Deep learning with wearable based heart rate variability for prediction of mental and general health
Deep learning with wearable based heart rate variability for prediction of mental and general health
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DOI：
10.1016/j.jbi.2020.103610
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发表时间：
2020-12-01
影响因子：
4.5
通讯作者：
Collomosse, John
中科院分区：
文献类型：
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作者：
Coutts, Louise V.;Plans, David;Collomosse, John
可穿戴生物传感器（健身带）的普及和商品化导致了个人医疗数据的泛滥，但通常只有有限的分析反馈给用户。通过评估压力、焦虑和抑郁（已知会影响心脏功能的因素）以及一般健康指标的增加水平是否可以仅使用来自手腕可穿戴设备的心率变异性（HRV）数据准确预测，研究了向用户反馈更复杂、看似无关的指标的可行性。压力，焦虑，抑郁和一般健康水平进行了评估，从主观问卷完成每周或每周两次的652名参与者。然后将这些分数转换为每个健康指标的二进制水平（高于或低于设定的阈值），并用作训练深度神经网络（LSTM）的标签，以单独使用HRV数据对每个健康指标进行分类。三种数据输入类型进行了研究：时域，频域和典型的HRV措施。对于心理健康指标，分类准确率分别达到83%和73%，分别为5分钟和2分钟的HRV数据流，显示出更好的预测能力和未来可穿戴设备用于跟踪压力和健康的潜力。
The ubiquity and commoditisation of wearable biosensors (fitness bands) has led to a deluge of personal healthcare data, but with limited analytics typically fed back to the user. The feasibility of feeding back more complex, seemingly unrelated measures to users was investigated, by assessing whether increased levels of stress, anxiety and depression (factors known to affect cardiac function) and general health measures could be accurately predicted using heart rate variability (HRV) data from wrist wearables alone. Levels of stress, anxiety, depression and general health were evaluated from subjective questionnaires completed on a weekly or twice-weekly basis by 652 participants. These scores were then converted into binary levels (either above or below a set threshold) for each health measure and used as tags to train Deep Neural Networks (LSTMs) to classify each health measure using HRV data alone. Three data input types were investigated: time domain, frequency domain and typical HRV measures. For mental health measures, classification accuracies of up to 83% and 73% were achieved, with five and two minute HRV data streams respectively, showing improved predictive capability and potential future wearable use for tracking stress and well-being.