Daphne I. Townsend

Relative Thresholding With Under-Mattress Pressure Sensors to Detect Central Apnea

Unobtrusive pressure sensors can be used for biological monitoring and long-term health assessment in smart homes. The challenge in detecting events from smart home data is that people have different mattresses, unlike in hospitals where bedding is standardized. This paper proposes to model central apneas using an under-mattress pressure sensor as a measuring instrument. The model uses three parameters, namely, a relative threshold and two time lengths, applied to a moving variance signal. The use of a relative threshold allows apneas to be detected under a variety of different conditions and improves results compared to hard-coded thresholds. The algorithm developed herein was applied to simulated apneas collected from pressure sensors placed under nine different mattresses. The parameters determined from the training set were applied to the test set and produced classification results of 0.78 positive predictive value (PPV) if the bed occupants position is known and 0.75 PPV if the position is unknown. The use of the relative threshold approach overcomes the variability in mattress types found in smart homes.

Validation of Unobtrusive Pressure Sensor Array for Central Sleep Apnea Screening

A new understanding about the role and importance of sleep in health combined with rapidly aging demographics presents opportunities for research and development of new approaches in sleep monitoring. As well, challenges with the current sleep-monitoring solution can be addressed by studying the suitability of new monitoring technologies. This paper presents a small-scale validation of the unobtrusive pressure sensor array compared with traditional polysomnography (PSG), for use as a central apnea (CA) screening tool. Algorithms developed for the pressure sensor array provided a very good detection of the CAs as compared to the gold-standard data for the six patients studied whose body-mass index was appropriate for the sensor. For the retained patients, the algorithm classified CA events with an average sensitivity of 87.6%, specificity of 99.9%, and Cohens kappa value of 0.875. This work evaluates the ability of an algorithm applied to the unobtrusive pressure sensor array to detect CAs. The sensor array was compared to three other signals: 1) expert PSG interpreters; 2) inductance plethysmography (IP) bands alone; and 3) IP bands combined with an airflow or oxygen-saturation sensor. The impact of unobtrusive CA detection on an older adults health could be in the areas of broadening of the access to sleep monitoring, longitudinal monitoring of the disease progression, and possibly providing information on the interaction between CA and other disease processes.

Validation of pressure sensors for physiological monitoring in home environments

Previous research using pressure sensors to monitor physiological signs, such as breathing and heart rate, has often been in controlled laboratory or institutional settings. We are interested in validating the use of such sensors in diverse home environments, where unobtrusiveness is achieved by embedding pressure sensors inside or below furniture.We have quantitatively examined outputs from sensors placed below a variety of mattress types and compared the response to outputs from sensors placed on top of the mattress. We show that embedded pressure sensors are valid for use in home environments and could be reliably used to collect physiological data. However, resultant low signal levels may require more sophisticated signal extraction algorithms.

Integration of new technology in a legacy system for collecting medical data - challenges and lessons learned

Integrating new technology into a legacy medical system can be very challenging. Completely new systems cannot always be built due to the high cost of medical equipment, thus integrating some new technology into an existing system may be required. This paper looks at the issues and challenges surrounding the integration of new components into a legacy system for collecting medical data. We discuss how the issues were solved, the lessons learned, and how future upgrades can be made more easily.

Simulated central apnea detection using the pressure variance

This paper presents use of an unobtrusive pressure sensor array for simulated central apnea detection. Data was collected from seven volunteers who performed a series of regular breathing and breath holding exercises to simulate central apneas. Results of the feature extraction from the breathing signals show that breathing events may be differentiated with epoch based variance calculations. Two approaches were considered: the single sensor approach and the multisensor vote approach. The multisensor vote approach can decrease false positives and increase the value of Matthew’s Correlation Coefficient. The effect of lying position on correct classification was investigated by modifying the multisensor vote approach to reduce false positives segments caused by the balistocardiogram signal and as such increase sensitivity while maintaining a low false positive rate. Intersubject classification results had low variability in both approaches.

Complimentary artificial neural network approaches for prediction of events in the neonatal intensive care unit

In the neonatal intensive care unit, the early and accurate prediction of mortality, length of stay and duration of ventilation can improve decision making. For physiological events, non-linear prediction models generally out-perform statistical-based approaches, as was confirmed in these experiments. For three medical outcomes, the maximum-likelihood (ML) approximation was used in conjunction with a gradient descent artificial neural network (ANN) prototype to create models with risk estimation ranges. The ML ANN showed that the ML estimation function was successful at creating variable sensitivity models for three important outcomes. The flexibility of the ML ANN in terms of output values differentiates it from the more traditional ANN.

Preliminary results on the effect of sensor position on unobtrusive rollover detection for sleep monitoring in smart homes

Older adults experience increased sleep movement disorders and sleep fragmentation, and these are associated with serious health consequences such as falls. Monitoring sleep fragmentation and restlessness in older adults can reveal information about their daily and long-term health status. Long-term home monitoring is only realistic within the contact of unobtrusive, non-contact sensors. This paper presents exploratory work using the pressure sensor array as an instrument for rollover detection. The sensor output is used to calculate a center of gravity signal, from which five features are extracted. These features are used in a decision tree to classify detected movements in two categories; rollovers and other movements. Rollovers were detected with a sensitivity and specificity of 82% and 100% respectively, and a Mathew’s correlation coefficient of 0.86 when data from all sensor positions were included. Intrapositional and interpositional effects of movements on sensors placed throughout the bed are described.

Breathing sensor selection during movement

A pressure sensor array placed below a mattress can be used to estimate the breathing effort signal unobtrusively. When multiple breathing effort sensor outputs are available, there is sometimes a need to choose the sensor with the best approximation of the actual breathing effort. Previous work with pressure sensor arrays placed on top of or under mattresses used for respiration rate and breathing signal estimation have used either the amplitude or the power spectrum to choose the most representative sensor. These methods are both useful when the subject is still; however, pressure sensor signals also contain movement. We propose and test a spectral ratio method for selection in the presence of movement. The spectral ratio method is good at finding strong breathing signals and at discriminating movement signals from strong breathing signals. This method provides a mean correlation to respiration bands that is 4% higher than the next best method during small movements and 14% higher during larger movements.

Effect of windowing on central apnea detection

The detection of central apneas using an unobtrusive pressure sensor array installed in the beds of smart homes could allow comfortable diagnosis of sleep disturbances. To improve central apnea detection, two methods of improving the results of apneas classified by a previously developed method are presented: moving average windowing and window elimination. The first improved classifier sensitivity, while the second improved the specificity with better duration estimation. However, it was slightly more likely to miss apnea segments altogether.

Measuring chest movement using an array of unobstusive pressure sensors

The use of unobtrusive sensors for physiological monitoring is growing in popularity. Advantages such as their non-contact nature and their limited cognitive demand on the user can increase acceptance and usefulness in certain populations. Biomedical applications for unobtrusive pressure sensors include the analysis of bed transfer sequences and the extraction of breathing rate during long-term trend analysis and health monitoring. This paper proposes an algorithm to measure chest wall motion using an unobtrusive pressure sensor array in a combined signal format and compare it to measurements derived from respiratory inductance plethysmography bands. The novel contributions are using an unobtrusive pressure sensor array beneath the mattress to determine the duration of inspiration and expiration segments, and to determine the timing of each movement. The cross-correlation was used to align signals for the pressure array. The durations of the movements were statistically similar, and the timing of the identified movements overlapped considerably (≫80%) when the signals were aligned using the calculated delay value.