ABDULQADER, Tareq (2020). Internet of medical things – integrated, ultrasound-based respiration monitoring system for incubators. Doctoral, Sheffield Hallam University.
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Abdulqader_2021_PhD_InternetMedicalThings.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Abdulqader_2021_PhD_InternetMedicalThings.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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Abstract
The study's aim was to develop a non-contact, ultrasound (US) based respiration rate
and respiratory signal monitor suitable for babies in incubators. Respiration rate
indicates average number of breaths per minute and is higher in young children than
adults. It is an important indicator of health deterioration in critically ill patients. The
current incubators do not have an integrated respiration monitor due to complexities in
its adaptation. Monitoring respiratory signal assists in diagnosing respiration rated
problems such as central Apnoea that can affect infants. US sensors are suitable for
integration into incubators as US is a harmless and cost-effective technology.
US beam is focused on the chest or abdomen. Chest or abdomen movements, caused
by respiration process, result in variations in their distance to the US transceiver located
at a distance of about 0.5 m. These variations are recorded by measuring the time of
flight from transmitting the signal and its reflection from the monitored surface.
Measurement of this delay over a time interval enables a respiration signal to be
produced from which respiration rate and pauses in breathing are determined.
To assess the accuracy of the developed device, a platform with a moving surface was
devised. The magnitude and frequency of its surface movement were accurately
controlled by its signal generator. The US sensor was mounted above this surface at a
distance of 0.5 m. This US signal was wirelessly transmitted to a microprocessor board
to digitise. The recorded signal that simulated a respiratory signal was subsequently
stored and displayed on a computer or an LCD screen. The results showed that US could
be used to measure respiration rate accurately. To cater for possible movement of the
infant in the incubator, four US sensors were adapted. These monitored the movements
from different angles. An algorithm to interpret the output from the four US sensors
was devised and evaluated. The algorithm interpreted which US sensor best detected
the chest movements.
An IoMT system was devised that incorporated NodeMcu to capture signals from the
US sensor. The detected data were transmitted to the ThingSpeak channel and
processed in real-time by ThingSpeak’s add-on Matlab© feature. The data were
processed on the cloud and then the results were displayed in real-time on a computer
screen. The respiration rate and respiration signal could be observed remotely on
portable devices e.g. mobile phones and tablets. These features allow caretakers to have
access to the data at any time and be alerted to respiratory complications.
A method to interpret the recorded US signals to determine respiration patterns, e.g.
intermittent pauses, were implemented by utilising Matlab© and ThingSpeak Server.
The method successfully detected respiratory pauses by identifying lack of chest
movements. The approach can be useful in diagnosing central apnoea. In central apnoea,
respiratory pauses are accompanied by cessation of chest or abdominal movements. The
devised system will require clinical trials and integration into an incubator by
conforming to the medical devices directives. The study demonstrated the integration
of IoMT-US for measuring respiration rate and respiratory signal. The US produced
respiration rate readings compared well with the actual signal generator's settings of the
platform that simulated chest movements.
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