  Wearable electrocardiograph (ECG) recording and processing systems have been
developed to detect cardiac arrhythmia to help prevent heart attacks.
Conventional wearable systems, however, suffer from high energy consumption at
both circuit and system levels. To overcome the design challenges, this paper
proposes an event-driven compressive ECG recording and neuromorphic processing
system for cardiac arrhythmia detection. The proposed system achieves low power
consumption and high arrhythmia detection accuracy via system level co-design
with spike-based information representation. Event-driven level-crossing ADC
(LC-ADC) is exploited in the recording system, which utilizes the sparsity of
ECG signal to enable compressive recording and save ADC energy during the
silent signal period. Meanwhile, the proposed spiking convolutional neural
network (SCNN) based neuromorphic arrhythmia detection method is inherently
compatible with the spike-based output of LC-ADC, hence realizing accurate
detection and low energy consumption at system level. Simulation results show
that the proposed system with 5-bit LC-ADC achieves 88.6\% reduction of sampled
data points compared with Nyquist sampling in the MIT-BIH dataset, and 93.59\%
arrhythmia detection accuracy with SCNN, demonstrating the compression ability
of LC-ADC and the effectiveness of system level co-design with SCNN.
