Zaman Md Sabbir, Morshed Bashir I
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5967-5970. doi: 10.1109/EMBC44109.2020.9175317.
Artificial intelligence (AI) algorithms including machine and deep learning relies on proper data for classification and subsequent action. However, real-time unsupervised streaming data might not be reliable, which can lead to reduced accuracy or high error rates. Estimating reliability of signals, such as from wearable sensors for disease monitoring, is thus important but challenging since signals can be noisy and vulnerable to artifacts. In this paper, we propose a novel "Data Reliability Metric (DReM)" and demonstrate the proof-of-concept with two bio signals: electrocardiogram (ECG) and photoplethysmogram (PPG). We explored various statistical features and developed Artificial Neural Network (ANN), Random Forest (RF) and Support Vector Machine (SVM) models to autonomously classify good quality signals from the bad quality signals. Our results demonstrate the performance of the classification with a cross-validation accuracy of 99.7%, sensitivity of 100%, precision of 97% and F-score of 96%. This work demonstrates the potential of DReM to objectively and automatically estimate signal quality in unsupervised real-time settings with low computational requirement suitable for low-power digital signal processing techniques on wearables.
包括机器学习和深度学习在内的人工智能（AI）算法依赖于合适的数据进行分类及后续操作。然而，实时无监督流数据可能不可靠，这会导致准确率降低或错误率升高。因此，估计信号的可靠性很重要且具有挑战性，比如来自用于疾病监测的可穿戴传感器的信号，因为信号可能有噪声且容易受到伪迹的影响。在本文中，我们提出了一种新颖的“数据可靠性度量（DReM）”，并通过两种生物信号——心电图（ECG）和光电容积脉搏波图（PPG）——进行了概念验证。我们探索了各种统计特征，并开发了人工神经网络（ANN）、随机森林（RF）和支持向量机（SVM）模型，以自动将高质量信号与低质量信号分类。我们的结果表明，分类性能的交叉验证准确率为99.7%，灵敏度为100%，精确率为97%，F值为96%。这项工作展示了DReM在无监督实时环境中客观自动估计信号质量的潜力，其计算要求低，适用于可穿戴设备上的低功耗数字信号处理技术。
