CapNet: A Deep Learning-based Framework for Estimation of Capnograph Signal from PPG

Abstract

Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnograph signal has received a lot of attention in recent years as a valuable indicator of respiratory conditions. However, the typical capnograph signal extraction method is quite expensive and also unpleasant to the patient due to the involvement of a nasal cannula. With the advent of wearable sensor technology, there has been significant research on the use of photoplethysmogram (PPG) signals as a less expensive alternative to extract respiratory information. In this paper, we propose CapNet, a novel deep learning-based framework which takes the regular PPG signal as input, and estimates the capnograph signal as output. Training, validation and testing of the proposed networks in CapNet is done using the IEEE TMBE Respiratory Rate Benchmark dataset by utilizing reference capnograph respiration signals. With a lower MSE and higher cross-correlation values, CapNet outperforms two traditional signal processing algorithms and another recently proposed deep neural network, RespNet. The proposed framework expectantly can be implementable and feasible for constant supervising of patients undergoing respiratory ailments.