Time Series Abnormal Detection

Training data

RRI interval from ecg signals(normed)

data length: 60

data dimension: 1

Theoretical Basis

google:

"Autoencoder Abnormal Detection"

"Autoencoder Outlier Detection"

"Deep learning Outlier Detection"

Base steps:

1. build a model to generate signals like raw input

2. apply model to a abnormal input

3. check model output to determine and making a decision

TODO

• Autoencoder based on MLP

• Autoencoder based on CNN

• Autoencoder based on RNN/LSTM

• Autoencoder based on CNN+RNN

• VAE model

• Some other adversarial models like GAN

• Methods based on Machine Learning / Features

Code Structure & Use

train.py --> train model

test.py --> run model

model*.py --> models

*.npy --> training data or test data, (may need to unzip)

*.pickle --> raw data, need to transform to apply a model

Smaple output

normal beats	anormal beats