大鼠药物依赖戒断症状自动化检测系统设计与研究

药物滥用导致的药物依赖是十分严重的社会问题。大部分神经类药物具有依赖性，而且近年来一直位居全球新上市药物数量前列，药物依赖性评价有很大的市场需求。药物依赖性评价需要进行三类特异性的依赖性试验，其中大鼠的戒断评价试验是用于评价药物的躯体依赖性最重要的试验。目前大鼠药物依赖戒断评价主要依赖专业人员耳听目测，观测人员很容易出现视听疲劳等问题导致戒断症状漏记、错记等主观性错误，影响人们对受试药物的正确判断，导致监管不当。大鼠的药物依赖戒断症状多种多样，研究人员选取的戒断症状评价指标并不统一。本文通过总结大鼠药物依赖戒断症状相关文献，确定了湿狗样抖动、站立、刻板、舔阴、齿颤为关键戒断行为。针对这些行为设计了由多角度高速摄像机、麦克风和触摸感知电路相融合的多模态戒断行为采集系统。其中湿狗样抖动、刻板、舔阴行为识别利用图像双帧差分算法计算像素的灰度值变化，将视频信号转化为一维信号后提取快速运动成分，大大减少了计算量，用YOLOv5算法进行二次识别，保证了行为识别准确率；采用提取色彩后进行图像腐蚀与膨胀操作对大鼠站立行为进行识别。语音信号处理采用提取梅尔倒频谱系数后进行支持向量机分类的方法，对大鼠齿颤行为进行了识别。 利用上述系统对大鼠吗啡依赖戒断模型进行了验证，能够将吗啡依赖组大鼠与对照组大鼠进行区分，满足药物依赖评价的基本需求，可以被用于大鼠药物依赖戒断症状的自动化评价。

Drug dependence caused by drug abuse is a very serious social problem. Most neurodrugs have dependence, and in recent years, they have been ranked among the top of the number of newly released drugs in the world, so there is a great market demand for drug dependence evaluation. Drug dependence evaluation requires three kinds of specific dependence tests, among which the rat withdrawal evaluation test is the most important test to evaluate the physical dependence of drugs. At present, the evaluation of drug withdrawal in rats mainly relies on the auditory and visual observation of professionals. Observers are prone to visual and visual fatigue, which leads to subjective errors such as omission and clerical error of withdrawal symptoms, it may leads to improper supervision. The withdrawal symptoms of drug dependence in rats are varied, and the evaluation indicators of withdrawal symptoms selected by researchers are not uniform. By summarizing the literature on drug dependence withdrawal symptoms in rats, the key withdrawal behaviors of drug dependence in rats were identified as wet-dog shaking, rearing, stereotyping, genital grooming and teeth chattering. A multi-mode withdrawal behavior acquisition system was designed based on multi-angle high-speed camera, microphone and touch sensing circuit. Among them, wet-dog shaking, stereotyping and genital grooming behavior recognition uses the image double-frame difference algorithm to calculate the change of pixel gray value, converts the video signal into one-dimensional signal and extracts the fast motion component, which greatly reduces the calculation amount. YOLOv5 algorithm is used for second recognition, ensuring the accuracy of behavior recognition. The rearing of rats was recognized by image erosion and swelling after color extraction. In audio signal processing, the teeth chattering of rats was recognized by the method of support vector machine classification after extracting Mel-frequency cepstral coefficient. The above system was used to verify the rat morphine dependence withdrawal model, which can distinguish the morphine dependence withdrawal group from the control group, meet the basic needs of drug dependence evaluation, and can be used for the automatic evaluation of drug dependence withdrawal symptoms in rats.

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