Adaptive-SWTTEO: enhancing an established energy-based spike detection algorithm to address signal non-stationarity...

Detecting spikes from extracellular neural recordings is essential for investigating neuronal activity. Over the past decade, numerous studies have focused on developing optimal algorithms for this task, evaluating their performance on both synthetic and real data. Despite significant progress, the problem remains an active area of research. In this study, we present a novel Matlab-based spike detection algorithm, Adaptive-SWTTEO, inspired by an existing method that has demonstrated superior performance compared to other approaches. The new algorithm's performance was evaluated against the original method using a custom synthetic dataset that included both non-bursting and bursting neural activity. Results showed that the Adaptive-SWTTEO outperformed the original algorithm in terms of detection efficiency, reliability and speed.