Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia worldwide, with 2% of the population affected. It is associated with an increased risk of strokes, heart failure and other heart-related complications. Monitoring at-risk individuals and detecting asymptomatic AF could result in considerable public health benefits, as individuals with asymptomatic AF could take preventive measures with lifestyle changes. With increasing affordability to wearables, personalized health care is becoming more accessible. These personalized healthcare solutions require accurate classification of bio-signals while being computationally inexpensive. By making inferences on-device, we avoid issues inherent to cloud-based systems such as latency and network connection dependency. We propose an efficient pipeline for real-time Atrial Fibrillation Detection with high accuracy that can be deployed in ultra-edge devices. The feature engineering employed in this research catered to optimizing the resource-efficient classifier used in the proposed pipeline, which was able to outperform the best performing standard ML model by $10^5\times$ in terms of memory footprint with a mere trade-off of 2% classification accuracy. We also obtain higher accuracy of approximately 6% while consuming 403$\times$ lesser memory and being 5.2$\times$ faster compared to the previous state-of-the-art (SoA) embedded implementation.
翻译:人工纤维化(AF)是全世界最常见的心律失常症(AF),占人口总数的2%。它与中风、心脏衰竭和其他心脏相关并发症的风险增加有关。监测有风险的个人和检测无症状的AF可能会带来相当的公共健康利益,因为无症状的AF可以采取预防措施改变生活方式。随着可穿戴性能的可负担性越来越高,个性化保健正在变得更加容易获得。这些个性化的保健解决方案需要精确地分类生物信号,而计算成本却很低。通过在设备上作出推断,我们避免了云基系统固有的问题,如悬浮和网络连接依赖性。我们建议为实时的Atraial纤维化检测提供高效管道,其精准性能可部署在超尖端装置中。这一研究中使用的特征工程是为了优化拟议管道中所使用的资源效率分类器,它能够比最佳的ML模型高出10 5\ 美元。在记忆足迹方面,只需要交易$和网络连接的系统。我们还提议了一个高效的管道管道管道,比先前的精确度要低2 %的精确度要高。我们还获得了比之前的精确度要低的精确度。我们获得了比之前的40的精确度。