspberry; the data from sensors are considered as analog signals ranging between 0 and 5 volts, while the data coming from Raspberry are command data or integer type data. Arduino itself provides a convenient way to read the analog signals using analogRead(command), the analogRead(function) takes 100 milliseconds leading to a theoretical sampling rate of 9600 Hz (9600 samples each second).

The Raspberry Pi 4 (Fig. 2) is connected to the two Arduino modules via a USB cable. Raspberry receives/sends the information to the main Arduino board through a USB cable, and the main Arduino receives the information from the secondary Arduino through serial clock-serial data pins.

Fig. 2
Fig. 2
Close modal

### The Method

#### User Interface.

Each breath is controlled by inspiratory time (Ti)/expiratory time (Te) or RR. The RR is controlled via the graphical user interface (touch screen), the screen displayed two setting parameters (RR) and (Ti), the other parameters are calculated values, such as the respiratory cycle ratio (I:E); the volume tidal (VT); the Te; the total cycle time (TCT); the minute ventilation (MV). Some values are measured; these values are needed in the calculation such as pulse rate, O2 saturation, and O2 concentration. The data transmission between the touch screen and the Raspberry Pi 4 is via a USB cable while the video and data transmission is via an high-definition multimedia interface cable. The transfer data from the screen to the Raspberry include a user controlling issues such as controlling the Ti and the RR, the received data from the Raspberry to the screen including monitoring data such as displaying the Te, TCT, the I:E, and the MV.

#### Microcontroller.

Microcontroller also includes two Arduino platforms, which are used for data collection from the different sensors such as O2, heart rate (MAX30100), flowmeter (type MJ HZ41W liquid meter), and oxygen saturation (MAX30100); some sensor needed for more processing phase, such as O2 that need to amplify the voltage signal level because the microcontrollers cannot read the millivolt that the sensor puts out, the (ADS1115), which is an analog to digital converter chip “ADC.” Also, the microcontroller provides the task of controlling the relays of the valves corresponding to the inspiration/expiration cycle time. The first Arduino connects with the second Arduino via transmitted signal and received signal or data communication ports. The main Arduino connects to the Raspberry via a serial USB cable (Fig. 4).

#### Materials.

The control panel includes many electronic components such as a 7-in. touch screen LCD; the waveshare 7 in. high-definition multimedia interface LCD touch screen is used as an interactive user interface; the graphical user interface is designed based on the Java application (studioide). Two pushbuttons: to reset the Arduino 1 and 2; Power ON/OFF, respiratory pause switch, 16*2 LCD with contrast potentiometer, Ti/Te dialog box, Air and oxygen pressure regulator with digital pressure gage (Lematec Digital Air Regulator), keyboard for user data entering, digital pen for the user entering data (Fig. 3).

Fig. 3
Fig. 3
Close modal

The electromechanical hardware (Fig. 4) includes the following components: compressor (1,compressor), (2, air container); (3, air filter); (4, air-reducing valve); (5, manometer); (7, 3/2 close valve); (8, humidifier); (mask); (10, oxygen cylinder); (11, oxygen-reducing valve); (12, 3/2 open valve); (13, positive end expiratory pressure valve); (14, Raspberry Pi 4); (15, Arduino module); (16,Touch screen); Two solenoid valves.

Fig. 4
Fig. 4
Close modal

### The Calculations.

This section describes the mathematical module of the proposed ventilator:

• The respiratory rate RR% data are used in Eq. (1), to find TCT
$TCT=60RR%$
(1)
• The setting information about the Ti and the TCT is used in Eq. (2), to find the Te

$Te =TCT−Ti%$
(2)
• The inspiration/expiratory time data are used in Eqs. (3) and (4), to find the (Rio1) and (Rio2)

$Rio1=TiTi$
(3)
$Rio1=TiTe$
(4)
• The flow (f) and the (Ti) data are sent to Eq. (5), to calculate the VT

$flow rate=volume tidalTi % L/min$
(5)
• The VT and RR data are used in Eq. (6), to calculate the MV
$minute ventilation=RR*volume tidal$
(6)

## The Software

### The Raspberry Pi Application.

The Raspberry Pi application: This program is used to control the input/output information. The ventilator_ raspberry_ code is used as the main code of the device; it controls the main Arduino code that controls the relay which corresponds to controlling the inspiration/expiration pneumatic valves.

