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的生物信号记录设备，设计用于MND患者在家中收集纵向数据，并创建一个专注于吞咽的人的延髓功能的数字双胞胎模型。我将验证EMG肌肉传感器和麦克风对BIOPAC的黄金标准设置，研究人员Johnny McNesty在以前的研究中使用。我还将验证IMU对加速度计MMR-MOTION R，也在早期的研究中使用。验证工作将在健康志愿者中进行，一旦验证通过，我将组织一个焦点小组讨论该设备的设计和预期用途。在这个共同设计阶段之后，我将进一步开发电子和电源管理，使设备便携和可穿戴。我还将创建材料来教患者和护理人员如何使用它。注意：这不是一个医疗设备，但用于科学研究的设备。它的使用需要伦理批准，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，创建一个我们希望用户在佩戴传感器时执行的任务列表。这些将是简单的任务，容易和快速进行定期在家里收集纵向数据。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演变。然后，我将使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。然后我将用它来产生一个预测系统，它可以识别出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，患者也会被要求每月到医院或实验室进行一次进一步的测试，使用高密度表面EMG，Electroglottograph和肺活量测定法。这些方法已经在发表的论文中使用，我仍然需要最终确定我将使用哪种设备。