Background: Accurate anomaly detection in brain and cardiac signals is essential for advancing diagnostic accuracy in neurological and cardiological research. Traditional signal processing methods often face challenges in preserving signal integrity while identifying anomalies.

Objective: This study investigates the application of Bessel activation functions in transforming brain and cardiac signals to facilitate effective anomaly detection and correlation analysis.

Methods: Bessel activation functions were applied to a dataset of brain and cardiac signals to transform the data. The transformed signals were then compared with the original signals using Pearson correlations to evaluate the preservation of signal integrity. Additionally, anomaly detection was performed by identifying peaks in the activated signals.

Results: The application of Bessel activation functions resulted in a significant improvement in the identification of anomalies, with the method effectively discerning peaks corresponding to signal anomalies. Pearson correlation analyses demonstrated that the Bessel activation preserved signal integrity, with correlation coefficients consistently above 0.9 across all samples.

Conclusion: This study demonstrates the potential of Bessel activation functions in enhancing the accuracy of anomaly detection in brain and cardiac signals while maintaining the integrity of the original data. These findings contribute to the broader field of signal processing and offer promising implications for improving diagnostic approaches in medical and scientific research.