A spike detection method in EEG based on improved morphological filter

Pitfalls in scalp EEG: Current obstacles and future directions

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Epileptic seizure focus detection from interictal electroencephalogram: a survey

Electroencephalogram (EEG) is one of most effective clinical diagnosis modalities for the localization of epileptic focus. Most current AI solutions use this modality to analyze the EEG signals in an automated manner to identify the epileptic seizure focus. To develop AI system for identifying the epileptic focus, there are many recently-published AI solutions based on biomarkers or statistic features that utilize interictal EEGs. In this review, we survey these solutions and find that they can be divided into three main categories: (i) those that use of biomarkers in EEG signals, including high-frequency oscillation, phase-amplitude coupling, and interictal epileptiform discharges, (ii) others that utilize feature-extraction methods, and (iii) solutions based upon neural networks (an end-to-end approach). We provide a detailed description of seizure focus with clinical diagnosis methods, a summary of the public datasets that seek to reduce the research gap in epilepsy, recent novel performance evaluation criteria used to evaluate the AI systems, and guidelines on when and how to use them. This review also suggests a number of future research challenges that must be overcome in order to design more efficient computer-aided solutions to epilepsy focus detection.

EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-based Ranking

Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates' magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).

Seizure classification with selected frequency bands and EEG montages: a Natural Language Processing approach

Individualized treatment is crucial for epileptic patients with different types of seizures. The differences among patients impact the drug choice as well as the surgery procedure. With the advance in machine learning, automatic seizure detection can ease the manual time-consuming and labor-intensive procedure for diagnose seizure in the clinical setting. In this paper, we present an electroencephalography (EEG) frequency bands (sub-bands) and montages selection (sub-zones) method for classifier training that exploits Natural Language Processing from individual patients' clinical report. The proposed approach is targeting for individualized treatment. We integrated the prior knowledge from patient's reports into the classifier-building process, mimicking the authentic thinking process of experienced neurologist's when diagnosing seizure using EEG. The keywords from clinical documents are mapped to the EEG data in terms of frequency bands and scalp EEG electrodes. The data of experiments are from the Temple University Hospital EEG seizure corpus, and the dataset is divided based on each group of patients with same seizure type and same recording electrode references. The classifier includes Random Forest, Support Vector Machine and Multi-Layer Perceptron. The classification performance indicates that competitive results can be achieve with a small portion of EEG the data. Using the sub-zones selection for Generalized Seizures (GNSZ) on all three electrodes, data are reduced by nearly 50% while the performance metrics remain at the same level with the whole frequency and zones. Moreover, when selecting by sub-zones and sub-bands together for GNSZ with Linked Ears reference, the data range reduced to 0.3% of whole range, and the performance deviates less than 3% from the results with whole range of data. Results show that using proposed approach may lead to more efficient implementations of the seizure classifier to be executed on power-efficient devices for long lasting real-time seizures detection.

Fast parametric curve matching (FPCM) for automatic spike detection

Epilepsy is a widely spread neurological disease, whose treatment often requires resection of the pathological cortical tissue. Interictal spike analysis observed in the non-invasively collected EEG or MEG data offers a way to localize epileptogenic cortical structures for surgery planning purposes. While a plethora of automatic spike detection techniques have been developed each with its own assumptions and limitations, non of them is ideal and the best results are achieved when the output of several automatic spike detectors are combined. This is especially true in the low signal-to-noise ratio conditions. To this end we propose a novel biomimetic approach for automatic spike detection based on a constrained mixed spline machinery that we dub as fast parametric curve matching (FPCM). Using the peak-wave shape parametrization, the constrained parametric morphological model is constructed and convolved with the observed multichannel data to very efficiently determine mixed spline parameters corresponding to each time-point in the dataset. Then the logical predicates that directly map to the expected interictal event morphology allow us to accomplish the spike detection task. The results of simulations mimicking typical low SNR scenario show the robustness and high ROC AUC values of the FPCM method as compared to the spike detection performed by the means of more conventional approaches such as wavelet decomposition, template matching or simple amplitude thresholding. Applied to the real MEG and EEG data from the human patients and to ECoG data from the rat, the FPCM technique demonstrates reliable detection of the interictal events and localization of epileptogenic zones concordant with independent conclusions made by the epileptologist. Since the FPCM is computationally light, tolerant to high amplitude artifacts and flexible to accommodate verbalized descriptions of the arbitrary target morphology, it may complement the existing arsenal of means for analysis of noisy interictal datasets.

