Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Czech Republic.
Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Czech Republic.
J Neurosci Methods. 2019 Apr 1;317:61-70. doi: 10.1016/j.jneumeth.2019.01.013. Epub 2019 Feb 7.
The classification of sleep signals is a subjective and time consuming task. A large number of automatic classifiers have been published in the past decade but a sleep community has no strong confidence to use them in clinical practice and still remains using a standard manual scoring according standardized rules.
We developed a semi-supervised data-driven approach for objective and efficient evaluation of polysomnographic (PSG) data. The proposed algorithm finds a representative set of signal segments that are subsequently scored by a sleep neurologist. The remaining part of the recording is then automatically classified using these templates.
The method was evaluated on 36 PSG recordings (18 chronic insomniacs, 18 healthy controls). We show a faster and objective evaluation of PSG data compared to the manual scoring that is over-performing automated classifiers (accuracy increases ∼14%). The classification results are comparable on both datasets.
COMPARISON WITH EXISTING METHOD(S): The methodology that we propose has not yet been published in the area of sleep PSG data processing. The performance of our method is comparable to various published automated approaches (a typical published classification accuracy is ∼75-95%). The method allows the evaluation of PSG recordings in more general terms and across different recording devices and standards.
The proposed solution is not based on a single-purpose rules or heuristics and training model is not trained on other patient's sleep recordings. The method is applicable to wide range of similar tasks and various types of physiological signals.
睡眠信号的分类是一项主观且耗时的任务。在过去的十年中，已经发布了大量的自动分类器，但睡眠领域的专家仍然没有信心将它们用于临床实践，而是仍然按照标准化规则使用标准的手动评分。
我们开发了一种半监督的数据驱动方法，用于客观有效地评估多导睡眠图（PSG）数据。该算法找到了一组有代表性的信号段，然后由睡眠神经学家对这些信号段进行评分。然后，使用这些模板对记录的其余部分进行自动分类。
该方法在 36 份 PSG 记录（18 名慢性失眠症患者，18 名健康对照者）上进行了评估。与手动评分相比，该方法能够更快、更客观地评估 PSG 数据，并且表现优于自动分类器（准确性提高了约 14%）。在两个数据集上，分类结果都是可比的。
我们提出的方法在睡眠 PSG 数据处理领域尚未发表。我们的方法的性能与各种已发表的自动方法相当（典型的已发表分类准确性约为 75-95%）。该方法允许更全面地评估 PSG 记录，并适用于不同的记录设备和标准。
所提出的解决方案不是基于单一用途的规则或启发式方法，并且训练模型也不是基于其他患者的睡眠记录进行训练的。该方法适用于广泛的类似任务和各种类型的生理信号。
