A multimodal seizure detection artificial intelligence-based smart wear

基于多模态癫痫检测人工智能的智能穿戴

• 批准号: 538852-2019
• 负责人: Nguyen, Dang
• 金额: $ 7.8万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Health Research Projects
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=692921
• 关键词: multimodal; seizure; detection; artificial; intelligence

Epilepsy is a chronic neurological condition that affects as many as 1 in every 100 Canadians. With the ageing of the population, the prevalence and incidence of epilepsy among the elderly is greater than any other age group due to the increasing number brain insults. The first line of treatment consists of long-term drug therapy but more than a third of patients are said to be drug-resistant and continue to suffer from disabling seizures. Due to their unpredictable nature, uncontrolled seizures represent a major personal handicap and source of worriment for patients (risks of injury and death). In addition, persistent seizures constitute a considerable public health burden due to high use of health care resources, high number ofdisability days or unemployment, and low annual income. For these patients, accurate and rapid detection of seizures could significantly improve their care. Indeed, a system capable of detecting seizures could alert family members, caretakers or medical personnel to intervene in a timely fashion to limit the risk of injuries and death. Hence, the main objective of thisproject is to develop a user-friendly artificial intelligence-based seizure detection system based on non-invasive multimodal physiological signals obtained through smart wearable devices. Social, ethical, environmental, economic, and legal implications will be taken into account to ensure a smooth and rapid translation to patients with epilepsy. Potential applications are numerous for the benefit of patients (reducing anxiety and enhancing selfconfidence), caretakers (evaluation and surveillance), and the healthcare system (reducing associated costs).

癫痫是一种慢性神经系统疾病，每 100 名加拿大人中就有 1 人患有癫痫症。随着人口老龄化，老年人脑损伤的发生率和发病率均高于其他年龄段。一线治疗包括长期药物治疗，但据说超过三分之一的患者具有耐药性，并继续遭受致残性癫痫发作。由于其不可预测性，不受控制的癫痫发作是患者的主要障碍和担忧的根源（受伤和死亡的风险）。此外，由于卫生保健资源的大量使用、大量的伤残天数或失业以及低年收入，持续性癫痫发作构成了相当大的公共健康负担。对于这些患者来说，准确、快速地检测癫痫发作可以显着改善他们的护理。事实上，能够检测癫痫发作的系统可以提醒家人、看护人员或医务人员及时进行干预，以限制受伤和死亡的风险。因此，该项目的主要目标是开发一种用户友好的基于人工智能的癫痫检测系统，该系统基于通过智能可穿戴设备获得的非侵入性多模态生理信号。将考虑社会、伦理、环境、经济和法律影响，以确保顺利、快速地向癫痫患者提供治疗。对于患者（减少焦虑和增强自信）、护理人员（评估和监测）和医疗保健系统（降低相关成本）来说，潜在的应用有很多。