The Departments of Biomedical Engineering and Biostatistics & Bioinformatics, Duke University, Durham, NC 27708, USA.
The Division of Cardiology and the Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA 94143, USA.
Sensors (Basel). 2020 May 9;20(9):2700. doi: 10.3390/s20092700.
The dynamic time warping (DTW) algorithm is widely used in pattern matching and sequence alignment tasks, including speech recognition and time series clustering. However, DTW algorithms perform poorly when aligning sequences of uneven sampling frequencies. This makes it difficult to apply DTW to practical problems, such as aligning signals that are recorded simultaneously by sensors with different, uneven, and dynamic sampling frequencies. As multi-modal sensing technologies become increasingly popular, it is necessary to develop methods for high quality alignment of such signals. Here we propose a DTW algorithm called EventDTW which uses information propagated from defined events as basis for path matching and hence sequence alignment. We have developed two metrics, the error rate (ER) and the singularity score (SS), to define and evaluate alignment quality and to enable comparison of performance across DTW algorithms. We demonstrate the utility of these metrics on 84 publicly-available signals in addition to our own multi-modal biomedical signals. EventDTW outperformed existing DTW algorithms for optimal alignment of signals with different sampling frequencies in 37% of artificial signal alignment tasks and 76% of real-world signal alignment tasks.
动态时间规整（DTW）算法广泛应用于模式匹配和序列比对任务，包括语音识别和时间序列聚类。然而，当对齐不均匀采样频率的序列时，DTW 算法的性能会下降。这使得 DTW 难以应用于实际问题，例如对齐由不同、不均匀且动态采样频率的传感器同时记录的信号。随着多模态传感技术的日益普及，有必要开发用于高质量对齐此类信号的方法。在这里，我们提出了一种称为 EventDTW 的 DTW 算法，它使用从定义的事件传播的信息作为路径匹配和序列对齐的基础。我们已经开发了两种度量标准，即错误率（ER）和奇异分数（SS），以定义和评估对齐质量，并能够比较不同 DTW 算法的性能。除了我们自己的多模态生物医学信号外，我们还在 84 个公开可用的信号上展示了这些度量标准的实用性。在 37%的人工信号对齐任务和 76%的真实世界信号对齐任务中，EventDTW 在最优对齐具有不同采样频率的信号方面优于现有的 DTW 算法。