In general, Raspberry specifically controls the following functions:

• All sensor calculation is implemented on two Arduino modules. The Raspberry receives all sensors data (flow, blood oxygen, heart rate) from the main Arduino through a USB cable. The Raspberry also receives the user instructions via a touch screen module, and the Raspberry is connecting to the touch screen through a USB cable. After receiving all the data, Raspberry displays these data on the touch screen.

• The user interaction with the touch screen data (read and set): the user takes a decision about the control parameters setting, for example, the user could change the RR, the new specific value will be set at the touch screen, the information then is sent to Raspberry through the USB cable, the Raspberry program then make the required calculation and sends the new information to the main Arduino to control the inspiration/expiration pneumatic valves.

The Arduino_2 program: Is used to determine the O2 saturation, and then sent the measurement values to the main Arduino proram_1. The Arduino 1—used to determine the O2 percentage, the pulse rate, and the valve timing driver control.

The user interface GUI code: Is a graphical user interface, the touch screen acts as an interactive environment between the user and the device; it is used to operate all ventilator settings.

## Results

The experiments have been carried to ensure the performance of the proposed mechanical ventilation efficiency with regard to the compliance of physical hardware with programming code. The performance detection is based on the interaction of the electromechanical hardware (solenoid valves) with the user's instructions. For example, if the inspiratory cycle time is set to 1.5 s, the inspiratory pneumatic valve must open in real-time and follow the instruction time. To ensure that, an external timer was used while the experiment is running. The numerical values that appeared in result tables are data measured based on questions in the calculation section (page 5), for example, the TCT pointed to the total cycle time that was measured in seconds; during the experiment, these times will be measured and compared to the instruction time. The standard measurements protocol of evaluating the mechanical ventilation: MV was obtained from the respirometer monitor; RR was measured by counting the patient's thoracic cage movement for 1 min, the other calculation value can be found in the calculation section (refer to Eqs. (1)(6)).

The results of this study focused on two essential aspects. The first aspect is the evaluation of the performance of the main proposed system (mechanical ventilation and respiratory parameters efficiency) as shown in Tables 13. The performance evaluation process includes four modules (three standards with Saudi Food and Drug Authority):

Table 1

Show the performance of M1 for different mechanical ventilation parameters

Device model (M1)TI (calculated)RR (setting)TCT (calculated)I:E ratio (setting)TE (calculated)Flow rate (calculate)VT (setting)MV (calculated)O2 concentration (setting)
CARESCAPE R860 (M1)1.7106.01:2.54.321.96006.321%
1.5144.31:2.02.828.765010.221%
0.8203.01:2.82.2133.34008.621%
0.8252.41:2.01.628.73509.421%
Device model (M1)TI (calculated)RR (setting)TCT (calculated)I:E ratio (setting)TE (calculated)Flow rate (calculate)VT (setting)MV (calculated)O2 concentration (setting)
CARESCAPE R860 (M1)1.7106.01:2.54.321.96006.321%
1.5144.31:2.02.828.765010.221%
0.8203.01:2.82.2133.34008.621%
0.8252.41:2.01.628.73509.421%
Table 2

Show the performance of M2 for different mechanical ventilation parameters

Device model (M2)TI (calculated)RR (setting)TCT (calculated)I:E ratio (setting)TE (calculated)Flow rate (calculate)VT (setting)MV (calculated)O2 concentration (setting)
SEVO I (M2)1.7106.01:2.54.322.26005.721%
1.5144.31:2.02.929.46509.021%
0.8203.01:2.82.233.14007.521%
0.8252.41:2.01.629.03508.321%
Device model (M2)TI (calculated)RR (setting)TCT (calculated)I:E ratio (setting)TE (calculated)Flow rate (calculate)VT (setting)MV (calculated)O2 concentration (setting)
SEVO I (M2)1.7106.01:2.54.322.26005.721%
1.5144.31:2.02.929.46509.021%
0.8203.01:2.82.233.14007.521%
0.8252.41:2.01.629.03508.321%
Table 3

