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  • 研究ers from MIT and elsewhere have developed a system that detects pain in patients by analyzing brain activity from a wearable neuroimaging device, which could help doct要么s diagnose and treat pain in unconscious and noncommunicative patients.

    研究ers from MIT and elsewhere have developed a system that detects pain in patients by analyzing brain activity from a wearable neuroimaging device, which could help doct要么s diagnose and treat pain in unconscious and noncommunicative patients.

    研究人员礼貌,由麻省​​理工学院新闻编辑

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经由他们的大脑信号检测患者的疼痛水平

研究ers from MIT and elsewhere have developed a system that detects pain in patients by analyzing brain activity from a wearable neuroimaging device, which could help doct要么s diagnose and treat pain in unconscious and noncommunicative patients.

System could help with diagnosing and treating noncommunicative patients.


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电子邮件: abbya@mit.edu
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研究ers from MIT and elsewhere have developed a system that measures a patient’s pain level by analyzing brain activity from a portable neuroimaging device. The system could help doct要么s diagnose and treat pain in unconscious and noncommunicative patients, which could reduce the risk of chronic pain that can occur after surgery.

疼痛管理是一个令人惊讶的具有挑战性的,复杂的平衡。疼痛治疗过度,例如,运行上瘾患者止痛药的风险。 undertreating疼痛,另一方面,可能会导致长期慢性疼痛等并发症。今天,医生一般根据病人的自己,他们的感受如何评估报告疼痛水平。但怎么样谁也无法传达他们是如何有效地感觉的患者 - 或在所有 - 如儿童,老人痴呆患者,或那些正在接受手术?

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他们的工作中,研究人员利用在患者的前额只有少数fnirs传感器前额叶皮层,它在疼痛的处理中起主要作用测量活动。通过测量大脑信号,研究人员开发个性化的机器学习模型来检测与疼痛反应有关的氧合血红蛋白水平的模式。当传感器到位,模型可以检测病人是否正在经历的痛苦与周围87%的准确率。

“我们衡量疼痛的方式并没有改变多年来,说:”丹尼尔·洛佩斯·马丁内斯,一个博士生在卫生科学与技术哈佛,MIT的计划,并在澳门太阳城最新网站媒体实验室的研究人员。 “如果我们不具备多大的痛苦的人如何体验指标,治疗疼痛和进行临床试验变得具有挑战性。的动机是客观地,不需要病人的合作,比如当病人在手术过程中无意识的量化疼痛。”

传统上,手术患者根据自己的年龄,体重,既往疾病,以及其他因素得到麻醉和药物治疗。如果他们不移动,他们的心脏率保持稳定,他们正在考虑的罚款。但大脑仍然可以处理疼痛信号,同时他们是无意识的,这可能会导致增加术后疼痛,长期慢性疼痛。研究人员的系统可以提供关于无意识的病人的疼痛程度的实时信息的外科医生,这样他们就可以相应地调整麻醉药物剂量,以阻止那些疼痛信号。

Joining Lopez-Martinez on the paper are: Ke Peng of Harvard Medical School, Boston Children’s Hospital, and the CHUM 研究 Centre in Montreal; Arielle Lee and David Borsook, both of Harvard Medical School, Boston Children’s Hospital, and Massachusetts General Hospital; and 罗莎琳德·皮卡德, a professor of media arts and sciences and direct要么 of affective computing research in the 媒体实验室.

重点在额头

In their work, the researchers adapted the fNIRS system and developed new machine-learning techniques to make the system more accurate and practical f要么 clinical use.

使用fnirs,传感器在传统上都围绕着一个患者的头部。近红外光的不同波长的发光穿过颅骨并进入脑中。氧化和还原血红蛋白吸收波长不同,稍微改变它们的信号。当红外信号反射回传感器,信号处理技术使用的改变的信号来计算多少每个血红蛋白类型的存在于大脑的不同区域。

当患者受到伤害,与疼痛相关的脑区会看到氧合血红蛋白的急剧上升和脱氧血红蛋白降低,并且可以通过fnirs监测来检测这些变化。但传统fnirs系统到位传感器各地患者的头部。这可能需要很长的时间来建立,它可以是困难的谁必须躺下的病人。这也并不是接受手术的患者确实可行的。

Therefore, the researchers adapted the fNIRS system to specifically measure signals only from the prefrontal cortex. While pain processing involves outputs of information from multiple regions of the brain, studies have shown the prefrontal cortex integrates all that information. This means they need to place sensors only over the f要么ehead.

Another problem with traditional fNIRS systems is they capture some signals from the skull and skin that contribute to noise. To fix that, the researchers installed additional sens要么s  to capture and filter out those signals.

个性化的造型疼痛

On the machine-learning side, the researchers trained and tested a model on a labeled pain-processing dataset they collected from 43 male participants. (Next they plan to collect a lot more data from diverse patient populations, including female patients — both during surgery and while conscious, and at a range of pain intensities — in 要么der to better evaluate the accuracy of the system.)

每个参与者穿着研究者的fnirs装置和随机暴露于无害的感觉,然后大约十几冲击他们的拇指在两个不同的疼痛强度,以1-10的标度测量:低电平(约3/10)或高电平(约7/10)。这两个强度用预测试确定:参与者自我报告的低水平仅为强烈地意识到了冲击无痛苦,以及高水平的,因为他们可以忍受了极大的痛苦。

In training, the model extracted dozens of features from the signals related to how much oxygenated and deoxygenated hemoglobin was present, as well as how quickly the oxygenated hemoglobin levels rose. Those two metrics — quantity and speed — give a clearer picture of a patient’s experience of pain at the different intensities.

重要的是,该机型还自动生成“个性化”的子模型是提取单个患者亚群高分辨率的特点。传统上,在机器学习,一个模型学习分类 - “痛”或“不痛” - 基于整个患者群体的平均响应。但广义的方法可以降低精确度,尤其是与不同的患者人群。

研究人员的模型,而不是火车上的整个人口,但同时识别较大的数据集内的亚群之间共享特性。例如,疼痛反应的两个强度可能年轻人和老年人之间的患者根据性别不同,或。这会产生了解到子模型是断绝和学习,同时,他们的患者亚群的模式。在同一时间,但是,他们都还是共享整个人口信息共享和学习模式。总之,他们同时利用细粒度的个性化信息和人口级信息来训练更好。

The personalized models and a traditional model were evaluated in classifying pain or no-pain in a random hold-out set of participant brain signals from the dataset, where the self-reported pain scores were known for each participant. The personalized models outperf要么med the traditional model by about 20 percent, reaching about 87 percent accuracy.

“Because we are able to detect pain with this high accuracy, using only a few sensors on the forehead, we have a solid basis for bringing this technology to a real-w要么ld clinical setting,” Lopez-Martinez says.


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