Machine learning for detection of interictal epileptiform discharges

The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Research towards automating IED detection started 45 years ago. Approaches range from mimetic methods to deep learning techniques. We review different approaches to IED detection, discussing their performance and limitations. Traditional machine learning and deep learning methods have yielded the best results so far and their application in the field is still growing. Standardization of datasets and outcome measures is necessary to compare models more objectively and decide which should be implemented in a clinical setting.

An energy screening and morphology characterization-based hybrid expert scheme for automatic identification of micro-sleep event K-complex

BACKGROUND AND OBJECTIVE

K-complexes, as a significant indicator in sleep staging and sleep protection, are an important micro-event in sleep analysis. Clinically, K-complexes are recognized through the expert visual inspection of electroencephalogram (EEG) during sleep. Since this process is laborious and has high inter-observer variability, developing automated K-complex detection methods can alleviate the burden on clinicians while providing reliable recognition results. However, existing methods face the following issues. First, most work only identifies the K-complexes in stage 2, which requires distinguishing the sleep stages as the prerequisite for further events' identification. Second, most approaches can only detect the occurrence of events without the ability to predict their location and duration, which are also essential to sleep analysis.

METHODS

In this work, a novel hybrid expert scheme for K-complex detection is proposed by integrating signal morphology with expert knowledge into the decision-making process. To eliminate artifacts, and to minimize the individual variability in raw sleep EEG signals, the potential K-complex candidates are first screened by combining Teager energy operator (TEO) and personalized thresholds. Then, to distinguish signal shapes from background activity, a novel frame of filtering based on morphological filtering (MF) is devised to differentiate morphological components of K-complex waveforms from EEG series. Finally, K-complex waveforms are identified from the extracted morphological information by judgment rules, which are inspired by expert knowledge of micro-sleep events.

RESULTS

Detection performance is evaluated by its application on the public database MASS-C1 (Montreal archives of sleep studies cohort one) which includes the recordings of 19 healthy adults. The detection performance demonstrates an F-measure of 0.63 with a recall of 0.81 and a precision of 0.53 on average. The duration error between events and detections is 0.10 s.

CONCLUSIONS

The presented scheme has detected the occurrence of events. Meanwhile, it has recognized their locations and durations. The favorable results exhibit that the proposed scheme outperforms the state-of-the-art studies and has great potential to help release the burden of experts in sleep EEG analysis.

Modeling Volatility Characteristics of Epileptic EEGs using GARCH Models

Objective: To determine if there was a difference in the volatility characteristics of seizure and non-seizure onset channels in the intracranial electroencephalogram (EEG) in a patient with temporal lobe epilepsy. Methods: The half-life of volatility for the different EEG channels was determined using Autoregressive Moving Average–Generalized Autoregressive Conditional Heteroscedasticity (ARMA–GARCH) models; confidence intervals were constructed using the delta method and an asymptotic method for comparing the half-lives. Results: Clinically determined seizure onsets occurred over strip electrodes named RAST (Right Anterior Subtemporal) and RMST (Right Mid Subtemporal), at locations 2, 3 and 4, on the strip electrodes. The half-lives of volatility for two of the three seizure channels, RAST3 and RAST4, were found to be significantly lower the rest of the channels for six one-minute EEG segments prior to seizure onset and nine one-minute EEG segments of an awake state. The half-lives of volatility for RAST3 and RAST4 were not significantly different to the non-seizure channels for ten one-minute segments of sleep and ten one-minute segments of sleep-to-awake states. The estimates for the half-lives were consistent for randomly selected one-minute EEG segments. Conclusions: The use of GARCH models may be a useful tool in determining hidden properties in epileptiform EEGs that may lead to better understanding of the seizure generating process.

