Event detection

File:Detection pics scipy signal find peaks.svg

Peak or event detection concepts support nanopore signal analysis. Source: Wikimedia Commons, CC BY-SA 4.0.

Event detection is part of the Nanopore Wiki learning path. Event detection identifies real blockade or signal-change events in nanopore recordings and extracts comparable features from them.

Core concepts

• Detection thresholds should be tied to baseline and local noise.
• Event boundaries affect dwell time, area, and shape metrics.
• Weak, overlapping, or multi-level events need careful validation.
• False positives can arise from noise, drift, bubbles, clogging, and analysis settings.

Event detection workflow

Step	Description
1	Annotate experimental phases and sample addition times.
2	Estimate local baseline and noise.
3	Set candidate thresholds and minimum duration rules.
4	Merge, split, or reject events using documented criteria.
5	Export event tables and plots for review.

Typical applications

• Quantifying blockade depth and dwell time.
• Comparing samples or pore variants.
• Building training data for classification models.

Common pitfalls

• Using one threshold for recordings with strong drift.
• Failing to test false positives on blank controls.
• Treating automated output as correct without visual review.

Related pages

• Data analysis
• Noise and filtering
• Single-molecule detection
• Troubleshooting

References

• Signal detection theory
• Signal processing