Schizophrenia is a complicated mental illness characterized by a broad spectrum of symptoms affecting cognition, behavior, and emotion. The task of identifying reliable biomarkers to classify Schizophrenia accurately continues to be a challenge in the field of psychiatry. We investigate the temporal patterns within the motor activity data as a potential key to enhancing the categorization of individuals with Schizophrenia, using the dataset having motor activity recordings of 22 Schizophrenia patients and 32 control subjects. The dataset contains per-minute motor activity measurements collected for an average of 12.7 days in a row for each participant. We dissect each day into segments (Twelve, Eight, six, four, three, and two parts) and evaluate their impact on classification. We employ sixteen statistical features within these temporal segments and train them on Seven machine learning models to get deeper insights. LightGBM model outperforms the other six models. Our results indicate that the temporal segmentation significantly improves the classification, with AUC-ROC = 0.93, F1 score = 0.84( LightGBM- without any segmentation) and AUC-ROC = 0.98, F1 score = 0.93( LightGBM- with segmentation). Distinguishing between diurnal and nocturnal segments amplifies the differences between Schizophrenia patients and controls. However, further subdivisions into smaller time segments do not affect the AUC- ROC significantly. Morning, afternoon, evening, and night partitioning gives similar classification performance to day-night partitioning. These findings are valuable as they indicate that extensive temporal classification beyond distinguishing between day and night does not yield substantial results, offering an efficient approach for further classification, early diagnosis, and monitoring of Schizophrenia.

翻译：精神分裂症是一种复杂的心理疾病，其症状广泛涉及认知、行为和情感等领域。在精神病学领域，识别可靠的生物标志物以准确分类精神分裂症仍是一项挑战。本研究探索运动活动数据中的时间模式，将其作为提升精神分裂症患者分类性能的潜在关键，使用的数据集包含22名精神分裂症患者和32名对照受试者的运动活动记录。该数据集记录了每位受试者连续平均12.7天的每分钟运动活动测量值。我们将每天划分为不同数量的时段（十二段、八段、六段、四段、三段及两段），并评估这些分割方式对分类的影响。在时间分割后的各时段内，我们提取16项统计特征，并基于七种机器学习模型进行训练以获取更深入的分析。其中，LightGBM模型的表现优于其他六种模型。研究结果表明，时间分割显著提升了分类性能：无分割时LightGBM的AUC-ROC为0.93，F1分数为0.84；而采用分割后，AUC-ROC达到0.98，F1分数为0.93。区分白天与夜间时段能进一步放大精神分裂症患者与对照受试者之间的差异。然而，进一步细分至更短的时间段对AUC-ROC的影响并不显著。将一天划分为早晨、下午、傍晚和夜晚四个时段所获得的分类性能与昼夜二分的划分方式相近。这些发现具有重要意义，表明在昼夜区分之外进行更细粒度的时间分类并未带来显著效益，从而为精神分裂症的进一步分类、早期诊断及监测提供了高效的方法。