Longitudinal home-based monitoring of bio signals of bulbar dysfunction to aid prognostication and therapeutic monitoring in MND. The home-based devic

对延髓功能障碍生物信号进行纵向在家监测，以帮助 MND 的预测和治疗监测。

• 批准号: 2841335
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2841335
• 关键词: Longitudinal; home; based; monitoring; bio

We aim to develop a non-invasive bio-signal recording device designed to be used at home by people living with MND to collect longitudinal data and create a digital twin model of a person's bulbar function focused on swallowing.I will validate the EMG muscle sensors and microphone against a gold standard setup by BIOPAC that researcher Johnny McNulty used in a previous study. I will also validate the IMU against an accelerometer MMR-MOTION R, also used in an earlier study. The validation work will be performed with healthy volunteers.Once validated, I will run a focus group to discuss the device's design and intended use. Following this co-design stage, I will further develop the electronics and power management to make the device portable and wearable. I will also create material to teach patients and carers how to use it. Note: this is not a medical device, but a device used for scientific research. Ethics approval will be required for its use but not MHRA approval.Working with clinical MND expert James Bashford and patient representatives, we will create a list of tasks that we intend the users to carry out while wearing the sensors. These will be simple tasks, easy and quick to carry out regularly at home to collect longitudinal data. First, I will use the raw data to calculate mean, standard deviation, and peak values to observe how they evolve with time. Then, I will use that information to develop a machine-learning model that relates bio-signal data with symptoms. I will then use this to produce a prediction system that identifies parameter changes predictive of symptom evolution.While collecting data weekly at home from the patients themselves, the patients will also be asked to come once a month to the hospital or labs for further tests using high-density surface EMG, Electroglottograph and spirometry. These methods have been used in published papers and I still need to finalise which equipment I will be using.

我们的目标是开发一种非侵入性生物信号记录设备，供 MND 患者在家中使用，以收集纵向数据并创建一个专注于吞咽的人延髓功能的数字孪生模型。我将根据研究员 Johnny McNulty 在之前的研究中使用的 BIOPAC 黄金标准设置来验证 EMG 肌肉传感器和麦克风。我还将对照加速度计 MMR-MOTION R 验证 IMU，该加速度计也在早期的研究中使用过。验证工作将由健康的志愿者进行。一旦验证，我将组织一个焦点小组来讨论设备的设计和预期用途。在这个协同设计阶段之后，我将进一步开发电子和电源管理，以使设备变得便携和可穿戴。我还将创建材料来教患者和护理人员如何使用它。注意：这不是医疗设备，而是用于科学研究的设备。其使用需要伦理批准，但不需要 MHRA 批准。我们将与临床 MND 专家 James Bashford 和患者代表合作，创建一份我们希望用户在佩戴传感器时执行的任务列表。这些都是简单的任务，可以轻松快捷地定期在家中执行以收集纵向数据。首先，我将使用原始数据计算平均值、标准差和峰值，以观察它们如何随时间变化。然后，我将利用这些信息开发一个机器学习模型，将生物信号数据与症状联系起来。然后，我将使用它来创建一个预测系统，识别预测症状演变的参数变化。在每周从患者自己家里收集数据的同时，患者还将被要求每月一次到医院或实验室使用高密度表面肌电图、电声门图和肺量计进行进一步测试。这些方法已在发表的论文中使用，我仍然需要最终确定我将使用哪些设备。