Development of an embedded device for real-time detection of atrial fibrillation and atrial flutter in single-channel ECG, using optimised classification based on a large training corpus (hardware)

Atrial fibrillation (A-Fib) and atrial flutter are widespread medical conditions of the heart. Loss of coordination between atrial and ventricular activities affects the smooth circulation of blood, causing an increased risk of blood clotting, which in turn elevates risk of pulmonary embolisms and cerebral infarction. However, the condition is not necessarily noticed by patients, for example through palpitations or tachycardia.

A custom embedded device developed for this master's thesis helps people to evaluate whether they are experiencing atrial fibrillation at a specific moment. The device measures single-channel ECG for less than one minute and instantly classifies it as either A-Fib, normal sinus rhythm (NSR) or undecided (low measurement quality or atypical ECG).

Building on an earlier proof of concept project work by the author, this thesis presents a fully integrated, custom device, using an advanced classification algorithm trained on thousands of short, annotated ECG fragments from the PTB-XL corpus. The algorithm uses morphological analysis of the averaged ECG shape, properties of the R/R interval distribution and spectral analysis of the ECG to create a feature vector used for classification. Analysis and raw ECG data can be transferred via Bluetooth at the user's discretion.

This Zenodo object contains the EAGLE schematics and PCB layout for the embedded device developed for the thesis, including component definition libraries. Of course, component definitions provided by component vendors or by EAGLE itself retain their respective licenses as stated in the respective library files. Both versions of the design, ecghelper2 (built) and ecghelper3 (proposed) are hereby made available as open hardware under the Creative Commons Attribution Share Alike 4.0 International license. See the included license text files for more information. The first ecghelper hardware were off-the-shelf modules, discussed in the earlier project report by the same author as this thesis.

The corresponding ESP32 Arduino and Linux software is available under the FreeBSD license in a separate Zenodo object. While the core algorithm should be easy to port to other ESP32-based systems such as the HeartyPatch, the hardware presented here will provide significantly better user interface and standby battery life for stand-alone use.