“Sense2beat” physiological signals from portable monitoring devices are often contaminated with a large amount of noise, making data interpretation extremely difficult. At the core of a Sense2beat Automated ECG Detection and Prediction AI tool, is set a unique proprietary method of disturbance filtering that does not necessitate any tentative assumptions and estimations of process dynamics. This approach has a great practical importance for the development of automated ECG waveform processing in the context of extremely noisy signal acquired by clinical wearable devices.
Share this project
