Non-stationary neural signal to image conversion framework for image-based deep learning algorithms

Sahaj Anilbhai Patel; Abidin Yildirim

doi:10.3389/fninf.2023.1081160

Non-stationary neural signal to image conversion framework for image-based deep learning algorithms

Front Neuroinform. 2023 Mar 24:17:1081160. doi: 10.3389/fninf.2023.1081160. eCollection 2023.

Authors

Sahaj Anilbhai Patel¹, Abidin Yildirim¹

Affiliation

¹ Department of Electrical and Computer Engineering, The University of Alabama at Birmingham, Birmingham, AL, United States.

Abstract

This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham's line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG).

Keywords: 2D convolution neural network (2D CNN); Bresenham’s line algorithm; biomedical signals; electroencephalogram (EEG); non-stationary signal to 2D image representation.