Show the performance of M3 for different mechanical ventilation parameters

Device model (M3)TI (calculated)RR (setting)TCT (calculated)I:E ratio (setting)TE (calculated)Flow rate (calculate)VT (setting)MV (calculated)O2 concentration (setting)
The proposed system (M3)1.71061:2.54.3175755.721%
1.5144.291:2.02.721.55177.521%
0.82031:2.82.2222997.721%
0.8252.41:2.01.6212697.321%
Device model (M3)TI (calculated)RR (setting)TCT (calculated)I:E ratio (setting)TE (calculated)Flow rate (calculate)VT (setting)MV (calculated)O2 concentration (setting)
The proposed system (M3)1.71061:2.54.3175755.721%
1.5144.291:2.02.721.55177.521%
0.82031:2.82.2222997.721%
0.8252.41:2.01.6212697.321%
• The proposed module (M3)

• CARESCAPE R860 (M1)

• SEVO I (M2)

• Lung simulation

The second aspect is the evaluation of the efficiency of the proposed medical sensor (pulse rate and O2 saturation). In this part, 40 subjects participated as volunteers from Inaya Medical Colleges (IMCs) for training and testing the proposed system. The experiment includes two pulse oximetry modules (one is standards with Saudi Food and Drug Authority):

• The proposed pulse oximetry (M3);

• Encomium fingertip pulse oximetry (N1);

Tables 1 and 2 are data obtained from commercially available ventilators; these data were collected and used to compare with the proposed device (Table 3) data in terms of evaluating the accuracy and precision of the new device.

The study used Cohen's kappa (CK) to calculate the degree of accuracy between the proposed (M3) modules and the judge's (reference) modules (M1 and M2). Table 4 shows the comparison between the proposed system (M3) and the references system used to conduct the research. The accuracy for measuring the flow rate was 72.39 with 0.62 Cohen's kappa (CK) compared with M1. Also, the accuracy of measuring the tidal volume was shown 83% with 0.80 CK. The result of measuring minute ventilation showed a 92% accuracy with 0.91 CK comparing with the M2 module (refer Table 4).

Table 4

Show the accuracy and Cohen's kappa for the proposed system with other systems

Proposed system (M3) versus models (M1 and M2)Accuracy (%)Cohen's kappa
M3 versus M1 (flow rate)72.380.61
M3 versus M2 (flow rate)71.550.60
M3 versus M1 /M2 (tidal volume )83.000.80
M3 versus M1 (min ventilation )81.740.77
M3 versus M2 (min ventilation)92.460.91
Proposed system (M3) versus models (M1 and M2)Accuracy (%)Cohen's kappa
M3 versus M1 (flow rate)72.380.61
M3 versus M2 (flow rate)71.550.60
M3 versus M1 /M2 (tidal volume )83.000.80
M3 versus M1 (min ventilation )81.740.77
M3 versus M2 (min ventilation)92.460.91

Table 5 demonstrated the accuracy of measuring the O2 saturation by using the M3 module. The accuracy for measuring O2 saturation reached 99.6 with 0.99 CK compared with the commercial module (eccomum fingertip, Canada).

Table 5

Show the accuracy of measuring O2 saturation

Type of measurementModelAccuracy (%)CK
O2 saturationThe proposed system versus commercial device (N1)99.60.99
Type of measurementModelAccuracy (%)CK
O2 saturationThe proposed system versus commercial device (N1)99.60.99

To calculate the dependence between the proposed pulse rate (M3) and the commercial pulse oximetry (N1), the correlation coefficient was used to show the dependence and precision between the two modules. A correlation with 0.757 value shows a perfect correlation between the two devices as shown in Fig. 5.

Correlation coefficient (R2) is: 0.757; the Pearson's correlation coefficient (R) is: 0.87.

Fig. 5
Fig. 5
Close modal

Regarding the accuracy (Table 6) of the proposed M3 module in terms of reproducibility, the repeated measurements under unchanging conditions were conducted (trial1, trial2, trial3). The statistical analysis of heart rate, including means, standard deviations, and degrees of freedom are reported. The result shows no significant differences in the interaction between trials (alpha = 0.050, p-value does not exceed 0.985).

