Context-aware algorithms for sleep apnea monitoring and sensor acceptance using unobtrusive pressure sensors arrays

Townsend, Daphné

doi:10.22215/etd/2012-09526

Cited by 2 publications

(2 citation statements)

References 70 publications

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“…An extensive investigation on respiration monitoring, apnea detection, and movement detection using pressure sensor array was conducted by Townsend et al [38], [40] and…”

Section: Pressure-based Respiration Rate Measurementmentioning

confidence: 99%

Neonatal Respiratory Rate Monitoring Using a Pressure-Sensitive Mat

Bekele¹

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Pressure measurement using a Pressure Sensitive Mat (PSM) enables a non-contact approach for monitoring patient vital signs such as respiration rate (RR) and heart rate (HR). Non-contact patient monitoring in Neonatal Intensive Care Units (NICU) can improve the quality of patient care and reduce patient discomfort. This thesis investigates the applicability of PSM for monitoring of neonatal patient respiration rate in the NICU.A clinical trial was conducted with the Children's Hospital of Eastern Ontario (CHEO) collecting PSM data from 15 patients in three different bed types. Gold standard RR and annotations of patient movement and interventions were collected simultaneously.Algorithms for estimating RR were developed by optimizing spatial downsampling, reference signal selection, enhancement, and combining. The RR estimation results were evaluated against gold standard estimates from a commercial patient monitor. The best RR estimation method worked well when the patient is on ventilator support achieving a mean absolute error (MAE) of 3.9 +/-6.6 bpm on clean data without patient movements or interventions. For this patient, 89.7% of RR estimates were within the clinically acceptable range of +/-10 bpm, while 78.4% fell within the more stringent limits of +/-5 bpm.When the RR estimation algorithm is evaluated on all patients, RR estimation performance is mixed. In particular, very low mass neonates and patients laying on multiple blankets presented challenges for RR estimation. Taken together, a 95% confidence interval (CI) for agreement between the estimated and gold standard RR was estimated to be [-44.8, 51.9] bpm with a mean bias of 3.5 bpm, using a mixed effects limits of agreement (LoA)

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“…An extensive investigation on respiration monitoring, apnea detection, and movement detection using pressure sensor array was conducted by Townsend et al [38], [40] and…”

Section: Pressure-based Respiration Rate Measurementmentioning

confidence: 99%

Neonatal Respiratory Rate Monitoring Using a Pressure-Sensitive Mat

Bekele¹

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“…There is always a small range of random noise in the sensors pressure value, even under fixed load condition [71]. Methods of reducing noise in order to display clean velocity plots will be discussed in this section for producing an accurate interpretation of movement velocity magnitude.…”

Section: Noise Suppression In the Computed Cop Velocity Magnitudementioning

confidence: 99%

Patient Mobility Monitoring Through the Analysis of Bed Exit Centre of Pressure

Ren¹

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The increase in the older adult population and the benefits of independent living are leading to the introduction of new approaches for monitoring the health and well-being of seniors living in their own homes. Technologies that can monitor an older person's daily activities and detect changes in functionality have the potential to act as early warning systems so that help can be provided before a serious health event occurs. Home monitoring can contribute to the creation of more supportive environments for aging at home. This thesis uses a pressure-sensitive mat that is placed between a bed frame and the mattress. The pressure data can reveal important clinical information about the bed occupant. This thesis deals with the analysis of bed exit characteristics in terms of centre of pressure trajectory and its dynamic behaviour. The algorithms were tested on data collected from bed occupants to demonstrate the various clinical features. A graphical user interface (GUI) was designed to assist health care providers in interpreting and comparing these clinical features. This work will be used to monitor frail older adults in their own homes to assist with early detection of mobility decline.ii

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Context-aware algorithms for sleep apnea monitoring and sensor acceptance using unobtrusive pressure sensors arrays

Cited by 2 publications

References 70 publications

Neonatal Respiratory Rate Monitoring Using a Pressure-Sensitive Mat

Neonatal Respiratory Rate Monitoring Using a Pressure-Sensitive Mat

Patient Mobility Monitoring Through the Analysis of Bed Exit Centre of Pressure

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