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Abstract:
In this paper we describe the architecture, implementation
and experimental results for an Intracardiac Electrogram (ICEG)
classification and compression chip. The chip processes and
vector-quantises 30 dimensional analogue vectors while consuming a
maximum of 2.5 W power for a heart rate of 60 beats per minute (1
vector per second) from a 3.3 V supply. This represents a
significant advance on previous work which achieved ultra low power
supervised morphology classification since the template matching
scheme used in this chip enables unsupervised blind classification
of abnormal rhythms and the computational support for low bit rate
data compression. The adaptive template matching scheme used is
tolerant to amplitude variations, and inter- and intra-sample time
shifts.
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