Estimating Reliability of Signal Quality of Physiological Data from Data Statistics Itself for Real-time Wearables
Estimating Reliability of Signal Quality of Physiological Data from Data Statistics Itself for Real-time Wearables
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DOI：
10.1109/embc44109.2020.9175317
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Md Sabbir Zaman;B. Morshed
中科院分区：
文献类型：
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作者：
Md Sabbir Zaman;B. Morshed
包括机器学习和深度学习在内的人工智能 (AI) 算法依赖于正确的数据进行分类和后续操作。然而，实时无监督流数据可能不可靠，这可能导致准确性降低或错误率较高。因此，估计信号（例如用于疾病监测的可穿戴传感器）的可靠性非常重要，但也具有挑战性，因为信号可能有噪声并且容易受到伪影的影响。在本文中，我们提出了一种新颖的“数据可靠性指标（DReM）”，并用两种生物信号演示了概念验证：心电图（ECG）和光电体积描记图（PPG）。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，以自动将优质信号与劣质信号进行分类。我们的结果证明了分类的性能，交叉验证准确度为 99.7%，灵敏度为 100%，精确度为 97%，F 分数为 96%。这项工作展示了 DReM 在无监督实时设置中客观、自动估计信号质量的潜力，计算要求低，适合可穿戴设备上的低功耗数字信号处理技术。
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.