Prediction of time series using ARMA models in an energy-efficient body area network

This paper investigates the tradeoff between accuracy and complexity cost to predict electrocardiogram values using auto-regressive moving average (ARMA) models in a fully functional body area network (BAN) platform. The proposed BAN platform captures, processes, and wirelessly transmits six-degrees-of-freedom inertial and electrocardiogram data in a wearable, non-invasive form factor. To reduce the number of packets sent, ARMA models are used to predict electrocardiogram (ECG) values. However, in the context of wearable devices, where the computing and memory capabilities are limited, the prediction model should be both accurate and lightweight. To this end, the goodness of the ARMA parameters is quantified considering ECG signal, we compute Akaike Information Criterion (AIC) on more than 900000 ECG measures. Finally, a tradeoff is given accordingly to the hardware constraints.