Data supporting: An Energy-Efficient Neuromorphic Front-End for Risk Pre-Screening Using Pulse-Encoded Biosensor Signals

“An Energy-Efficient Neuromorphic Front-End for Risk Pre-Screening Using Pulse-Encoded Biosensor Signals”, IEEE Sensors Journal, 2024.

The dataset includes:

• Synthetic pulse streams corresponding to free PSA (fPSA) and total PSA (tPSA), generated according to the empirical pulse-encoding model described in the manuscript

• Synaptic-filtered temporal representations used as inputs to the spiking neural network

• Risk category labels derived from clinically established free-to-total PSA ratio thresholds

• Python-based implementation of the spiking neural network architecture, including data generation, synaptic filtering, training, and evaluation modules

The synthetic dataset consists of 500 samples balanced across four clinically defined prostate cancer risk categories. The neuromorphic front-end operates directly in the pulse domain without reconstructing continuous biomarker concentrations or explicitly computing biomarker ratios.

This repository enables reproduction of the system-level evaluation results reported in the associated publication, including classification accuracy, confusion matrices, and layer-wise temporal activity analysis.