Objective

To validate automatic sleep stage classification using deep neural networks on sleep assessed by radar technology in the commercially available sleep assistant Somnofy® against polysomnography (PSG).

Methods

Seventy-one nights of overnight sleep in healthy individuals were assessed by both PSG and Somnofy at two different institutions. The Somnofy unit was placed in two different locations per room (nightstand and wall). The sleep algorithm was validated against PSG using a 25-fold cross validation technique, and performance was compared to the inter-rater reliability between the PSG sleep scored by two independent sleep specialists.

Results

Epoch-by-epoch analyses showed a sensitivity (accuracy to detect sleep) and specificity (accuracy to detect wake) for Somnofy of 0.97 and 0.72 respectively, compared to 0.99 and 0.85 for the PSG scorers. The sleep stage differentiation for Somnofy was 0.75 for N1/N2, 0.74 for N3 and 0.78 for R, whilst PSG scorers ranged between 0.83 and 0.96. The intraclass correlation coefficient revealed excellent and good reliability for total sleep time and sleep efficiency, while sleep onset and R latency had poor agreement. Somnofy underestimated total wake time by 5 min and N1/N2 by 3 min. N3 was overestimated by 4 min and R by 3 min. Results were independent of institution and sensor location.

Conclusion

Somnofy showed a high accuracy staging sleep in healthy individuals and has potential to assess sleep quality and quantity in a sample of healthy, mostly young adults. More research is needed to examine performance in children, older individuals and those with sleep disorders.

中文翻译：

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使用非接触式雷达技术和机器学习（Somnofy®）验证睡眠阶段分类。

目的

为了在市场上使用针对多导睡眠图（PSG）的睡眠助手Somnofy®中的雷达技术评估的睡眠深度神经网络来验证自动睡眠阶段分类。

方法

PSG和Somnofy在两个不同的机构对健康个体的71个晚上的夜间睡眠进行了评估。将Somnofy单元放置在每个房间的两个不同位置（床头柜和墙）。使用25倍交叉验证技术针对PSG对睡眠算法进行了验证，并将性能与两名独立睡眠专家对PSG睡眠之间的评分者间可靠性进行了比较。

结果

逐时分析显示Somnofy的敏感度（检测睡眠的准确性）和专一性（检测觉醒的准确性）分别为0.97和0.72，而PSG评分者为0.99和0.85。N1 / N2的Somnofy睡眠阶段分化为0.75，N3的为0.74，R的为0.78，而PSG得分介于0.83至0.96之间。组内相关系数显示了总的睡眠时间和睡眠效率极好的和良好的可靠性，而睡眠发作和R潜伏期的一致性较差。Somnofy低估了5分钟的总唤醒时间，低估了3分钟的N1 / N2。N3高估了4分钟，R高估了3分钟。结果与机构和传感器位置无关。

结论

Somnofy在健康个体中表现出高精度的分期睡眠，并有潜力评估健康的样本（主要是年轻人）中的睡眠质量和数量。需要更多的研究来检查儿童，老年人和睡眠障碍者的表现。