Detection of Nocturnal Scratching Movements in Patients with Atopic Dermatitis Using Accelerometers and Recurrent Neural Networks
Detection of Nocturnal Scratching Movements in Patients with Atopic Dermatitis Using Accelerometers and Recurrent Neural Networks
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DOI：
10.1109/jbhi.2017.2710798
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发表时间：
2018-07-01
影响因子：
7.7
通讯作者：
Peterson, Barry
中科院分区：
文献类型：
--
作者：
Moreau, Arnaud;Anderer, Peter;Peterson, Barry
特应性皮炎是一种慢性炎症性皮肤病，影响儿童和成人，并与瘙痒有关。一种客观量化夜间抓挠事件的方法可以帮助开发特应性皮炎和其他过敏性疾病的治疗方法。高分辨率腕关节活动记录仪（以>= 20 Hz采样的三维加速度计传感器）是一种记录运动的非侵入性方法。提出了一种基于体动记录数据的夜间抓挠事件检测算法。该双重过程包括将数据分割成“无运动”、“单手运动”和“双手运动”，然后使用双向递归神经网络分类器将运动片段区分成刮擦和其他运动。将性能与从24名受试者（6名健康对照和18名特应性皮炎患者）收集的手动评分的红外视频数据进行比较，证明F-1评分为0.68，等级相关性为0.945。该算法明显优于基于腕动记录仪的已发表参考方法（F-1评分为0.09，等级相关性为0.466）。结果表明，抓挠运动可以准确地区分从其他夜间运动。
Atopic dermatitis is a chronic inflammatory skin condition affecting both children and adults and is associated with pruritus. A method for objectively quantifying nocturnal scratching events could aid in the development of therapies for atopic dermatitis and other pruritic disorders. High-resolution wrist actigraphy (three-dimensional accelerometer sensors sampled at >= 20 Hz) is a noninvasive method to record movement. This paper presents an algorithm to detect nocturnal scratching events based on actigraphy data. The twofold process consists of segmenting the data into "no motion," "single handed motion," and "both handed motion" followed by discriminating motion segments into scratching and other motion using a bidirectional recurrent neural network classifier. The performance was compared against manually scored infrared video data collected from 24 subjects (6 healthy controls and 18 atopic dermatitis patients) demonstrating an F-1 score of 0.68 and a rank correlation of 0.945. The algorithm clearly outperformed a published reference method based on wrist actigraphy (F-1 score of 0.09 and a rank correlation of 0.466). The results suggest that scratching movements can be discriminated from other nocturnal movements accurately.