⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出了一款用于心理健康监测的非线性HRV-EEG混沌处理器，集成了体信道通信接口。该处理器通过结合心率和脑电信号进行压力检测，实现了259.6µW的超低功耗。
Recently, there are many reports on the measurement of mental health conditions as derived from physiological parameters – see references in [1]. Usually, heart-rate or heart rate variability (HRV) has been used for monitoring mental health because of its strong dependency on the autonomic nervous system (ANS) [1-2]. Reference [1] reported that ECG, respiration, skin conductance and EMG data collected by means of wearable sensors was combined and analyzed to get 80% accurate detection of mental stress. Another important way to measure stress is by the combination of HRV and EEG. It is claimed that HRV, which represents the effect of ANS, and EEG, which is the primary signal of the central nervous system (CNS), should be analyzed together at the same time in order to extract accurate physiological markers for mental stress and disorder. Using this approach there was a report of stress detection with 90% confidence [3]. In contrast to the case of only HRV, however, this approach requires
Taehwan Roh, Sunjoo Hong, Hyunwoo Cho, Hoi-Jun Yoo
KAIST, Daejeon, Korea