  Accurate detection of pathological conditions in human subjects can be
achieved through off-line analysis of recorded biological signals such as
electrocardiograms (ECGs). However, human diagnosis is time-consuming and
expensive, as it requires the time of medical professionals. This is especially
inefficient when indicative patterns in the biological signals are infrequent.
Moreover, patients with suspected pathologies are often monitored for extended
periods, requiring the storage and examination of large amounts of
non-pathological data, and entailing a difficult visual search task for
diagnosing professionals.
  In this work we propose a compact and sub-mW low power neural processing
system that can be used to perform on-line and real-time preliminary diagnosis
of pathological conditions, to raise warnings for the existence of possible
pathological conditions, or to trigger an off-line data recording system for
further analysis by a medical professional. We apply the system to real-time
classification of ECG data for distinguishing between healthy heartbeats and
pathological rhythms.
  Multi-channel analog ECG traces are encoded as asynchronous streams of binary
events and processed using a spiking recurrent neural network operated in a
reservoir computing paradigm. An event-driven neuron output layer is then
trained to recognize one of several pathologies. Finally, the filtered activity
of this output layer is used to generate a binary trigger signal indicating the
presence or absence of a pathological pattern.
  We validate the approach proposed using a Dynamic Neuromorphic Asynchronous
Processor (DYNAP) chip, implemented using a standard 180 nm CMOS VLSI process,
and present experimental results measured from the chip.
