A Multichannel Intraluminal Impedance Gastroesophageal Reflux Characterization Algorithm Based on Sparse Representation Publisher Pubmed

Summary: Scientists report a new method detects acid reflux with 97% accuracy, improving diagnosis for digestive issues. #DigestiveHealth #MedicalTech

Rasouli A¹ ; Rabbani H¹ ; Kermani S¹ ; Raisi M² ; Soheilipour M² ; Adibi P²
Source: IEEE Journal of Biomedical and Health Informatics Published:2021

Abstract

Gastroesophageal reflux disease (GERD) is a common digestive disorder with troublesome symptoms that has been affected millions of people worldwide. Multichannel Intraluminal Impedance-pH (MII-pH) monitoring is a recently developed technique, which is currently considered as the gold standard for the diagnosis of GERD. In this paper, we address the problem of characterizing gastroesophageal reflux events in MII signals. A GER detection algorithm has been developed based on the sparse representation of local segments. Two dictionaries are trained using the online dictionary learning approach from the distal impedance data of selected patches of GER and no specific patterns intervals. A classifier is then designed based on the ${\ell _{\boldsymbol{p}}}$-norm of dictionary approximations. Next, a preliminary permutation mask is obtained from the classification results of patches, which is then used in post-processing procedure to investigate the exact timings of GERs at all impedance sites. Our algorithm was tested on 33 MII episodes, resulting a sensitivity of 96.97% and a positive predictive value of 94.12%. © 2013 IEEE.