Abstract
Ballistocardiography (BCG), a non-invasive technique for measuring micro-body vibrations arising from cardiac contractions. It also contains motion arising from breathing, snoring and body movements. Long-term acquisition of respiratory signal finds relevance in various applications such as sleep analysis as well as monitoring of respiratory disorders. Current methods (such as nasal thermistor and Respiratory Inductance Plethysmography) are costly, inconvenient and require technical expertise to setup and analyse. In this paper we assess how BCG based contact-free methods can allow for an accurate, cost-effective and convenient long-term monitoring from the ease of home environment. We propose a novel algorithm to detect breathing cycles from BCG signal, achieving an accuracy of ~95% in determining respiration rate for 30 s epochs with a detection rate of 72.8% compared to current methods. Long-term continuous monitoring of respiratory signals with a high accuracy will allow for detection of abnormalities like respiratory distress and apnea/hypopnea episodes.
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Acknowledgement
All the validation PSG studies were conducted at the Human Sleep Research Laboratory of Department of Neurophysiology at NIMHANS. We would like to thank Dr. Bindu M Kutty and her team at the lab for help they provided in conducting these studies and acquiring validation data. This work is partly funded under the Biotechnology Innovation Grant Scheme of the Government of India.
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Saran, V., Kumar, G., Parchani, G. (2019). Unsupervised Extraction of Respiration Cycles Through Ballistocardiography. In: Luhach, A., Jat, D., Hawari, K., Gao, XZ., Lingras, P. (eds) Advanced Informatics for Computing Research. ICAICR 2019. Communications in Computer and Information Science, vol 1075. Springer, Singapore. https://doi.org/10.1007/978-981-15-0108-1_14
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DOI: https://doi.org/10.1007/978-981-15-0108-1_14
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