A review on the pattern detection methods for epilepsy seizure detection from EEG signals

Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.

Epilepsy detection from EEG signals: a review

Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.

Rapid annotation of interictal epileptiform discharges via template matching under Dynamic Time Warping

BACKGROUND

EEG interpretation relies on experts who are in short supply. There is a great need for automated pattern recognition systems to assist with interpretation. However, attempts to develop such systems have been limited by insufficient expert-annotated data. To address these issues, we developed a system named NeuroBrowser for EEG review and rapid waveform annotation.

NEW METHODS

At the core of NeuroBrowser lies on ultrafast template matching under Dynamic Time Warping, which substantially accelerates the task of annotation.

RESULTS

Our results demonstrate that NeuroBrowser can reduce the time required for annotation of interictal epileptiform discharges by EEG experts by 20-90%, with an average of approximately 70%.

COMPARISON WITH EXISTING METHOD(S)

In comparison with conventional manual EEG annotation, NeuroBrowser is able to save EEG experts approximately 70% on average of the time spent in annotating interictal epileptiform discharges. We have already extracted 19,000+ interictal epileptiform discharges from 100 patient EEG recordings. To our knowledge this represents the largest annotated database of interictal epileptiform discharges in existence.

CONCLUSION

NeuroBrowser is an integrated system for rapid waveform annotation. While the algorithm is currently tailored to annotation of interictal epileptiform discharges in scalp EEG recordings, the concepts can be easily generalized to other waveforms and signal types.

Mouse EEG spike detection based on the adapted continuous wavelet transform

OBJECTIVE

Electroencephalography (EEG) is an important tool in the diagnosis of epilepsy. Interictal spikes on EEG are used to monitor the development of epilepsy and the effects of drug therapy. EEG recordings are generally long and the data voluminous. Thus developing a sensitive and reliable automated algorithm for analyzing EEG data is necessary.

APPROACH

A new algorithm for detecting and classifying interictal spikes in mouse EEG recordings is proposed, based on the adapted continuous wavelet transform (CWT). The construction of the adapted mother wavelet is founded on a template obtained from a sample comprising the first few minutes of an EEG data set.

MAIN RESULT

The algorithm was tested with EEG data from a mouse model of epilepsy and experimental results showed that the algorithm could distinguish EEG spikes from other transient waveforms with a high degree of sensitivity and specificity.

SIGNIFICANCE

Differing from existing approaches, the proposed approach combines wavelet denoising, to isolate transient signals, with adapted CWT-based template matching, to detect true interictal spikes. Using the adapted wavelet constructed from a predefined template, the adapted CWT is calculated on small EEG segments to fit dynamical changes in the EEG recording.

A novel spatiotemporal analysis of peri-ictal spiking to probe the relation of spikes and seizures in epilepsy

The relation between epileptic spikes and seizures is an important but still unresolved question in epilepsy research. Preclinical and clinical studies have produced inconclusive results on the causality or even on the existence of such a relation. We set to investigate this relation taking in consideration seizure severity and spatial extent of spike rate. We developed a novel automated spike detection algorithm based on morphological filtering techniques and then tested the hypothesis that there is a pre-ictal increase and post-ictal decrease of the spatial extent of spike rate. Peri-ictal (around seizures) spikes were detected from intracranial EEG recordings in 5 patients with temporal lobe epilepsy. The 94 recorded seizures were classified into two classes, based on the percentage of brain sites having higher or lower rate of spikes in the pre-ictal compared to post-ictal periods, with a classification accuracy of 87.4%. This seizure classification showed that seizures with increased pre-ictal spike rate and spatial extent compared to the post-ictal period were mostly (83%) clinical seizures, whereas no such statistically significant (α = 0.05) increase was observed peri-ictally in 93% of sub-clinical seizures. These consistent across patients results show the existence of a causal relation between spikes and clinical seizures, and imply resetting of the preceding spiking process by clinical seizures.