Insights

What analysis tools do you need for a rapid, data-driven development of continuous glucose monitoring (CGM) sensors?

TTP’s CGM test system can speed up development by providing fluidic automation, sensor output measurement, and data management, while maintaining flexibility and allowing rapid customisation where needed.

In this insight related to the development of a CGM test system, we take a closer look at the workflow and tools that guide a data-driven development process.

We start by finding the test data relevant to our investigation. This could be a set of sensor IDs corresponding to designs being compared, along with date ranges, test protocols, or other attributes.

Interactive GUI-based software

We built an interactive GUI-based software tool to select and plot test data as the first step in the workflow. Sensor outputs (typically current or voltage) are plotted along with test conditions such as glucose concentration and temperature, and overlaid with fluidics information such as valve position and flow rate.

This first-pass view allows a rapid qualitative review of performance. We use existing software, in particular MongoDB Compass, to browse the database and even generate query search terms from natural language inputs.

Custom analysis scripts

In the next step, we develop and run custom analysis scripts to carry out database queries and analysis procedures, generating quantified sensor performance metrics.

A common example is to generate a set of calibration curves to examine sensitivity, linearity, drift and response times over the sensor lifetime.

We can also quantify sensor noise and interfering substance responses.

Using automated analysis, we can compare performance metrics across statistically significant numbers of sensors, revealing subtle features and allowing us to estimate the performance and yield expected from larger production batches.

Virtual experiments

A powerful feature of the database is the ease with which we can carry out virtual experiments: when a new question arises, the data is available to query and provide evidence. For example, what was the impact of a change, which might be deliberate or unintentional, in the manufacturing process?

As a development progresses, the scalability of database storage becomes essential, with data being generated and analysed on multiple sites.

The cloud-based architecture allows expansion and a smooth transition from R&D to production.

Download the white paper

To find out how our system can accelerate the development of your CGM, download the white paper. Download white paper

Automated biosensor testing for the development of continuous health monitors

To find out how our system can accelerate the development of your CGM (continuous glucose monitor), get in touch.

Talk to us about your next project

Talk to us about your next project

Whether you would like to discuss a project or would like to learn more about our work, get in touch through the form below.

Last Updated
January 23, 2025

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