Interpretable heartbeat classification using local model-agnostic explanations on ECGs
Abstract Treatment and prevention of cardiovascular diseases often rely on
Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG …
Electrocardiogram (ECG) interpretation. Dependent on the physician's variability, ECG …
Performing calculus with epsilon-near-zero metamaterials
Calculus is a fundamental subject in mathematics and extensively used in physics and
astronomy. Performing calculus operations by analog computing has received much recent …
astronomy. Performing calculus operations by analog computing has received much recent …
Multivariate time series classification with parametric derivative dynamic time warping
Multivariate time series (MTS) data are widely used in a very broad range of fields, including
medicine, finance, multimedia and engineering. In this paper a new approach for MTS …
medicine, finance, multimedia and engineering. In this paper a new approach for MTS …
Hierarchical clustering of time series data with parametric derivative dynamic time warping
M Łuczak - Expert Systems with Applications, 2016 - Elsevier
Abstract Dynamic Time Warping (DTW) is a popular and efficient distance measure used in
classification and clustering algorithms applied to time series data. By computing the DTW …
classification and clustering algorithms applied to time series data. By computing the DTW …
Dynamic time warping as an alternative to windowed cross correlation in seismological applications
We investigate the feasibility of using the dynamic time warping (DTW) technique as an
alternative to windowed cross correlation (WCC) for an indirect measure to quantify both the …
alternative to windowed cross correlation (WCC) for an indirect measure to quantify both the …
PISD: A linear complexity distance beats dynamic time warping on time series classification and clustering
Over the past decades, Dynamic Time Warping (DTW) and its variants have been widely
adopted as the most effective similarity measures for time series. Nevertheless, they suffer …
adopted as the most effective similarity measures for time series. Nevertheless, they suffer …
Classification of time series using combination of DTW and LCSS dissimilarity measures
T Górecki - Communications in Statistics-simulation and …, 2018 - Taylor & Francis
In the domain of time series, different dissimilarity measures are applied for comparing
sequences, the most successful ones being based on dynamic programming. Such …
sequences, the most successful ones being based on dynamic programming. Such …
Similarity Measure of Time Series With Different Sampling Frequencies Based on Context Density Consistency and Dynamic Time Warping
W Li, R He, B Liang, F Yang… - IEEE Signal Processing …, 2023 - ieeexplore.ieee.org
Similarity measure of time series with different sampling frequencies is vitally important for
many signal processing applications. Dynamic Time Warping (DTW) is one of the most …
many signal processing applications. Dynamic Time Warping (DTW) is one of the most …
An exhaustive comparison of distance measures in the classification of time series with 1nn method
Time series classification is an important and challenging problem in data analysis. With the
increase in time series data availability, hundreds of algorithms have been proposed. A …
increase in time series data availability, hundreds of algorithms have been proposed. A …
Using derivatives in a longest common subsequence dissimilarity measure for time series classification
T Górecki - Pattern Recognition Letters, 2014 - Elsevier
Over recent years the popularity of time series has soared. Given the widespread use of
modern information technology, a large number of time series may be collected. As a …
modern information technology, a large number of time series may be collected. As a …