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Title: Biomagnetic signal analysis
Author: Mishin, A.
Awarding Body: University of Wales Swansea
Current Institution: Swansea University
Date of Award: 2003
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Most of this thesis is an account of the effort to develop new methods for biomagnetic data analysis. Variations of the heart rate reflect the neural heart control mechanisms which are performed via the electrical modulation of the sinoatrial node by the autonomic nervous system. This modulation involves the interaction of several physiological mechanisms that operate on differing time scales. Using SQUID (superconducting quantum interference device) instrumentation, the fetal cardiogram can be measured with great accuracy and a high temporal resolution, thereby providing the opportunity to assess the neural function in the fetus non-invasively by analysing heart rate variability (HRV). However, a quantitative analysis of HRV requires several other physiological parameters such as blood pressure, respiration etc. to be analysed simultaneously with HRV. These parameters are obviously inaccessible in the fetus although they are routinely recorded in premature neonates treated in the intensive care units. Using a time domain correlation method, the behaviour of different HRV components was quantitatively studied for both fetuses and premature neonates and a number of consistent features were found. The correlation between neonatal HRV, respiration and arterial blood pressure was studied with the ultimate goal of constructing a numerical model of HRV. It was also observed that different types of ventilation equipment used in neonatal intensive care cause different patterns of respiration/HRV correlation, which may be indicative of the efficacy of the ventilator. Investigation of the spontaneous activity of the human brain and in particular alpha rhythm is another area where SQUID-based biomagnetic techniques can make an important contribution. In the final chapter of this work the multichannel alpha magnetoencephalogram (MEG) is considered as a sequences of MEG maps. A neural-net based algorithm for segmentation of MEG records into words is presented. Using this method three recurring words were found in an eight-second magnetoecephalogram. This could be of value for active testing of the functional role of the cortex in neurological experiments.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available