Table 6

Calculate the differences between the three measurements value of measuring pulse rate

SubjectGenderAgeTrial 1Trial 2Trial 3
Subject 1Male23606259
Subject 2Male21565457
Subject 3Male25585758
Subject 39Female22918991
Subject 40Female23918991
MeanTotal 71.07570.871.12571.3
Std. dev. of the meanTotal 11.65311.69811.5711.692
Hypothesized meanTotal 47.24347.0647.5847.09
t-static22.40337
Degrees of freedom118
SubjectGenderAgeTrial 1Trial 2Trial 3
Subject 1Male23606259
Subject 2Male21565457
Subject 3Male25585758
Subject 39Female22918991
Subject 40Female23918991
MeanTotal 71.07570.871.12571.3
Std. dev. of the meanTotal 11.65311.69811.5711.692
Hypothesized meanTotal 47.24347.0647.5847.09
t-static22.40337
Degrees of freedom118

The results of the proposed system compared with the reference system showed a significant performance for measuring some important parameters for mechanical ventilation such as flow rate, VT, and MV, as shown in Figs. 68.

Fig. 6
Fig. 6
Close modal
Fig. 7
Fig. 7
Close modal
Fig. 8
Fig. 8
Close modal

## Discussion

It is clear that the coronavirus 2019 (COVID-19) has led to a global pandemic. There are many attempts to mass manufacture mechanical ventilators. First priority, of course, is the fast implementation, low cost, and clinical safety. This study proposed a modern mechanical ventilator that provides effective ventilation. The ventilator supports the following types of modes: (i) volume-controlled modes (adaptive) and (ii) pressure support modes (adaptive). The proposed device addresses three main issues: cost, fast implementation, and accuracy. The lightweight and small size allow the device to be used to ventilate patients during in-hospital as well as at home. The compact design of the M3 mechanical ventilator makes handling much easier.

The proposed device includes a mini-computer Raspberry Pi 4 and a 7 in. touch screen. The Raspberry is used as the central core of the system; it is used for collecting, calculating, and making decisions. The 7-in. LCD touch screen is used as an interactive user interface. It is also used to display the collected data/send data from the user to the Raspberry through the touch interactive surface. The graphical user interface via the 7 in. touch screen reduces the cost and time while implementing because the device is based on software, allowing to give up from many electronic components such as switches, potentiometers, and transistors. Thus, the user setting can be entered via a keyboard or a graphical progressive scale, so the graphical interface helps avoid many electronic complexities.

The Raspberry Pi 4 and the Arduino microcontroller platform allow high-speed data processing, including the ventilator breathing parameters and patient vital signs monitoring. The reliability: all components of this device are commercially available in the local market. Also, the reliability depends on the specifications of the components. The system is designed to be simple, low cost, and easy to repair. The proposed design has several advantages: The cost may not exceed 1500\$, including electronics and design; the weight may not exceed 8 kg and the dimensions of the design are 22 cm × 18 cm × 8 cm. Therefore, these specifications make the design suitable to use at home/hospital in future. The user setting includes RR and the Ti while other commercial apparatuses such as CARESCAPE R860 (GE Healthcare), (M1) and SEVO I (MAQUET, Sweden), (M2) use the VT as the main ventilation parameters. The calculation parameters are (1) TE; (2) TCT; (3) MV and flow rate.

In order to evaluate the performance of the proposed (M3) of mechanical ventilation parameters, this study used three modules (standard with Food and Drug Administration) such as CARESCAPE R860 (M1), SEVO I (M2), and lung simulation.

To avoid ventilator-induced lung injury, it is important to track flow rate and volume exchanges regularly. Flowmeter is used in the mechanical ventilators to calculate the quantity of gas provided to patients, and the flow signal for the adjustment of the volume of gas supplied [20]. The study used CK to calculate the degree of accuracy between the proposed (M3) module versus the judge's modules (M1; M2). The result shows (Table 4) a good accuracy of the proposed (M3) module; 72% and 92% for measuring flow rate and MV, respectively. The CK was estimated by 0.61 and 0.91, respectively, which implies a good agreement or accuracy between our proposed (M3) module compared to models (M1 and M2).

The device also includes pulse oximetry (SpO2) monitoring and pulse rate. The study comprises 40 participants for the proposed SpO2 evaluation. The modules used in the study are the proposed pulse oximetry (M3) and judge (reference) pulse oximetry (N1). The result shows (Table 5) a significant accuracy for the proposed (M3) module in terms of measuring O2 saturation. The accuracy was 99% and CK near to 1. The accuracy of measuring the pulse rate was estimated by comparing the proposed measurement data versus the commotional module (N1), the correlation coefficient of the propose pulse rate data was 0.757.

## Conclusion

The proposed study demonstrated a mechanical ventilation device that addresses three main issues: the costs, the fast implementation, and the accuracy. To compare the performance of the proposed system, two significant modules used in several hospitals have been used as reference. The result showed a significant performance for measuring some important parameters such as flow rate, VT, and MV, the accuracy was 72%, 83%, and 92%, respectively.

