Proceedings on Automation in Medical Engineering
Vol. 2 No. 1 (2023): Proc AUTOMED

General procedures and methods, ID 747

A single-hidden-layer neural network for the classification of spike-waveforms

Main Article Content

Luca Maximilian Meyer (Institute of Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Gießen), Prof. Dr. Thomas Schanze (Institute of Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Gießen), Fars Samann (Institute of Biomedical Engineering (IBMT), Faculty of Life Science Engineering (LSE), Technische Hochschule Mittelhessen (THM) – University of Applied Sciences, Gießen)

Abstract





The goal of this work is to use a simple neural network to classify and distinguish between pure signals of Gaussian white noise and eight different center-aligned spikes of different correlation levels. Using Python and Tensorflow, our network was trained with a supervised-learning approach. For small noise levels up to 10% (?noise= 10%max(x)), where max(x) is the maximum spike-amplitude) classification accuracy, sensitivity and specificity are 100%. Higher noise levels, e.g., ?noise=50%max(x), indicate that the network struggles with two things: distinguishing correlated data and reliably detecting pure noise.





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