身体外睡眠分析用于预测自闭症谱系障碍患者的不良行为
Yashar Kiarashi1, Pradyumna B Suresha1, Ali Bahrami Rad1
1Department of Biomedical Informatics, Emory University, Atlanta, GA.
medRxiv : the preprint server for health sciences
|February 12, 2024
概括
患有自闭症谱系障碍 (ASD) 个体的睡眠质量差预测了第二天具有挑战性的行为. 保护隐私的睡眠监测可以预测这些行为,使及时干预成为可能.
科学领域:
- 神经科学是一个神经科学.
- 行为科学 行为科学
- 机器学习 机器学习
背景情况:
- 睡眠质量差在自闭症谱系障碍 (ASD) 中很普遍.
- 患有自闭症的睡眠障碍与日间挑战性行为增加有关,例如侵略和自我伤害.
- 预测这些行为对于有效的干预和护理至关重要.
研究的目的:
- 研究一夜间睡眠结构对ASD个体第二天行为的预测能力.
- 开发和验证机器学习模型,根据睡眠数据预测具有挑战性的行为.
- 评估保护隐私的可行性,体外睡眠监测用于ASD行为预测.
主要方法:
- 在两年内收集了14名自闭症患者的夜间运动数据,使用了一种低成本,保护隐私的近红外摄像头.
- 追踪了具有挑战性的白天行为,包括侵略,自我伤害和干扰.
- 开发了一种集体机器学习算法,根据睡眠模式预测早上和下午的行为.
主要成果:
- 睡眠质量是早晨行为比下午行为更可靠的预测指标.
- 机器学习模型在目标敏感任务中实现了74%的准确性和0.74F1得分.
- 在14名参与者中,有7人观察到比机会更好的预测准确性,其中3人显示出显著的趋势.
结论:
- 保护隐私的体外睡眠监测是一种可行的方法,可以预测ASD患者第二天的不良行为.
- 这种方法有可能使积极的行为干预和提高在教育和社会环境中的护理.
- 了解睡眠与行为之间的联系,可以为患有自闭症的人提供更好的支持策略.
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