Anomaly-Detection-in-Time-Series-with-Triadic-Motif-Fields
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Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification
Author: Yadong Zhang and Xin Chen
Paper:
Online demo:
Modules
| Module | Path | Note | Default Settings |
|---|---|---|---|
| Basic | 1. lib 2. data 3. model |
1. Basic functions of the project. 2. Dataset processing. 3. Saved tail model weights. |
1. - 2. no filter, z-normalization 3. MLP model |
| Classification | 1. extractor 2. classifier |
1. Features extraction of TMF images based on transfer learning. 2. Feature vectors classification to AF and non-AF probabilities. |
1. VGG16, map-reduce use 10 nodes and 5 mpisize.2. - |
| Evaluation | 1. length_effect | 1. Evaluate the trained model on varying-length ECG signals. | 1. VGG16-MLP, map-reduce use 10 nodes and 5 mpisize. |
| App | 1. pyQT app 2. bokeh app |
1. Local app for classification and interpretation. 2. Web server for interpretation. |
VGG16-MLP |
Structures of Parallel Codes (mpi)
extractor and length_effect are parallelized on the linux clustering. (map-reduce)
.py: main code..sh: script for single submission to the pbs queue.map*.py: map the tasks to multi-nodes and mpi.reduce*.py: collect the results from the finished tasks.
Guidelines of APP
| Features | Classification | Visualization | Interactive | Remote | Local |
|---|---|---|---|---|---|
| pyQT app | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :x: | :heavy_check_mark: |
| bokeh app | :x: (available in future) | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
pyQT app
- Start page (click
start)- Start button
- Process bar & status
- Main page (from top to bottom)
- Time series with label
- Symmetrized Grad-CAM of AF and its predicted probability
- Symmetrized Grad-CAM of non-AF and its predicted probability
- Sliders of
time indexanddelayto adjust the triadic time series motifs- Triad (red) in time series is corresponding to the cross (white) in two Symmetrized Grad-CAM images
- The text with red background indicates the predicted type.
bokeh app
Requirements
Python 3.6:
matplotlib
mpi4py==3.0.3
numba==0.50.1
scikit-learn==0.23.0
scipy==1.5.2
tensorflow==1.14.0
opencv-python
tqdm
PyQT5
Citation
Cite our work with:
@misc{zhang2020anomaly,
title={Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG Classification},
author={Yadong Zhang and Xin Chen},
year={2020},
eprint={2012.04936},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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