In the course of the pandemic, the interest in creating more innovative medical devices has run high, as recent years showed how unpredictable the situation in healthcare can be. Never before have we faced such an acute need for masks, ventilators, oxygen cylinders, and other must-have devices to conquer the pandemic.

All this has become a trigger to develop devices that can work autonomously for a long time, without access to the internet or cloud, just on batteries with ultra-low power consumption.

And most importantly, it’s vital that such devices can be made by a wider range of people, even without in-depth technical skills. Perhaps, you’ve heard the story about two engineers from Lombardy who, at the peak of the epidemic in Italy, really saved their city as they began to print plastic valves for ventilators on 3D printers in their office and provided them to hospitals for free. The pandemic unified all, even those who were far from medicine before.

We took the picture here, and you also can read the full story from the same source.

I would also love to share a simple example, so to say — a super user-friendly concept. My goal is to show that any user without data science knowledge at all can make the smallest medical devices smarter (yes, even an 8-bit microcontroller) with the help of tiny ML solutions. Let’s go!


In this tutorial, I’d like to provide a vivid example of how the tiny ML approach can help to predict whether there is an impending arrhythmia or not, by running inferences on the microcontroller, without sending the corresponding sensor data to the cloud.

Let’s learn how to train and embed a compact machine learning model into the 8-bit ATmega2560 microcontroller. I deliberately chose such a memory-constrained and primitive microcontroller to show how simple and smart tiny ML devices can be.

Let’s start by training a model. I took the original dataset from this resource:

This dataset contains the signals of heart rate oscillations. The signals correspond to electrocardiogram (ECG) shapes of heartbeats for the normal case and the cases affected by different arrhythmias and myocardial infarction.

The goal was to detect abnormal heartbeats affected by arrhythmias and myocardial infarction based on electrocardiogram shapes. All the samples were cropped, downsampled, and padded with zeroes, if necessary, to the fixed dimension of 187.

The final element of each row denotes the class to which that example belongs.

Features, target, and target metric:

0…186 — sample description

  • target — class of sample (0 — normal heartbeat, 1 — heartbeat affected by arrhythmias or myocardial infarction)

I combined all the cases into a CSV file and split it into a dataset for training (11, 641 rows), and a file to make an inference (2, 911 rows). Amplitudes of contractions of the heart muscle act as features for training the model. Here you can download preprocessed training and test datasets that we used for model training and prediction on new data.


Step 1: TinyML Model Training

For the AI part of my project, I chose the Neuton Tiny ML platform. Having a special algorithm under the hood, Neuton automatically creates an optimal model in terms of accuracy and compactness. And the best part — the model doesn’t need to be compressed (which is perfect since I needed a very small model that would support the 8-bit architecture).

Next, I uploaded a CSV file and selected the column that should be trained to predict. In my case, this was a column where it was indicated whether there was arrhythmia on the cardiogram or not (1 — yes and 0 — no). Since I needed to embed the model into an 8-bit microcontroller, I selected such a setting in the interface (8-bit support) and started the training. Everything happened automatically.

The model was trained. To assess its quality, I chose the Area Under the Curve....

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