Shouldice Redmond B, Heneghan Conor, Petres Gabor, Zaffaroni Alberto, Boyle Patricia, McNicholas Walter, de Chazal Philip
BiancaMed, NovaUCD, University College Dublin, 4, Ireland.
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:630-3. doi: 10.1109/IEMBS.2010.5627275.
An automated real time method for detecting human breathing rate from a non contact biosensor is considered in this paper. The method has low computational and RAM requirements making it well-suited to real-time, low power implementation on a microcontroller. Time and frequency domain methods are used to separate a 15s block of data into movement, breathing or absent states; a breathing rate estimate is then calculated. On a 1s basis, 96% of breaths were scored within 1 breath per minute of expert scored respiratory inductance plethysmography, while 99% of breaths were scored within 2 breaths per minute. When averaged over 30s, as is used in this respiration monitoring system, over 99% of breaths are within 1 breath per minute of the expert score.
本文考虑了一种用于从非接触式生物传感器检测人体呼吸率的自动化实时方法。该方法具有较低的计算和内存需求,使其非常适合在微控制器上进行实时、低功耗实现。使用时域和频域方法将15秒的数据块分为运动、呼吸或无状态;然后计算呼吸率估计值。以1秒为基础,96%的呼吸得分与专家评分的呼吸感应体积描记法每分钟1次呼吸范围内,而99%的呼吸得分在每分钟2次呼吸范围内。在本呼吸监测系统中使用的30秒平均时间内,超过99%的呼吸与专家评分每分钟1次呼吸范围内。