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.

精神分裂症是一种复杂的精神疾病，特征是广泛影响认知、行为和情绪的症状。本研究通过对 22 名精神分裂症患者和 32 名对照对象进行的运动活动记录数据集的调查，发现运动活动数据中时间模式对准确分类精神分裂症患者具有潜在关键作用。使用 16 种统计特征对不同时间段进行分析，并通过七种机器学习模型进行训练，结果表明 LightGBM 模型的性能优于其他六种模型。研究结果显示，时间分割显著提高了分类性能，AUC-ROC 为 0.93，F1 得分为 0.84（无分割）和 AUC-ROC 为 0.98，F1 得分为 0.93（有分割）。白天和夜晚之间的分割增加了精神分裂症患者和对照组之间的差异，但更小的时间段划分并未对 AUC-ROC 产生显著影响。早晨、下午、晚上和夜晚的分割性能类似于白天和夜晚的分割。这些发现具有重要价值，因为它们表明除了分辨白天和夜晚之外，对时间进行更大范围的分类并不能取得实质性的结果，为精神分裂症的进一步分类、早期诊断和监测提供了一种高效的方法。