Digital filtering dissemination for optimizing impedance cytometry signal quality and counting accuracy

Area of Science:

• Biomedical Engineering
• Signal Processing
• Analytical Chemistry

Background:

• Biosensor performance is crucial for clinical diagnostics, but signal quality often requires improvement post-analysis.
• Current digital signal processing methods for impedance cytometry may not be optimally selected, limiting accuracy.
• There is a need for a systematic approach to optimize digital filtering for unique impedance cytometry systems.

Purpose of the Study:

• To investigate the impact of various digital filtering parameters on impedance cytometry signal quality.
• To develop a framework for optimizing digital filtering configurations for enhanced biosensor performance.
• To determine the limits of signal processing for noise reduction and improved accuracy in impedance cytometry.

Main Methods:

• Fabrication of a microfluidic impedance cytometer.
• Application of diverse digital filter orders, cutoff frequencies, and filter types to experimental data.
• Systematic evaluation of filtering performance based on signal quality metrics and object counting accuracy.

Main Results:

• Signal quality improved by 6.09 dB for 9 µm polystyrene particles using digital filtering.
• Signal quality improved by 7.50 dB for isolated human neutrophils compared to unfiltered data.
• Demonstrated a method to identify optimal filtering parameters for specific impedance cytometry setups.

Conclusions:

• Digital filtering is a powerful tool for enhancing impedance cytometry signal quality and diagnostic accuracy.
• The presented systematic approach provides a framework for optimizing filtering in diverse biosensor applications.
• Optimized digital filtering can lead to more sensitive and reliable clinical and diagnostic tools.