## Acknowledgment

We would like to thank Dr. Sulaiman Al Habib Medical Group for supporting this research. This research would not be achieved and possible without the support and fund of Dr. Sulaiman Al Habib Medical Group. Also, we would thank Inaya Medical Colleges for assisting us to use their facility to conduct and complete this research.

## Conflict of Interest

There is no conflict of interest.

## References

1.
Velavan
,
T. P.
, and
Meyer
,
C. G.
,
2020
, “
The COVID‐19 Epidemic
,”
Trop. Med. Int. Health
,
25
(
3
), pp.
278
280
.10.1111/tmi.13383
2.
Giordano
,
G.
,
Blanchini
,
F.
,
Bruno
,
R.
,
Colaneri
,
P.
,
Di Filippo
,
A.
,
Di Matteo
,
A.
, and
Colaneri
,
M.
,
2020
, “
Modelling the COVID-19 Epidemic and Implementation of Population-Wide Interventions in Italy
,”
Nat. Med.
,
26
(
6
), pp.
855
860
.10.1038/s41591-020-0883-7
3.
Thomson
,
A.
,
1997
, “
The Role of Negative Pressure Ventilation
,”
Arch. Dis. Child.
,
77
(
5
), pp.
454
458
4.
Tobias
,
J. D.
,
2010
, “Conventional Mechanical Ventilation,”
Saudi J. Anaesth.
, 4(2), pp.
86
98
.10.4103/1658-354X.65128
5.
Meyer
,
T. J.
, and
Hill
,
N. S.
,
1994
, “
Noninvasive Positive Pressure Ventilation to Treat Respiratory Failure
,”
Ann. Intern. Med.
,
120
(
9
), pp.
760
770
.10.7326/0003-4819-120-9-199405010-00008
6.
Vogelmeier
,
C. F.
,
Criner
,
G. J.
,
Martinez
,
F. J.
,
Anzueto
,
A.
,
Barnes
,
P. J.
,
Bourbeau
,
J.
,
Celli
,
B. R.
,
Chen
,
R.
,
Decramer
,
M.
,
Fabbri
,
L. M.
,
Frith
,
P.
,
Halpin
,
D. M. G.
,
López Varela
,
M. V.
,
Nishimura
,
M.
,
Roche
,
N.
,
Rodriguez-Roisin
,
R.
,
Sin
,
D. D.
,
Singh
,
D.
,
Stockley
,
R.
,
Vestbo
,
J.
,
Wedzicha
,
J. A.
, and
Agustí
,
A.
,
2017
, “
Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary
,”
Am. J. Respir. Crit. Care Med.
,
195
(
5
), pp.
557
582
.10.1164/rccm.201701-0218PP
7.
Rochwerg
,
B.
,
Brochard
,
L.
,
Elliott
,
M. W.
,
Hess
,
D.
,
Hill
,
N. S.
,
Nava
,
S.
,
Navalesi
,
P.
,
Antonelli
,
M.
,
Brozek
,
J.
,
Conti
,
G.
,
Ferrer
,
M.
,
Guntupalli
,
K.
,
Jaber
,
S.
,
Keenan
,
S.
,
Mancebo
,
J.
,
Mehta
,
S.
, and
Raoof
,
S.
,
2017
, “
Official ERS/ATS Clinical Practice Guidelines: Noninvasive Ventilation for Acute Respiratory Failure
,”
Eur. Respir. J.
,
50
(
2
), p.
1602426
.10.1183/13993003.02426-2016
8.
Simonds
,
A. K.
, and
Elliott
,
M. W.
,
1995
, “
Outcome of Domiciliary Nasal Intermittent Positive Pressure Ventilation in Restrictive and Obstructive Disorders
,”
Thorax
,
50
(
6
), pp.
604
609
.10.1136/thx.50.6.604
9.
Kacmarek
,
R. M.
,
2011
, “
The Mechanical Ventilator: Past, Present, and Future
,”
Respir. Care
,
56
(
8
), pp.
1170
1180
.10.4187/respcare.01420
10.
Cook
,
T. M.
, and
Kelly
,
F. E.
,
2015
, “
