ResCSRF: Algorithm to Automatically Extract Cheyne Stokes Respiration Features from Respiratory Sign

Abstract

Cheyne-Stokes respiration (CSR) related features are significantly associated with cardiac dysfunction. Scoring of these features is labor-intensive and time-consuming. To automate the scoring process, an algorithm (ResCSRF) has been developed to extract these features from nocturnal measurement of respiratory signals. Methods: ResCSRF takes 4 signals (nasal flow, thorax, abdomen and finger oxygen saturation) as input. It first detects CSR cycles and then calculates the respiratory features [cyclelength (CL), lung-to-periphery circulation time (LPCT) and time to peak flow (TTPF)]. It outputs nightly statistics (mean, median, standard deviation (StD) and percentiles) of these features. It was developed and blindly tested on a group of 49 chronic heart failure (CHF) patients undergoing overnight in-home unattended respiratory polygraphy recordings.

Existing System

Volume Overload Decompensation.

Proposed System

This paper presented an automated algorithm (ResCSRF) to automatically detect CSR-related features from multiple respiratory channels. Nightly statistics generated by ResCSRF from these features showed excellent correlation with expert sleep scoring. The use of an algorithm like ResCSRF could significantly reduce the computational cost of manually scoring individual recordings. The development and implementation of such an algorithm into a remote monitoring device is a positive step towards developing technologies capable of continuously monitoring cardiac function.

Architecture

BLOCK DIAGRAM