An artificial neural network for automated behavioral state classification in rats.
An artificial neural network for automated behavioral state classification in rats.
复制标题
用于大鼠自动行为状态分类的人工神经网络。
作者：
Ellen JG;Dash MB
准确的行为状态分类对于许多研究应用是至关重要的。研究人员通常依赖于通过视觉检查的电生理信号的行为状态的手动识别，但这种方法是时间密集型的，并受到低评分者之间的可靠性。为了克服这些限制，已经提出了一组不同的算法方法来自动化分类过程。最近，新的机器学习方法已经被详细描述，可以产生快速和高度准确的分类。然而，这些方法通常在计算上是昂贵的，需要大量的专业知识来实现，和/或需要限制更广泛采用的专有软件。在这里，我们详细介绍了一种新的人工神经网络，使用电生理功能自动分类的行为状态，在大鼠具有高准确性，灵敏度和特异性。睡眠科学家感兴趣的常见参数，包括状态依赖性功率谱和稳态非REM慢波活动，与手动评分相比，使用此自动分类器时没有显著差异。灵活的选项使研究人员能够通过手动重新评分一小部分具有低模型预测确定性的时间间隔来进一步提高分类准确性，或者通过在多个记录日内概括训练过的网络来进一步减少研究人员的时间。该算法是完全开源的，并在一个流行的，免费提供的软件平台中编码，以增加对该研究工具的访问，并为未来的研究人员提供额外的灵活性。总之，我们已经开发出一种易于实现的，高效的，有效的方法，在大鼠的自动行为状态分类。
Accurate behavioral state classification is critical for many research applications. Researchers typically rely upon manual identification of behavioral state through visual inspection of electrophysiological signals, but this approach is time intensive and subject to low inter-rater reliability. To overcome these limitations, a diverse set of algorithmic approaches have been put forth to automate the classification process. Recently, novel machine learning approaches have been detailed that produce rapid and highly accurate classifications. These approaches however, are often computationally expensive, require significant expertise to implement, and/or require proprietary software that limits broader adoption. Here we detail a novel artificial neural network that uses electrophysiological features to automatically classify behavioral state in rats with high accuracy, sensitivity, and specificity. Common parameters of interest to sleep scientists, including state-dependent power spectra and homeostatic non-REM slow wave activity, did not significantly differ when using this automated classifier as compared to manual scoring. Flexible options enable researchers to further increase classification accuracy through manual rescoring of a small subset of time intervals with low model prediction certainty or further decrease researcher time by generalizing trained networks across multiple recording days. The algorithm is fully open-source and coded within a popular, and freely available, software platform to increase access to this research tool and provide additional flexibility for future researchers. In sum, we have developed a readily implementable, efficient, and effective approach for automated behavioral state classification in rats.
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