Objective. Extraction of temporal features of neuronal activity from electrophysiological data can be used for accurate classification of neural networks in healthy and pathologically perturbed conditions. In this study, we provide an extensive approach for the classification of human in vitro neural networks with and without an underlying pathology, from electrophysiological recordings obtained using a microelectrode array (MEA) platform. Approach. We developed a Dirichlet mixture (DM) Point Process statistical model able to extract temporal features related to neurons. We then applied a machine learning algorithm to discriminate between healthy control and pathologically perturbed in vitro neural networks. Main Results. We found a high degree of separability between the classes using DM point process features (p-value <0.001 for all the features, paired t-test), which reaches 93.10 of accuracy (92.37 of ROC AUC) with the Random Forest classifier. In particular, results show a higher latency in firing for pathologically perturbed neurons (4316 ms versus 6731 ms, feature distribution). Significance. Our approach has been successful in extracting temporal features related to the neurons’ behaviour, as well as distinguishing healthy from pathologically perturbed networks, including classification of responses to a transient induced perturbation.

客观的。从电生理数据中提取神经元活动的时间特征可用于在健康和病理性扰动条件下对神经网络进行准确分类。在这项研究中，我们提供了一种广泛的方法，用于根据使用微电极阵列 (MEA) 平台获得的电生理记录，对有和没有潜在病理学的人类体外神经网络进行分类。方法。我们开发了一个 Dirichlet 混合 (DM) 点过程统计模型，能够提取与神经元相关的时间特征。然后，我们应用机器学习算法来区分健康对照和病理性扰动的体外神经网络。主要结果。我们发现使用 DM 点过程特征（所有特征的 p 值 <0.001，配对 t 检验）的类之间具有高度的可分离性，使用随机森林分类器达到 93.10 的准确度（ROC AUC 的 92.37）。特别是，结果显示病理性扰动神经元的放电延迟较高（4316 ms 与 6731 ms，特征分布）。意义。我们的方法成功地提取了与神经元行为相关的时间特征，以及区分健康网络和病理性扰动网络，包括对瞬态诱导扰动的响应分类。