Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)
Richard Coggins, Raymond Wang, Marwan Jabri
In this paper we describe the architecture, implementation and experi(cid:173) mental results for an Intracardiac Electrogram (ICEG) classification and compression chip. The chip processes and vector-quantises 30 dimen(cid:173) sional analogue vectors while consuming a maximum of 2.5 J-tW 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 abnonnal 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.