HMM-based Supervised Machine Learning Framework for the Detection of fECG R-R Peak Locations

ConclusionTwo primary challenges in these methods are finding the right reference threshold for the normalization of the extracted fECG signal during the initial trials and limitation of discrete frame work of HMM signal (converted from continuous time) which only offers a countable number of levels in observations. By feeding the posterior probabilities, obtained from SVM, into HMM, as emission probabilities, can further improve the accuracy of fQRS location detection.Graphical abstractBlock diagram of different stages of fECG QRS location detection using HMM.
Source: IRBM - Category: Biomedical Engineering Source Type: research