Circuits, Systems, and Signal Processing (2020) 39:5656–5680 https://doi.org/10.1007/s00034-020-01427-5 Spike Detection Based on the Adaptive Time–Frequency Analysis Mokhtar Mohammadi 1 · Nabeel Ali Khan 2 · Hamid Hassanpour 3 · Adil Hussien Mohammed 4 Received: 26 November 2018 / Revised: 12 April 2020 / Accepted: 15 April 2020 / Published online: 12 May 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020 Abstract This paper presents a novel spike detection algorithm in nonstationary signals using a time–frequency (t – f ) approach. The proposed algorithm exploits the direction of signal energy in the t – f domain to detect spikes in the presence of high-frequency non- stationary signals even at low signal-to-noise ratio. The performance of the proposed approach is evaluated using synthetic nonstationary signals, synthesized signals mim- icking electroencephalogram (EEG) signals, manually selected segments of speech signals, and manually selected segments of real EEG signals. The statistical measures, such as hit rate and precision, are used to demonstrate that the proposed algorithm performs better than other widely used algorithms, such as the smoothed nonlinear energy detector. Keywords Spike detection · Time–frequency analysis · Directional filtering · Real-life signals B Mokhtar Mohammadi mokhtar.mohammadi1@gmail.com Nabeel Ali Khan nabeel.alikhan@gmail.com Hamid Hassanpour H_hassanpour@yahoo.com Adil Hussien Mohammed adil.mohammed@cihanuniversity.edu.iq 1 Department of Information Technology, University of Human Development, Sulaymaniyah, Iraq 2 Electrical Engineering, Foundation University, Islamabad, Pakistan 3 Department of Computer Engineering and Information Technology, Shahrood University of Technology, Shahrood, Iran 4 Department of Communication and Computer Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq Content courtesy of Springer Nature, terms of use apply. Rights reserved.