Introduction
The Inductive Respiration (RIP) sensor is a popular respiration sensor applied in a variety of different research applications in which respiration monitoring is required.
In many research applications, inducing apnea, for example by holding the breath, is required to stimulate certain responses from the body by alternating between normal breathing and apnea phases. This can be of interest if the goal is to induce stress to a subject to monitor their physiological reaction, which in this case, might be the body’s response to a restricted oxygen supply.
This post aims to present the use of the biosignalsplux RIP sensor in such a context and to highlight the resulting change in sensor signals, which can be counter-intuitive regarding the expected outcome of the signal.
Step 1 – Sensor Placement & Data Acquisition
The acquisition of Respiration (RIP) sensor data can be acquired by placing the sensor according to one of the positions presented in the following illustration.
The sensor data has been acquired in position b by following these breathing dynamics:
- Phase: Normal breathing without pre-defined breathing pattern (0:00min to 0:30min)
- Phase: Apnea (0:30min to 0:45min)
- Phase: Normal breathing without pre-defined breathing pattern (0:45min to 1:15min)
- Phase: Apnea (1:15min to 1:30min)
- Phase: Normal breathing without pre-defined breathing pattern (1:30min to end)
The acquired raw data is presented in the following plot figure in which we can observe that the periodic signal components that are caused by the breathing dynamics during inhalation and expiration (more about this in the next steps).
Step 2 – A Closer Look at Respiration Sensor Data
In this acquired signal, we can observe that the periodic signal components that typical for respiration signals and caused by the breathing dynamics during inhalation and expiration, i.e. one respiration cycle, as the example highlighted in the plot figure below.
The duration of each cycle represents a breath-to-breath interval using which respiration data (e.g. breaths-per-minute) is commonly extracted.
In addition, the sensor data appears to revolve around a baseline line which, in this example, can be approximated by the mean of the signal as presented in the following plot figure.
Step 3 – Analysing Sensor Behavior and Identification of Apnea Phases
As previously stated, the Respiration (RIP) sensor measures displacements only. This means that changes in the sensor signal do only occur if the sensor is stretched during inhalation (signal increases) or relaxed during expiration (signal decreases).
During apnea phases (here induced by holding the breath), however, the signal returns to the previously identified baseline regardless if one holds the breath immediately after inhalation or after expiration. This might be counter-intuitive as one would expect that the signal would remain at the last value reached before holding the breath.
Using this information, it is possible to identify apnea phases by monitoring sections of the signal in which the signal flattens near the baseline as highlighted in the following plot figure, where the signal returns to the baseline shortly after the apnea phase begins.
It must be noted, that, although the signal surpasses the baseline, it does slowly return to near-baseline values until the subject returns to normal breathing rhythms. For this reason, it is recommended to identify the baseline of each recording which afterwards can be a helpful tool to detect apnea phases with near-baseline values and to distinguish them from normal breathing dynamics which lead to significantly higher signal peak-to-peak amplitudes.
