Key result
A wearable PPG system and ELM regression algorithm using spectral kurtosis features was developed to estimate respiration rate, reducing computational cost compared to neural networks.
A novel wearable PPG system and ELM-based algorithm using spectral kurtosis features can estimate respiration rate with reduced computational cost compared to standard neural networks.
May enable efficient wearable respiration monitoring; leaves open prospective clinical validation.
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.
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Hari Dubey (2017) studied this question. R-band wearable PPG system and ELM regression algorithm vs. Neural network (NN) was evaluated on Estimation of respiration rate. A wearable PPG system and ELM regression algorithm using spectral kurtosis features was developed to estimate respiration rate, reducing computational cost compared to neural networks.
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