Key result
Spiking bandpass wavelets match continuous wavelet transforms for ECG reconstruction while mapping to neuromorphic hardware.
Why the study?
Spike-based encodings are sparse and energy-efficient but have largely been formulated probabilistically, disconnected from most signal processing literature.
Spiking bandpass wavelets can effectively encode and decode temporal signals like ECGs with reconstruction accuracy comparable to continuous wavelet transforms, while mapping directly to neuromorphic hardware.
Facilitates sparse ECG spike encoding with error bounds; leaves open validation in clinical neuromorphic pipelines.
Spike-based encodings are sparse and energy-efficient, but have largely been formulated probabilistically, disconnected from most signal processing literature. We recast spike encoders as time-causal wavelet frames with quantitative bandwidths and reconstruction error bounds. The proposed wavelets preserve the sparsity and locality of spiking representations, with reconstruction up to spike quantization and time discretization. We demonstrate reconstruction on ECG and audio datasets, achieving a normalized RMSE comparable to continuous wavelet transforms. The spiking wavelets map directly to neuromorphic hardware.
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Pedersen et al. (2026) studied this question. Spiking Bandpass Wavelets vs. Continuous wavelet transforms was evaluated on Normalized root mean square error (nRMSE) for signal reconstruction. Spiking bandpass wavelets achieved a normalized RMSE comparable to continuous wavelet transforms on ECG and audio datasets while preserving sparsity and mapping directly to neuromorphic hardware.
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