Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates

06/20/2018
by   Justus T. C. Schwabedal, et al.
0

Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classification. By computing FT surrogates of raw EEG, EOG, and EMG signals of under-represented sleep stages, we balanced the CAPSLPDB sleep database. We then trained and tested a convolutional neural network for sleep stage classification, and found that our surrogate-based augmentation improved the mean F1-score by 7 an approach to compute saliency maps for individual sleep epochs. The visualization is based on the response of inferred class probabilities under replacement of short data segments by partial surrogates. To quantify how well the distributions of the surrogates and the original data match, we evaluated a trained classifier on surrogates of correctly classified examples, and summarized these conditional predictions in a confusion matrix. We show how such conditional confusion matrices can qualitatively explain the performance of surrogates in class balancing. The FT-surrogate augmentation approach may improve classification on noisy signals if carefully adapted to the data distribution under analysis.

READ FULL TEXT

page 3

page 5

research
07/07/2021

Sleep syndromes onset detection based on automatic sleep staging algorithm

In this paper, we propose a novel method and a practical approach to pre...
research
10/05/2016

Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks

We used convolutional neural networks (CNNs) for automatic sleep stage s...
research
10/08/2018

Deep residual networks for automatic sleep stage classification of raw polysomnographic waveforms

We have developed an automatic sleep stage classification algorithm base...
research
10/15/2016

Mixed Neural Network Approach for Temporal Sleep Stage Classification

This paper proposes a practical approach to addressing limitations posed...
research
01/07/2021

MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging

Sleep staging based on electroencephalogram (EEG) plays an important rol...
research
09/26/2022

PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognition

Sleep stage recognition is crucial for assessing sleep and diagnosing ch...
research
03/17/2021

Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias

Hereditary hemolytic anemias are genetic disorders that affect the shape...

Please sign up or login with your details

Forgot password? Click here to reset