RESUMEN
Impulse radio ultra-wide band (IR-UWB) radar has recently emerged as a promising candidate for non-contact monitoring of respiration and heart rate. Different studies have reported various radar based algorithms for estimation of these physiological parameters. The radar can be placed under a subject's mattress as he lays stationary on his back or it can be attached to the ceiling directly above the subject's bed. However, advertent or inadvertent movement on part of the subject and different postures can affect the radar returned signal and also the accuracy of the estimated parameters from it. The detection and analysis of these postural changes can not only lead to improvement in estimation algorithms but also towards prevention of bed sores and ulcers in patients who require periodic posture changes. In this paper, we present an algorithm that detects and quantifies different types of motion events using an under-the-mattress IR-UWB radar. The algorithm also indicates a change in posture after a macro-movement event. Based on the findings of this paper, we anticipate that IR-UWB radar can be used for extracting posture related information in non-clinical enviroments for patients who are bed-ridden.
Asunto(s)
Algoritmos , Monitoreo Fisiológico/métodos , Postura/fisiología , Radar , Adulto , Femenino , Frecuencia Cardíaca/fisiología , Humanos , Masculino , Persona de Mediana Edad , Monitoreo Fisiológico/instrumentación , Movimiento , Radar/instrumentación , Respiración , Procesamiento de Señales Asistido por ComputadorRESUMEN
In this work, we extract features from an under-the-mattress impulse radio ultra-wide band (IR-UWB) radar and a microphone, placed on the side table of the bed, to classify epochs belonging to normal sleep and those that contain an apnea event in them. Sleep apnea is the most common form of sleep related breathing disorder in adults with an estimated prevalance of 5-15%. The common diagnostic process for sleep apnea, polysomnography (PSG), involves sleeping in well-equipped sleep clinics. The cost and discomfort associated with the process has spurred research towards the design of portable home-based monitoring devices. However, these include sensors which need to be attached to patients at various locations on the body. In this preliminary and on-going study, we collected 18 hours of data of 3 subjects who were previously diagnosed with sleep apnea. The data was recorded using non-contact sensors, an IR-UWB radar and a microphone, in a sleep clinic along with the time synchronized gold-standard PSG data. A simple linear classifier was used to perform binary classification between normal and apnea epochs and the performance was analyzed compared to the true results provided by the PSG. It was observed, that combining snore features from the microphone data improves the overall accuracy of the classifier.