Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection
Open Access
Abstract
Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss. The autoencoder has an embedded attention module at the bottleneck to learn the salient activations of the encoded distribution. Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data analysis. The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy heartbeats from patients with severe congestive heart failure.Keywords
This publication has 33 references indexed in Scilit:
- A novel tracking deep wavelet auto-encoder method for intelligent fault diagnosis of electric locomotive bearingsMechanical Systems and Signal Processing, 2018
- Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI DataIEEE Transactions on Medical Imaging, 2018
- Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory DataIEEE Transactions on Industrial Informatics, 2018
- LSTM Fully Convolutional Networks for Time Series ClassificationIEEE Access, 2017
- Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identificationSignal Processing, 2017
- Representational Learning for Fault Diagnosis of Wind Turbine Equipment: A Multi-Layered Extreme Learning Machines ApproachEnergies, 2016
- Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive dataMechanical Systems and Signal Processing, 2016
- A general framework for never-ending learning from time series streamsData Mining and Knowledge Discovery, 2014
- PhysioBank, PhysioToolkit, and PhysioNetCirculation, 2000
- Long Short-Term MemoryNeural Computation, 1997