Non-Contact REM Sleep Estimation Correction by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data
Non-Contact REM Sleep Estimation Correction by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data
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DOI：
10.1109/embc48229.2022.9871503
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Iko Nakari;Naoya Matsuda;K. Takadama
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文献类型：
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作者：
Iko Nakari;Naoya Matsuda;K. Takadama
研究了基于床垫传感器采集的生物振动数据的快速眼动睡眠（REM）估计方法，并提出了基于随机森林（RF）作为机器学习（ML）方法之一的REM睡眠预测时间序列置信度（TSC）的"校正"方法。不同于传统的ML分类是否REM睡眠或不作为其二进制预测，所提出的方法确定是否估计的REM睡眠应该被校正或不从其连续预测。具体地，所提出的方法将REM睡眠预测计算为对每个时期（30秒）的REM睡眠进行分类的树的百分比，通过对特定数量的时期的REM睡眠预测加窗以平滑它们来计算REM睡眠预测的TSC，并且当TSC低于特定阈值时，校正由其他ML估计的REM睡眠。通过对人类受试者的实验，我们发现：（1）所提出的方法在快速眼动睡眠的突然错误估计中显示了一个小的TSC，这有助于纠正它;以及（2）由于所提出的方法的这个特征，成功地减少了REM睡眠估计的假阳性的数量，其将精密度从51.4%（w/o TSC）提高到59.4%（w/TSC）。
This paper focuses on the REM sleep estimation with bio-vibration data acquired from mattress sensor, and proposes its “correction” method based on Time-Series Confidence (TSC) of the REM sleep prediction calculated by Random Forest (RF) as one of the Machine Learnings (MLs). Unlike the conventional MLs that classify whether the REM sleep or not as its binary prediction, the proposed method determines whether the estimated REM sleep should be corrected or not from its continuous prediction. Concretely, the proposed method computes the REM sleep prediction as the percentage of trees that classify the REM sleep for each epoch (30 seconds), calculates TSC of the REM sleep prediction by windowing the REM sleep prediction of a certain number of epochs to smooth them, and the REM sleep estimated by other MLs is corrected when TSC is lower than a certain threshold. Through the human subject experiments, the following implications have been revealed: (1) the proposed method shows a small TSC in the sudden wrong REM sleep estimation, which contributes to correct it; and (2) because of this feature of the proposed method, the number of False-Positive of the REM sleep estimation is successfully reduced, which improves Precision from 51.4% (w/o TSC) to 59.4% (w/ TSC).