Waltisberg Daniel, Amft Oliver, Brunner Daniel P, Troster Gerhard
IEEE J Biomed Health Inform. 2017 Jul;21(4):930-938. doi: 10.1109/JBHI.2016.2549938. Epub 2016 Apr 4.
We present and evaluate measurement fusion and decision fusion for recognizing apnea and periodic limb movement in sleep episodes. We used an in-bed sensor system composed of an array of strain gauges to detect pressure changes corresponding to respiration and body movement. The sensor system was placed under the bed mattress during sleep and continuously recorded pressure changes. We evaluated both fusion frameworks in a study with nine adult participants that had mixed occurrences of normal sleep, apnea, and periodic limb movement. Both frameworks yielded similar recognition accuracies of 72.1 ± ∼ 12% compared to 63.7 ± 17.4% for a rule-based detection reported in the literature. We concluded that the pattern recognition methods can outperform previous rule-based detection methods for classifying disordered breathing and period limb movements simultaneously.
我们展示并评估了用于识别睡眠期间呼吸暂停和周期性肢体运动的测量融合和决策融合方法。我们使用了一个由应变片阵列组成的床内传感器系统，以检测与呼吸和身体运动相对应的压力变化。该传感器系统在睡眠期间放置在床垫下方，并持续记录压力变化。我们在一项针对9名成年参与者的研究中评估了这两种融合框架，这些参与者的睡眠情况包括正常睡眠、呼吸暂停和周期性肢体运动的混合出现。与文献中报道的基于规则的检测方法的63.7±17.4%相比，这两种框架都产生了相似的识别准确率，为72.1±约12%。我们得出结论，模式识别方法在同时对呼吸紊乱和周期性肢体运动进行分类方面可以优于以前基于规则的检测方法。
