Smart EKG's model works by pooling information from all 10 leads in order to make well-informed conclusions about what’s happening in the heart. The signals from each of the ten leads are individually run through several convolutional neural network blocks, which include pooling and dropout laters, to filter out noisy data. The results from all of these blocks are then pooled together and then run through a fully connected artificial neural network block. The output of the final block is a signal number that represents the probability of atrial fibrillation occurring at that moment. This same architecture is then used for STEMI detection.
Our Machine Learning works very fast to provide on the spot diagnoses
Our Machine Learning has a very high accuracy and is very efficient
SmartEKG employs advanced level CNN's and math to make the machine learning model
Our team had years of coding experience to make the machine learning model perfect
A product that revolutionizes the portable EKG by using Machine Learning to detect heart abnormalities and predict heart attacks comfortable and precisely