Abstract: Gastroesophageal reflux disease (GERD) is a common cause of chronic cough. For the diagnosis and treatment of GERD, it is desirable to quantify the temporal correlation between cough and reflux events. Cough episodes can be identified on esophageal manometric recordings as short-duration, rapid pressure rises. The present study aims at facilitating the detection of coughs by proposing an algorithm for the classification of cough events using manometric recordings. The algorithm detects cough episodes based on digital filtering, slope and amplitude analysis, and duration of the event. The algorithm has been tested on in vivo data acquired using a single-channel intra-esophageal manometric probe that comprises a miniature white-light interferometric fiber optic pressure sensor. Experimental results demonstrate the feasibility of using the proposed algorithm for identifying cough episodes based on real-time recordings using a single channel pressure catheter. The presented work can be integrated with commercial reflux pH/impedance probes to facilitate simultaneous 24-hour ambulatory monitoring of cough and reflux events, with the ultimate goal of quantifying the temporal correlation between the two types of events.

Keywords: Biomedical signal processing, cough detection, gastroesophageal reflux disease.

ACM Classification Keywords: I.5.4 Pattern Recognition: Applications – Signal processing; J.3 Life and Medical Sciences

Link:

MANOMETRY-BASED COUGH IDENTIFICATION ALGORITHM

Jennifer A. Hogan, Martin P. Mintchev

http://www.foibg.com/ijita/vol14/ijita14-2-p03.pdf