Time to Abandon the ‘Vintage’ Laryngeal Mask Airway and Adopt Second-Generation Supraglottic Airway Devices as First Choice
,”
Br. J. Anaesth.
, 115(4), pp.
497
499
.10.1093/bja/aev156
11.
Platen
,
P. V.
,
Pomprapa
,
A.
,
Lachmann
,
B.
, and
Leonhardt
,
S.
,
2020
, “
The Dawn of Physiological Closed-Loop Ventilation—A Review
,”
Crit. Care
,
24
(
1
), p.
1
.10.1186/s13054-020-2810-1
12.
Chopin
,
C.
, and
Chambrin
,
M. C.
,
1993
, “
Closed-Loop Control in Mechanical Ventilation
,”
Yearbook of Intensive Care and Emergency Medicine 1993
,
Springer
,
Berlin, Heidelberg
, pp.
499
507
.
13.
Desmettre
,
T. J.
,
Chambrin
,
M. C.
,
Mangalaboyi
,
J.
,
Pigot
,
A.
, and
Chopin
,
C.
,
2005
, “
Evaluation of Auto-Regulated Inspiratory Support During Rebreathing and Acute Lung Injury in Pigs
,”
Respir. Care
,
50
(
8
), pp.
1050
1061
.http://rc.rcjournal.com/content/50/8/1050
14.
Tobin
,
M. J.
,
2001
, “
,”
New Engl. J. Med.
,
344
(
26
), pp.
1986
1996
.10.1056/NEJM200106283442606
15.
Dreyfuss
,
D.
, and
Saumon
,
G.
,
1993
, “
Role of Tidal Volume, FRC, and End-Inspiratory Volume in the Development of Pulmonary Edema Following Mechanical Ventilation
,”
Am. Rev. Respir. Disease
,
148
(
5
), pp.
1194
1203
.10.1164/ajrccm/148.5.1194
16.
Frat
,
J.-P.
,
Thille
,
A. W.
,
Mercat
,
A.
,
Girault
,
C.
,
Ragot
,
S.
,
Perbet
,
S.
,
Prat
,
G.
,
Boulain
,
T.
,
Morawiec
,
E.
,
Cottereau
,
A.
,
Devaquet
,
J.
,
Nseir
,
S.
,
Razazi
,
K.
,
Mira
,
J.-P.
,
Argaud
,
L.
,
Chakarian
,
J.-C.
,
Ricard
,
J.-D.
,
Wittebole
,
X.
,
Chevalier
,
S.
,
Herbland
,
A.
,
Fartoukh
,
M.
,
Constantin
,
J.-M.
,
Tonnelier
,
J.-M.
,
Pierrot
,
M.
,
Mathonnet
,
A.
,
Béduneau
,
G.
,
Delétage-Métreau
,
C.
,
Richard
,
J.-C. M.
,
Brochard
,
L.
, and
Robert
,
R.
,
2015
, “
High-Flow Oxygen Through Nasal Cannula in Acute Hypoxemic Respiratory Failure
,”
New Engl. J. Med.
,
372
(
23
), pp.
2185
2196
.10.1056/NEJMoa1503326
17.
Matthay
,
M. A.
, and
Zemans
,
R. L.
,
2011
, “
The Acute Respiratory Distress Syndrome: Pathogenesis and Treatment
,”
Annu. Rev. Pathol. Mech. Disease
,
6
(
1
), pp.
147
–1
63
.10.1146/annurev-pathol-011110-130158
18.
Wysocki
,
M.
,
Jouvet
,
P.
, and
Jaber
,
S.
,
2014
, “
Closed Loop Mechanical Ventilation
,”
J. Clin. Monit. Comput.
,
28
(
1
), pp.
49
56
.10.1007/s10877-013-9465-2
19.
Martin
,
E.
,
Fenaughty
,
K.
,
Parker
,
D.
,
Lubliner
,
M.
, and
Howard
,
L.
,
2018
,
Field and Laboratory Testing of Approaches to Smart Whole-House Mechanical Ventilation Control
,
Florida Solar Energy Center, Cocoa
,
FL
.
20.
Schena
,
E.
,
Massaroni
,
C.
,
Saccomandi
,
P.
, and
Cecchini
,
S.
,
2015
, “
Flow Measurement in Mechanical Ventilation: A Review
,”
Med. Eng. Phys.
,
37
(
3
), pp.
257
264
.10.1016/j.medengphy.2015.01.010