SBIR Phase I: Real-time arrhythmia detection and classification using a waterproof armband

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是帮助在心律失常变得危险之前识别出有阵发性或无症状的心律失常的人。心房颤动(房颤)是最常见的心律失常，且房颤的发生率随着年龄的增长而增加。房颤对人的寿命和生活质量有深远的影响。患者通常会出现阵发性、短暂的心律失常发作，并且经常没有症状。这往往会导致房颤的诊断延迟到心律失常更持续的后期。即使阵发性房颤发作很短，也会增加中风、心力衰竭、住院和死亡的风险。未诊断房颤的人群很多，研究表明，需要连续3年以上的心电监测才能实现接近100%的房颤检测。目前的30天监护仪可以检测到5%的阵发性和无症状病例，因为事件必须在佩戴监护仪时发生。一种基于臂带的方法提供了一种方法，以克服目前胸部监护仪的限制，并在短期30天监护仪和长期植入式监护仪之间架起一座桥梁。拟议的项目使用基于臂带的设备，使用防水和无线设备捕获上臂的心电(CG)和肌电(EMG)信号。传感器是由一种可以干湿操作的复合材料制成的，不需要粘合剂或水凝胶来捕捉波形的所有方面。上臂面临的挑战是，心电信号的幅度小于心脏信号，因此必须去除运动/噪声伪影以及肌电信号，以创建干净的心电信号。这项研究的重点是将算法嵌入到设备中，并测试和改进算法，以消除加速度计捕获的运动和噪声伪影，以及删除肌电信号。在建立干净的信号后，可以嵌入设备中运行经过验证的心律失常分类算法。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to help identify people that have paroxysmal or asymptomatic cardiac arrhythmias before they become dangerous. Atrial Fibrillation (AF) is the most common arrhythmia, and the prevalence of AF increases with age. AF has a profound impact on longevity and quality of life. Patients typically develop paroxysmal, short-lived episodes of the arrhythmia and are frequently asymptomatic. This often leads to delays in diagnosis of AF until later stages when the arrhythmia is more persistent. Even short episodes of paroxysmal AF are associated with increased risk for stroke, heart failure, hospitalization, and death. The population with undiagnosed AF is substantial and studies have shown that continuous ECG monitoring for more than 3 years is required to achieve close to 100% AF detection. Current 30-day monitors detect 5% of paroxysmal and asymptomatic cases, since events must occur while the monitor is being worn. An armband-based approach offers a way to overcome the current restrictions in chest based monitors and bridges the gap between short-duration 30-day monitors and long-term implanted monitors.The proposed project uses an armband-based device to capture electrocardiogram (ECG) and electromyogram (EMG) signals on the upper arm using a waterproof and wireless device. The sensors are made from a composite material that can operate both wet and dry and does not require adhesives or hydrogels to capture all aspects of the waveforms. The challenge with the upper arm is that the ECG signals have less magnitude then over-the-heart and it is necessary to remove motion / noise artifacts as well as EMG signals to create a clean ECG signal. The research is focused on embedding algorithms into the device and testing and refining them for removing motion and noise artifacts that are captured by an accelerometer and to remove EMG signals. After a clean signal is established, proven algorithms for classification of the arrhythmias can be run embedded in the device.