Predictive Algorithms vs. Sensor Noise

The Challenge

Predictive algorithms power CGMs, but sensor noise constantly threatens accuracy.

How Algorithms Handle Noise

Machine Learning Approaches

• Pattern recognition to identify noise vs. signal
• Historical data comparison
• Multi-point trend analysis

Signal Filtering

• Moving averages to smooth readings
• Outlier detection and rejection
• Weighted recent data preference

Sources of Sensor Noise

Biological

• Foreign body response
• Local tissue changes
• Hydration fluctuations

Mechanical

• Sensor movement
• Compression events
• Adhesive issues

Electrical

• Electromagnetic interference
• Temperature variations
• Power fluctuations

Algorithm Trade-offs

Aggressive Smoothing

• Pros: Cleaner, more stable readings
• Cons: Increased lag time, delayed trend detection

Minimal Filtering

• Pros: Faster response, real-time data
• Cons: Noisier readings, more false alerts

Innovation in Noise Handling

Manufacturers continue to improve:

• More sophisticated ML models
• Better sensor chemistry (reduced biological noise)
• Improved signal processing hardware
• Adaptive algorithms that learn user patterns