# Wavelets for CGM Denoising

Nicolas Magdelaine, Lucy Chaillous, Isabelle Guilhem, Jean-Yves Poirier,

Michel Krempf, Fournier-Guilloux Anne-Laure, Claude H. Moog

► To cite this version:

Nicolas Magdelaine, Lucy Chaillous, Isabelle Guilhem, Jean-Yves Poirier, Michel Krempf, et al. Wavelets for CGM Denoising. ATTD 2016; 9th International Conference on Advanced Technologies & Treatments of Diabetes, Feb 2016, Milano, Italy. hal-01264662

HAL Id: hal-01264662

https://hal.science/hal-01264662v1

Submitted on 28 Nov 2024

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Distributed under a Creative Commons Attribution 4.0 International License
# Wavelet for CGM Denoising

N. Magdelaine¹, L. Chaillous², I. Guilhem³, J.-Y. Poirier³, M. Krempf², A.-L. Fournier-Guilloux², C. Moog¹

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1. IRCCyN, Nantes

2. CHU Nantes, Institut du Thorax, Diabetology Department

3. CHU Rennes, Diabetes and Metabolism

## Background

Continuous Glucose Monitoring devices

* allow sensor augmented pump therapy for type-1 diabetic patients

* are useful for physicians to analyze nychthemeral glycemic profile and glucose variability.

Common sources of error in CGM readings are:

* Deviations

* Spikes

* Loss of sensitivity

<figure><img src="image_5.png" /></figure>

## Methods

* Typical CGM noises have been used [1].

* The noise was added to a simulated BG signal.

The objective is to maximize the Signal to Noise Ratio (SNR)

* The Haar wavelet basis has been selected.

* At each scale *k* of decomposition, the threshold level $t_k^* | t_{k-1}^*$ of the *detail coefficients* $d_k$ is selected in order

* Decomposition level *k*\* and thresholds $t_k^*$ are computed.

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Threshold [%.max($d_k$)]

Decomposition Level (k)

## Results

The optimal threshold levels $t_k^*$ were used to denoise CGM data from 16 type-1 diabetic patients off-line.

* ✓ Deviations, loss of sensitivity and spikes were removed

* ✓ likely pikes and nadirs were not damped

<figure><img src="image_7.png" /></figure>

## Conclusion

* An off-line wavelet algorithm has been developed.

* It can provide a pre-processing tool for model fitting.

* It allows an accurate reading of CGM to facilitate analysis by the physicians.

## References

[1] Facchinetti et al., "Modeling the Glucose Sensor Error", IEEE TBME, 2014; 61(3): 620-629