Amplifier front-end design in dry-electrode electrocardiography

THESIS 9178This thesis presents an analytical approach to some key issues related to the design of biopotential amplifiers for use in long-term ambulatory recording of the electrocardiogram (ECG) employing pasteless or dry electrodes. The two principal problems of low-frequency distortion and immunity to common-mode interfering signals are analysed in detail. There is also an algorithm for the identification of the model parameters of a group of systems simulated as a double-time-constant network in an attempt to characterise the interface composed of the skin and the sensing electrodes. In the case of low-frequency distortion, new input impedance specifications for dry-electrode electrocardiography are derived from the analysis of performance requirements issued in international standards and recommendations. This is necessary if the low-frequency components in the ECG that contain valuable clinical information about the condition of the cardiovascular system are to be faithfully reproduced. The factors defining the ability of the recording system to effectively reject external unwanted voltages are then determined to allow the suitability of a small number of amplifier circuit configurations to be assessed. The design of new instrumentation amplifiers is finally outlined and their performance evaluated in a worst-case scenario

A quantitative skin impedance test to diagnose spinal cord injury

The purpose of this study was to develop a quantitative skin impedance test that could be used to diagnose spinal cord injury (SCI) if any, especially in unconscious and/or non-cooperative SCI patients. To achieve this goal, initially skin impedance of the sensory key points of the dermatomes (between C3 and S1 bilaterally) was measured in 15 traumatic SCI patients (13 paraplegics and 2 tetraplegics) and 15 control subjects. In order to classify impedance values and to observe whether there would be a significant difference between patient and subject impedances, an artificial neural network (ANN) with back-propagation algorithm was employed. Validation results of the ANN showed promising performance. It could classify traumatic SCI patients with a success rate of 73%. By assessing the experimental protocols and the validation results, the proposed method seemed to be a simple, objective, quantitative, non-invasive and non-expensive way of assessing SCI in such patients