On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior Using Cardiac and Respiratory Signals
On-body Sensing of Cocaine Craving, Euphoria and Drug-Seeking Behavior Using Cardiac and Respiratory Signals
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DOI：
10.1145/3328917
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发表时间：
2019-06
影响因子：
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通讯作者：
Bhanuteja Gullapalli;A. Natarajan;G. Angarita;R. Malison;Deepak Ganesan;Tauhidur Rahman
中科院分区：
文献类型：
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作者：
Bhanuteja Gullapalli;A. Natarajan;G. Angarita;R. Malison;Deepak Ganesan;Tauhidur Rahman
药物成瘾是一种慢性脑功能障碍，会影响一个人的行为，导致无法控制药物的使用。已经针对不同类型的药物充分研究和了解了用于检测非法药物使用的无处不在的生理传感技术。然而，我们目前缺乏以可能揭示可卡因诱导的主观状态（例如，渴望和欣快）和强迫性药物寻求行为。更具体地说，可穿戴传感器检测药物相关状态的适用性尚未得到充分探索。在目前的工作中，我们在使用心电图（ECG）和呼吸信号从可穿戴胸带中不显眼地收集的可卡因渴望，欣快和寻求药物行为的建模中迈出了第一步。使用自我调节的人类实验室范例（即，“狂欢”）可卡因给药，在此期间，自我报告的可卡因诱导主观效应的视觉模拟量表（VAS）评级（即，渴望和欣快）和药物寻求行为的行为测量（即，用于药物输注的按钮点击）被收集。我们的结果是令人鼓舞的，并表明，自我报告的VAS渴望分数预测的归一化均方根误差（NRMSE）为17.6%，皮尔逊相关系数为0.49。同样，对于VAS欣快预测，NRMSE为16.7%，Pearson相关系数为0.73。我们进一步分析了不同形态相关的心电图和呼吸特征对渴望和欣快预测的相对重要性。人口因素分析揭示了一个单一因素（即，平均每可卡因使用美元（$））可以帮助进一步提高我们的渴望和欣快模型的性能。最后，我们使用心脏和呼吸信号对药物寻求行为进行建模。具体来说，我们证明，后者的信号可以预测参与者的按钮点击与F1得分为0.80，并估计不同水平的点击密度与相关系数为0.85和17.9%的NRMSE。
Drug addiction is a chronic brain-based disorder that affects a person's behavior and leads to an inability to control drug usage. Ubiquitous physiological sensing technologies to detect illicit drug use have been well studied and understood for different types of drugs. However, we currently lack the ability to continuously and passively measure the user state in ways that might shed light on the complex relationships between cocaine-induced subjective states (e.g., craving and euphoria) and compulsive drug-seeking behavior. More specifically, the applicability of wearable sensors to detect drug-related states is underexplored. In the current work, we take an initial step in the modeling of cocaine craving, euphoria and drug-seeking behavior using electrocardiographic (ECG) and respiratory signals unobtrusively collected from a wearable chest band. Ten experienced cocaine users were studied using a human laboratory paradigm of self-regulated (i.e., "binge") cocaine administration, during which self-reported visual analog scale (VAS) ratings of cocaine-induced subjective effects (i.e., craving and euphoria) and behavioral measures of drug-seeking behavior (i.e., button clicks for drug infusions) are collected. Our results are encouraging and show that self-reported VAS Craving scores are predicted with a normalized root-mean-squared error (NRMSE) of 17.6% and a Pearson correlation coefficient of 0.49. Similarly, for VAS Euphoria prediction, an NRMSE of 16.7% and a Pearson correlation coefficient of 0.73 were achieved. We further analyze the relative importance of different morphology-related ECG and respiratory features for craving and euphoria prediction. Demographic factor analysis reveals how one single factor (i.e., average dollar ($) per cocaine use) can help to further boost the performance of our craving and euphoria models. Lastly, we model drug-seeking behavior using cardiac and respiratory signals. Specifically, we demonstrate that the latter signals can predict participant button clicks with an F1 score of 0.80 and estimate different levels of click density with a correlation coefficient of 0.85 and an NRMSE of 17.9%.