Preprint Article Version 1 This version not peer reviewed

RESPIRE: A Spectral Kurtosis-based Method to Extract Respiration Rate from Wearable PPG Signals

Version 1 : Received: 16 June 2017 / Approved: 16 June 2017 / Online: 16 June 2017 (10:45:32 CEST)

How to cite: Dubey, H. RESPIRE: A Spectral Kurtosis-based Method to Extract Respiration Rate from Wearable PPG Signals. Preprints 2017, 2017060079 (doi: 10.20944/preprints201706.0079.v1). Dubey, H. RESPIRE: A Spectral Kurtosis-based Method to Extract Respiration Rate from Wearable PPG Signals. Preprints 2017, 2017060079 (doi: 10.20944/preprints201706.0079.v1).

Abstract

In this paper, we present the design of a wearable photoplethysmography (PPG) system, R-band for acquiring the PPG signals. PPG signals are influenced by the respiration or breathing process and hence can be used for estimation of respiration rate. R-Band detects the PPG signal that is routed to a Bluetooth low energy device such as a nearbyplaced smartphone via microprocessor. Further, we developed an algorithm based on Extreme Learning Machine (ELM) regression for the estimation of respiration rate. We proposed spectral kurtosis features that are fused with the state-ofthe-art respiratory-induced amplitude, intensity and frequency variations-based features for the estimation of respiration rate (in units of breaths per minute). In contrast to the neural network (NN), ELM does not require tuning of hidden layer parameter and thus drastically reduces the computational cost as compared to NN trained by the standard backpropagation algorithm. We evaluated the proposed algorithm on Capnobase data available in the public domain.

Subject Areas

wearable; photoplethysmography; spectral kurtosis; extreme learning machine (ELM) regression; respiration rate; cardiovascular diseases (CVD)

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