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In recent years, the development of microelectrode arrays and multichannel recordings has provided opportunities for high‐precision detection in signal processing. The study of neuronal frontal potentials has been rapidly emerging as an important component in brain‐computer interface and neuroscience research. Neuronal spike detection provides a basis for neuronal discharge analysis and nucleus cluster identification; its accuracy depends on feature extraction and classification, which affect neuronal decoding analysis. However, improving the detection accuracy of spike potentials in highly noisy signals remains a problem. IThe authors propose a heuristic adaptive threshold spike‐detection algorithm that removes noise and reduces the phase shift using a zero‐phase Butterworth infinite impulse response filter. Next, heuristic thresholding is applied to obtain spike points, remove repetitions, and achieve robust spike detection. The proposed algorithm achieved an average accuracy of 95.40% using extracellular spiked datasets and effectively detected spikes. [ABSTRACT FROM AUTHOR]

We propose a rule‐based method of spike detection and suppression method. This method is an extension of the jump detector that was proposed by the second author, M. Pawlak and A. Steland. Its elementary properties are established, and the example of application for a laser power control in a 3‐dimensional additive manufacturing process is discussed. [ABSTRACT FROM AUTHOR]

This paper presents a theoretical study and the resulting architecture of an analogue-based standard deviation (SD) estimator, typically used in biomedical spike detectors to assess the noise level of bioelectrical recordings online. This well-known circuit generated a significant inaccuracy, so the aim was to increase the efficiency of spike detection through proper calculation of the noise SD, by developing a rigorous, original theoretical study. The approach consisted of establishing behavioural models for the SD estimation circuit to obtain a transfer function and compare different controllers. This modelling approach also highlighted the parameters available and the impact of their relationships on design optimisation. The behavioural models, inherently based on approximations, were then initially validated by comparing them with actual circuit behaviour, obtained using discrete components. These comparisons revealed that the models matched the actual circuit measurements. Finally, taking into account the conclusions of our theoretical study, an integrated version of this adaptive threshold spike detector was designed using 0.35-µm complementary metal-oxide-semiconductor technology. Simulations of this circuit revealed that the proposed controller eliminated the static error and ensured efficient spike detection. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective A prerequisite for the implementation of interictal electroencephalography-correlated functional magnetic resonance imaging ( EEG-f MRI) in the presurgical work-up for epilepsy surgery is straightforward processing. We propose a new semi-automatic method as alternative for the challenging and time-consuming visual spike identification. Methods Our method starts from a patient-specific spike-template, built by averaging spikes recorded on the EEG outside the scanner. Spatiotemporal cross-correlations between the template and the EEG measured during f MRI were calculated. To minimize false-positive detections, this time course of cross-correlations was binarized by means of a spike-template-specific threshold determined in healthy controls. To inform our model for statistical parametric mapping, this binarized regressor was convolved with the canonical hemodynamic response function. We validated our 'template-based' method in 21 adult patients with refractory focal epilepsy with a well-defined epileptogenic zone and interictal spikes during EEG-f MRI. Sensitivity and specificity for detecting the epileptogenic zone were calculated and represented in receiver operating characteristic ( ROC) curves. Our approach was compared with a previously proposed semiautomatic 'topography-based' method that used the topographic amplitude distribution of spikes as a starting point for correlation-based fitting. Results Good diagnostic performance could be reached with our template-based method. The optimal area under the ROC curve was 0.77. Diagnostic performance of the topography-based method was overall low. Significance Our new template-based method is more standardized and time-saving than visual spike identification on intra-scanner EEG recordings, and preserves good diagnostic performance for detecting the epileptogenic zone. [ABSTRACT FROM AUTHOR]

Purpose: The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring. Methods: Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity. Our rule-based algorithm estimated 18 parameters around each detected peak and combined multichannel detections into one event. Similarity measures were derived from equivalent location, scalp topography, and source waveform of each event to form clusters over 2-h epochs using a density-based algorithm. Similar measures were applied to all 2-h clusters to form 24-h hyper-clusters. Independent raters evaluated electroencephalography data of 50 patients with epilepsy (25 children) using traditional visual spike review and optimized hyper-cluster inspection. Congruence between visual spike types and epileptiform hyper-clusters was assessed on a sublobar level using three-dimensional (3D) peak topographies. Key Findings: Visual rating found 126 different epileptiform spike types (2.5 per patient). Independently, 129 hyper-clusters were classified as epileptiform and originating in separate sublobar regions (2.6 per patient). Ninety-one percent of visual spike types matched with hyper-clusters (temporal lobe spikes 94%, extratemporal 89%). Conversely, 11% of hyper-clusters rated epileptiform had no corresponding visual spike type. Numbers were comparable in adults and children. On average, 15 hyper-clusters had to be inspected and rated per patient with an evaluation time of around 5 min. Significance: Hyper-clustering over 24 h provides an independent tool for rapid daily evaluation of interictal spikes in long-term video-EEG monitoring. If used in addition to routine review of 2-5 min EEG per hour, sensitivity and reliability in noninvasive diagnosis of focal epilepsy increases. [ABSTRACT FROM AUTHOR]

The present study introduces an approach to detect and classify extracellulary recorded action potentials of neurons, usually termed spike sorting. Our approach is based on template matching, which is an optimal filter under Gaussian noise; however, this approach is usually expensive in computational time. To speed up the filter, it is important to curtail the matching process when the distance between template and waveform exceeds some threshold. We approach this aspect of the problem using the frame of similarity detection algorithms (SSDA) and Davies-Bouldin validation indices (DBVI). Windowing pair of the filter was selected in DBVI-based order and a signal which has a rapidly increasing error was discarded to reduce the computational time. DBVI is a function of the ratio of the sum of within-cluster scatter to between-cluster separation; thus, using this order we can expect to separate a signal and a noise in fewer window point calculation than full point matching. This matching process performed well, with a shortter computational time and fewer incorrect classifications than other ordering methods such as time-based or amplitude-based order. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(11): 57–63, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecj.10066 [ABSTRACT FROM AUTHOR]