Abstract
The Heart Rate Variability (HRV) is based on the analysis of the R-peak to R-peak intervals (RR-intervals) of the ECG signal in the time and/or frequency domains. Doctors and psychologists are increasingly recognizing the importance of HRV; in fact, a number of studies have demonstrated that patients with anxiety, phobias and post-traumatic stress disorder consistently show lower HRV, even when not exposed to a trauma related prompt. Importantly, this relationship existed independently of age, gender, trait anxiety, cardio-respiratory fitness, heart rate, blood pressure and respiration rate. In this paper, we present a toolkit based on body sensor networks (BSN) for the time-domain HRV analysis, namely SPINE-HRV (Signal Processing In Node Environment-HRV). The SPINE-HRV is composed of a wearable heart activity monitoring system to continuously acquire the RR-intervals, and a processing application developed using the SPINE framework. The developed system consists of a wireless chest band, a wireless wearable sensor node and a base station. The RR-intervals are processed using the SPINE framework at the base station side through a time-domain analysis of HRV. The analysis provides seven common parameters known in medical literature to help cardiologists in the diagnosis related to several heart diseases. In particular, SPINE-HRV is applied for stress detection of people during activities in their everyday life. Experimentations carried out by monitoring subjects in specific activities have shown the effectiveness of SPINE-HRV in detecting stress.
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Andreoli, A., Gravina, R., Giannantonio, R., Pierleoni, P., Fortino, G. (2010). SPINE-HRV: A BSN-Based Toolkit for Heart Rate Variability Analysis in the Time-Domain. In: Lay-Ekuakille, A., Mukhopadhyay, S.C. (eds) Wearable and Autonomous Biomedical Devices and Systems for Smart Environment. Lecture Notes in Electrical Engineering, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15687-8_19
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DOI: https://doi.org/10.1007/978-3-642-15687-8_19
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