Real-time monitoring and continuous monitoring are different goals. The use case for something like this is probably a wearer who is experiencing heart palpitations, and has the time and clarity of thought to actually find the correct app on their device and start recording.

Continuous monitoring is extremely challenging still because ECG data needs to be sampled at a relatively high frequency (~200 Hz) to accurately identify the QRS complex in the waveform. That uses a lot of power, and the batteries we have still aren't good enough to support those types of demands. 200 * 60 * 60 = 720,000 samples per hour to collect and process.

It's possible that algorithmic approaches may be able to reduce the sampling frequency required. Power-constraints were the main issue when I studied this topic 10 years ago during my master's degree. I had looked into non-frequency domain techniques (such as empirical mode analysis/Hilbert-Huang transform) as a possible way to reduce sampling frequency and thus power consumption.

https://github.com/AshwinSundar/Empirical-Mode-Decomposition...