Signal-to-peak-interference ratio (SPIR) optimal filters are template matching filters with peak interference suppression properties. Such max-SPIR filters are used in multi-pattern recognition problems, such as neural spike sorting in microelectrode array probes, where cellular action potentials need to be detected and clustered according to their firing neuron cells. In high-density probes with hundreds of channels, such max-SPIR filter banks can require unacceptable high computational resources, in particular for applications with real-time demands and/or on-probe spike sorting. In this paper, we present a computationally attractive substitute for max-SPIR filters by recursively computed Autonomous Linear State Space Model (ALSSM) filters. In our approach, we approximate the impulse response of max-SPIR filters by low order ALSSMs and perform the signal convolution in the new, low-dimensional ALSSM vector space. We demonstrate our method on real neural recordings from high-density probes and show only minimal loss in detection quality while the computational complexity drops by up to a factor 10.

Bioelectrical signals are often pulse-shaped with superimposed interference signals. In this context, accurate identification of features such as pulse onsets, peaks, amplitudes, and duration is a frequent problem. In this paper, we present a versatile method of rather low computational complexity to robustly identify such features in real-world signals. For that, we take use of two straight-line models fit to the observations by minimizing a quadratic cost term, and then identify desired features by tweaked likelihood measures. To demonstrate the idea and facilitate access to the method, we provide examples from the field of cardiology.