Demonstration of subclinical autonomic dysfunction following severe traumatic brain injury using serial heart rate variability monitoring

Deepika, Akhil; Baruah, Satyakam; Shukla, Dhaval; Sathyaprabha, Talakad N.; Devi, Bhagavatula Indira

doi:10.1016/j.autneu.2015.07.088

Cited by 1 publication

(1 citation statement)

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“…the continuous monitoring of sportsmen's functional state in the conditions of natural activity (Runova et al, 2012). Other recent researches underlines the potentials of using this tool in outpatient settings (Deepika et al, 2015), (Angarita et al, 2015), (Bakhchina et al, 2014), emergency department triage (Bianchi et al, 2013), or as a support tool in the prevention of abnormal events, e.g. fall detection (Hemalatha and Vaidehi, 2013), (Sannino et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Smart Monitoring of User’s Health at Home: Performance Evaluation and Signal Processing of a Wearable Sensor for the Measurement of Heart Rate and Breathing Rate

Casaccia

Pietroni

Calvaresi

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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Nowadays, the monitoring of users' health status is possible by means of smart sensing devices at low-cost and with high measuring capabilities. Wearable devices are able to acquire multiple physiological and physical waveforms and are equipped with on-board algorithms to process these signals and extract the required quantities. However, the performance of such processing techniques should be evaluated and compared to different approaches, e.g. processing of the raw waveforms acquired. In this paper, the authors have performed a metrological characterization of a commercial wearable monitoring device for the continuous acquisition of physiological quantities (e.g. Heart Rate-HR and Breathing Rate-BR) and raw waveforms (e.g. Electrocardiogram-ECG). The aim of this work is to compare the performance of the onboard processing algorithms for the calculation of HR and BR with a novel approach applied to the raw signals. Results show that the HR values provided by the device are accurate enough (±2.1 and ±2.8 bpm in static and dynamic tests), without the need of additional processing. On the contrary, the implementation of the dedicated processing technique for breathing waveform allows to compute accurate BR values (±2.1 bpm with respect to standard equipment).

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