The power of CNN & DL in arrhythmia prediction
Detection & Validation of 21 categorizations of arrhythmias
Accuracy score of 98% and a Precision score of 95% & growing
Affirmed by MD American board-certified panel of Physicians
Trained over a million data sets that are manually annotated by the American CCT/CRAT techs.
Built in fusion with Computer vision and Artificial neural network to detect arrhythmias
Works across a diversified set of digital ECG data sets; such as Holters, MCT, Events & 12 lead
Runs on secure HIPAA cloud infrastructure, enabling easy integration and customisation for IDTF’s & Wearables
The conception of machine learning became engrossing in almost every sector as the healthcare industry stumbles its application as well. We are in devoir of an automated edifice that would administer the detection of assorted arrhythmias from the captured ECGs at the same time be mindful of the signal quality and analogous artifacts.
The approach we have adopted here is the implementation of Deep Learning for arrhythmia detection through a fresh approach utilizing discriminative visualization to enhance transparency and the interpretability of the Deep Neutral Network in an effort to imitate “Human eye interpretation”.
1. Signal regularization: The ECG data from discrepant scales pertinent to their respective hardware are standardized to mV signal
2. Befitting lead selection:Felicitous data channel is designated just in case channel information is proven to be enigmatic or inaccessible
3. ECG strip production and dynamic cropping: Transformation of raw data for One-Dimensional to Two-Dimensional raw data transformation for classification.
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