This article presents a review of signals used for measuring physiology and activity during sleep and techniques for extracting information from these signals. We examine both clinical needs and biomedical signal processing approaches across a range of sensor types. Issues with recording and analysing the signals are discussed, together with their applicability to various clinical disorders. Both univariate and data fusion (exploiting the diverse characteristics of the primary recorded signals) approaches are discussed, together with a comparison of automated methods for analysing sleep.
Sleep disorders are a common problem and contribute to a wide range of healthcare issues. The societal and financial costs of sleep disorders are enormous. Sleep-related disorders are often diagnosed with an overnight sleep test called a polysomnogram, or sleep study involving the measurement of brain activity through the electroencephalogram. Other parameters monitored include oxygen saturation, respiratory effort, cardiac activity (through the electrocardiogram), as well as video recording, sound and movement activity. Monitoring can be costly and removes the patients from their normal sleeping environment, preventing repeated unbiased studies. The recent increase in adoption of smartphones, with high quality on-board sensors has led to the proliferation of many sleep screening applications running on the phone. However, with the exception of simple questionnaires, no existing sleep-related application available for smartphones is based on scientific evidence. This paper reviews the existing smartphone applications landscape used in the field of sleep disorders and proposes possible advances to improve screening approaches.
Non-invasive assessment of ventilatory control stability or loop gain (which is a key contributor in a number of sleep-related breathing disorders) has proven to be cumbersome. We present a novel multivariate autoregressive model that we hypothesize will enable us to make time-varying measurements of loop gain using nothing more than spontaneous fluctuations in ventilation and CO2. The model is adaptive to changes in the feedback control loop and therefore can account for system non-stationarities (e.g. changes in sleep state) and it is resistant to artifacts by using a signal quality measure. We tested this method by assessing its ability to detect a known increase in loop gain induced by proportional assist ventilation (PAV). Subjects were studied during sleep while breathing on continuous positive airway pressure (CPAP) alone (to stabilize the airway) or on CPAP + PAV. We show that the method tracked the PAV-induced increase in loop gain, demonstrating its time-varying capabilities, and it remained accurate in the face of measurement related artifacts. The model was able to detect a statistically significant increase in loop gain from 0.14 ± 10 on CPAP alone to 0.21 ± 0.13 on CPAP + PAV (p < 0.05). Furthermore, our method correctly detected that the PAV-induced increase in loop gain was predominantly driven by an increase in controller gain. Taken together, these data provide compelling evidence for the validity of this technique.
Patients with obstructive sleep apnea (OSA) syn drome experience repeated periods of apnea and arousal during sleep. A condition which in short term leads to excessive daytime sleepiness and in the long term may have clinical consequences such as stroke and cardiovascular abnormalities.Although complex equipment can be used to screen for sleep apnea, the screening tests are often expensive, inconvenient for the patient, and time-consuming to be manually analysed.This research investigates methods for automating sleep apnea screening using low-cost off-body cameras. Polysomnography video recordings of twenty one patients, 11 with OSA, and 10 'normals' who were referred for suspected OSA, were analysed with the objective to differentiate the two groups. The proposed technique is based on motion estimation in videos using two successive video frames. The complexities of motion signals from the video data were analysed by calculating sample entropy over multiple time scales. The sample entropy values providing the best separation between the OSA and non-OSA groups were chosen using the Bhattacharyya distance and were then used as the input to a support vector machine classifier.The classification results both on the training and validation data indicate that patients with OSA can be differentiated from patients without OSA with 90% accuracy.
The impact of poor and disrupted sleep on an individual is significant, affecting the quality of life physiologically, psychologically, and financially. It is estimated that a large population of people who suffer from sleep disorders is unaware of the condition and remains undiagnosed [1], creating a need (and desire) to self-monitor. However, sleep screening is generally cumbersome and complex, requiring multiple wearable sensors (and associated wires) and experts to interpret the large volumes of data.
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