This thesis presents novel research for real time heart sound detection, heart rate extraction and acoustic respiratory rate extraction algorithms. This was done based on signals obtained with a novel in-house developed wireless acoustic breathing and heart rate monitor. The core aim of this work is to enable additional features with respect to which physiological parameters can be measured by acoustic means with the above- mentioned sensor in the hospital bed environment. The performance evaluation was done with data collected from clinical trials carried out at Queens Square hospital - UCL Institute of Neurology - in London, UK. The respiratory rate extraction algorithm presented achieved a value difference bias of 0.07 and standard deviation of 1.55 breaths per minute with respect to the counted respiratory oscillations on the polysomnography device signals of the flow sensor as well as the abdominal and thoracic band evaluated on more than 21 hours of data from 13 different subjects during sleep. Similarly the novel heart rate extraction algorithm for processing the acoustic signals achieves a performance of 90.20% and 90.26% agreement with respect to heart rate value provided by the Konica-Minolta and SomnoMedics devices respec- tively evaluated on more than 57 hours of data acquired from 13 different subjects during sleep in the clinical trials. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. Overall, these results represent: a clear proof of concept for heart rate monitoring with the in-house developed wireless acoustic monitoring system; the addition of two very important monitoring capabilities to the wireless acoustic monitoring system; as well as significant contributions to the field of signal processing for both acoustic respiratory rate and heart rate monitoring.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:693861 |
| Date | January 2010 |
| Creators | Aguilar Pelaez, Eduardo |
| Contributors | Rodriguez Villegas, Esther |
| Publisher | Imperial College London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10044/1/39384 |
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