Gadaleta Matteo, Rossi Michele, Topol Eric J, Steinhubl Steven R, Quer Giorgio
Scripps Research Translational Institute, Scripps Research, La Jolla, CA, US.
Department of Information Engineering (DEI), University of Padova, Italy.
Computer (Long Beach Calif). 2019 Nov;52(11):18-29. doi: 10.1109/MC.2019.2932716. Epub 2019 Oct 21.
The automatic and unsupervised analysis of biomedical time series is of primary importance for diagnostic and preventive medicine, enabling fast and reliable data processing to reveal clinical insights without the need for human intervention. Representation learning (RL) methods perform an automatic extraction of meaningful features that can be used, e.g., for a subsequent classification of the measured data. The goal of this study is to explore and quantify the benefits of RL techniques of varying degrees of complexity, focusing on modern deep learning (DL) architectures. We focus on the automatic classification of atrial fibrillation (AF) events from noisy single-lead electrocardiographic signals (ECG) obtained from wireless sensors. This is an important task as it allows the detection of sub-clinical AF which is hard to diagnose with a short in-clinic 12-lead ECG. The effectiveness of the considered architectures is quantified and discussed in terms of classification performance, memory/data efficiency and computational complexity.
生物医学时间序列的自动且无监督分析对诊断和预防医学至关重要，它能实现快速可靠的数据处理，无需人工干预即可揭示临床见解。表示学习（RL）方法可自动提取有意义的特征，例如用于对测量数据进行后续分类。本研究的目的是探索和量化不同复杂度的RL技术的益处，重点关注现代深度学习（DL）架构。我们专注于从无线传感器获取的有噪声单导联心电图信号（ECG）中自动分类房颤（AF）事件。这是一项重要任务，因为它能检测出临床症状不明显的房颤，而这种房颤很难通过短时间的门诊12导联心电图进行诊断。从分类性能、内存/数据效率和计算复杂度方面对所考虑架构的有效性进行了量化和讨论。
