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/商业潜力是帮助识别患有阵发性或无症状性心律失常的人，以免他们变得危险。心房颤动（AF）是最常见的心律失常，并且AF的患病率随着年龄的增长而增加。AF对寿命和生活质量有着深远的影响。患者通常发生阵发性、短暂的心律失常发作，并且通常无症状。这通常导致AF诊断延迟，直到心律失常更持久的后期阶段。即使阵发性房颤发作时间较短，也会增加中风、心力衰竭、住院和死亡的风险。未确诊房颤的人群相当多，研究表明，需要连续3年以上的ECG监测才能实现接近100%的房颤检测。目前的30天监测仪检测到5%的阵发性和无症状病例，因为事件必须在佩戴监测仪时发生。基于臂带的方法提供了一种方法，以克服目前的限制，在胸部为基础的监测和桥梁之间的差距差距短期30天的监测和长期植入monitors.The拟议的项目使用臂带为基础的设备，以捕捉心电图（ECG）和肌电图（EMG）信号的上臂使用防水和无线设备。传感器由复合材料制成，可以在潮湿和干燥的情况下工作，并且不需要粘合剂或水凝胶来捕获波形的所有方面。上臂的挑战在于ECG信号的幅度小于心脏上的幅度，并且必须去除运动/噪声伪影以及EMG信号以创建干净的ECG信号。该研究的重点是将算法嵌入到设备中，并对其进行测试和改进，以消除加速度计捕获的运动和噪声伪影，并消除EMG信号。在建立干净的信号之后，可以运行嵌入在设备中的用于心律失常分类的经验证的算法。

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.