[home] [Personal Program] [Help]

---

Session: Poster session II
Session starts: Thursday 24 January, 16:00

---

Samprajani Rout (TU Delft)
Samaneh Babayan Mashhadi (TU Delft)
Wouter A. Serdijn (TU Delft)

Abstract:
Atrial electrograms (AEGs) are the signals recorded on the surface of the heart and can be used to study the signal propagation in the atria with an aim to understand the development and progression of atrial fibrillation, which is a type of cardiac arrhythmia. AEGs are acquired using a high resolution electrode array which pose strict constraints on the power and area consumption of the data acquisition system. For a single channel, a dedicated analog-to-digital converter (ADC) is used. However, this approach becomes hardware intensive as the number of channels increase. Multi-channels closely spaced together acquire AEGs that are slowly varying in time, and also share similarities in terms of amplitudes values. Digitizing the signals acquired from every electrode in a multi-channel acquisition system generates a large amount of redundant data. While researchers have attempted to apply various signal specific approaches [1,2,3] such as adaptive resolution ADC, adaptive sampling rate based ADC, a signal specific successive approximation register (SAR) ADC which depends on the difference between the two samples which differ by a few LSBs, a non-linear signal specific SAR ADC, they suffer from being incomplete in the signal data representation and do not account for the redundancies in the spatial domain. In this work, we aim to exploit the redundancies in space based on the gradient or the difference in the amplitudes of the signals recorded between adjacent electrodes. Firstly, signal characteristics of the AEGs are extracted from the available sample data. The difference in amplitude between different channels at a given time instant is calculated and is averaged over many time instants. From a probability density function curve, the mean difference in amplitude and the variance over the entire array is obtained. Based on the values, we define a threshold to activate a high resolution or a low resolution operation mode of the ADC. We aim to develop a signal specific algorithm and circuit architecture using a SAR ADC, that accounts for the changes in the spatial domain to arrive at energy and area efficient multi-channel data acquisition design for AEGs. References: S. O’Driscoll, K. V. Shenoy, and T. H. Meng, “Adaptive resolution ADC array for an implantable neural sensor,” IEEE Transactions on Biomedical Circuits and Systems, vol. 5, no. 2, pp. 120–130, April 2011. E. Rahiminejad, M. Saberi, and R. Lotfi, “A power-efficient signal-specific ADC for sensor-interface applications,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 9, pp. 1032–1036, Sept 2017. M. Judy, A. M. Sodagar, R. Lotfi, and M. Sawan, “Nonlinear signal-specific ADC for efficient neural recording in brain-machine interfaces,” IEEE Transactions on Biomedical Circuits and Systems, vol. 8, no. 3, pp. 371–381, June 2014.