4 Current results

4.1 Regime change detection

The current status on regime change detection pipeline is the implementation of the resampling strategies and evaluation in order to start the parameter tuning. An example of the current implementation is shown on Fig. 4.1.

Regime change detection example. The graph on top shows the ECG streaming; the blue line marks the ten seconds before the original alarm was fired; the red line marks the time constraint of 1250; the dark red line marks the limit for taking a decision in this case of Asystole the blue horizontal line represents the size of the sliding window. The graph on the middle shows the Arc counts as seen by the algorithm (with the corrected distribution); the red line marks the current minimum value and its index; the blue horizontal line shows the minimum value seen until then. The graph on the bottom shows the computed Arc counts (raw) and the red line is the theoretical distribution used for correction.

FIGURE 4.1: Regime change detection example. The graph on top shows the ECG streaming; the blue line marks the ten seconds before the original alarm was fired; the red line marks the time constraint of 1250; the dark red line marks the limit for taking a decision in this case of Asystole the blue horizontal line represents the size of the sliding window. The graph on the middle shows the Arc counts as seen by the algorithm (with the corrected distribution); the red line marks the current minimum value and its index; the blue horizontal line shows the minimum value seen until then. The graph on the bottom shows the computed Arc counts (raw) and the red line is the theoretical distribution used for correction.

4.2 Classification

The current status on the classification pipeline is the implementation of the shapelets extraction using the Contrast Profile.

An example of candidates for ventricular tachycardia is presented on Fig. 4.2.

FIGURE 4.2: Shapelet candidates for Ventricular Tachycardia.

4.3 Feasibility trial

A side-project called “false.alarm.io” has been derived from this work (an unfortunate mix of “false.alarm” and “PlatformIO,”⁶⁵ the IDE chosen to interface the panoply of embedded systems we can experiment with). The current results of this side-project are very enlightening and show that the final algorithm can indeed be used in small hardware. Further data will be available in the future.

A brief mentioning, linking back to the objectives of this work, an initial trial was done using an ESP32 MCU (Fig. 4.3) in order to be sure if such small device can handle the task.

FIGURE 4.3: ESP32 MCU

Current results show that such device has enough computation power to handle the task in real-time using just one of its two microprocessors. The main limitation seen in advance is the on-chip SRAM that must be well managed.