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Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

Seizure detection from EEG signals is crucial for diagnosing and treating neurological disorders. However, accurately detecting seizures is challenging due to the complexity and variability of EEG signals. This paper proposes a deep learning model, called Hybrid Cross Layer Attention Based Convolutional Bidirectional Gated Recurrent Unit (HCLA_CBiGRU), which combines convolutional neural networks and recurrent neural networks to capture spatial and temporal features in EEG signals. A combinational EEG dataset was created by merging publicly available datasets and applying a preprocessing pipeline to remove noise and artifacts. The dataset was then segmented and split into training and testing sets. The HCLA_CBiGRU model was trained on the training set and evaluated on the testing set, achieving an impressive accuracy of 98.5%, surpassing existing state-of-the-art methods. Sensitivity and specificity, critical metrics in clinical practice, were also assessed, with the model demonstrating a sensitivity of 98.5% and a specificity of 98.9%, highlighting its effectiveness in seizure detection. Visualization techniques were used to analyze the learned features, showing the model's ability to capture distinguishing seizure-related characteristics. In conclusion, the proposed CBiGRU model outperforms existing methods in terms of accuracy, sensitivity, and specificity for seizure detection from EEG signals. Its integration with EEG signal analysis has significant implications for improving the diagnosis and treatment of neurological disorders, potentially leading to better patient outcomes.

Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficient diagnosis. In this study, we aimed to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN). Our proposed approach provides an online and real-time preliminary diagnosis of epileptic seizures and helps to detect possible pathological conditions. To validate our approach, we conducted experiments using multiple datasets. We utilized a trained SNN to identify the presence of epileptic seizures and compared our results with those of related studies. The SNN model was deployed on Xylo, a digital SNN neuromorphic processor designed to process temporal signals. Xylo efficiently simulates spiking leaky integrate-and-fire neurons with exponential input synapses. Xylo has much lower energy requirements than traditional approaches to signal processing, making it an ideal platform for developing low-power seizure detection systems. Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. At the same time, the application has an average power consumption of 87.4 μW (IO power) + 287.9 μW (compute power) when deployed to Xylo. Our method demonstrates excellent low-latency performance when tested on multiple datasets. Our work provides a new solution for seizure detection, and it is expected to be widely used in portable and wearable devices in the future., Competing Interests: Declaration of competing interest The authors of this paper hereby declare that there are no financial, personal, or other relationships that could or might be perceived to influence the objectivity of our research and the results presented in this paper. We affirm that our research work has been conducted independently and has not been unduly influenced by any funding agencies, commercial entities, or other organizations., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A patient-specific novel systematic methodology is described in this study for automatic seizure detection from raw electroencephalogram (EEG) signals. Filtering process by means of band-pass finite impulse response (FIR) filter with the frequency range of 0.5–40 Hz is implemented at the outset to eliminate different artifacts and noises mixed with raw EEG signals. As EEGs are highly non-linear and non-stationary signals in nature, discrete wavelet transform (DWT) is then used to analyze the signals in time-frequency domain. DWT with four level decomposition is performed using db6 mother wavelet for feature extraction. A new feature set, composed of eleven non-linear statistical features extracted from each sub-bands resulting from due to wavelet decomposition, is then fed to the input of artificial neural network (ANN) to classify the signal accurately. Finally, a novel algorithm named sequential window algorithm is carried out to improve the classification performance. 99.44% mean classification accuracy, 80.66% average sensitivity, 4.12 s mean latency and 0.2% average false positive rate (FPR) are achieved in this study. This study successfully reduces the latency time with more accuracy and significantly low FPR.

Background: Assistive automatic seizure detection can empower human annotators to shorten patient monitoring data review times. We present a proof-of-concept for a seizure detection system that is sensitive, automated, patient-specific, and tunable to maximise sensitivity while minimizing human annotation times. The system uses custom data preparation methods, deep learning analytics and electroencephalography (EEG) data. Methods: Scalp EEG data of 365 patients containing 171,745 s ictal and 2,185,864 s interictal samples obtained from clinical monitoring systems were analysed as part of a crowdsourced artificial intelligence (AI) challenge. Participants were tasked to develop an ictal/interictal classifier with high sensitivity and low false alarm rates. We built a challenge platform that prevented participants from downloading or directly accessing the data while allowing crowdsourced model development. Findings: The automatic detection system achieved tunable sensitivities between 75.00% and 91.60% allowing a reduction in the amount of raw EEG data to be reviewed by a human annotator by factors between 142x, and 22x respectively. The algorithm enables instantaneous reviewer-managed optimization of the balance between sensitivity and the amount of raw EEG data to be reviewed. Interpretation: This study demonstrates the utility of deep learning for patient-specific seizure detection in EEG data. Furthermore, deep learning in combination with a human reviewer can provide the basis for an assistive data labelling system lowering the time of manual review while maintaining human expert annotation performance. Funding: IBM employed all IBM Research authors. Temple University employed all Temple University authors. The Icahn School of Medicine at Mount Sinai employed Eren Ahsen. The corresponding authors Stefan Harrer and Gustavo Stolovitzky declare that they had full access to all the data in the study and that they had final responsibility for the decision to submit for publication.

Detection of non-convulsive seizures (NCSz) is a challenging task because they lack convulsions, meaning no physical visible symptoms are there to detect the presence of a seizure activity. Hence their diagnosis is not easy, also continuous observation of full length EEG for the detection of non-convulsive seizures (NCSz) by an expert or a technician is a very exhaustive, time consuming job. A technique for the automatic detection of NCSz is proposed in this paper. The database used in this research was recorded at the All India Institute of Medical Sciences (AIIMS), New Delhi. 13 EEG recordings of 9 subjects consisting of a total 23 seizures of 29.42 min duration were used for analysis. Normalized modified Wilson amplitude is used as a key feature to classify between normal and seizure activity. The main advantage of this study lies in the fact that no classifier is used here and hence algorithm is very simple and computationally fast. With the use of only one feature, all of the seizures under test were detected correctly, and hence the median sensitivity and specificity of 100% and 99.21% were achieved respectively.

Objective: To analyze chronological changes in phase-amplitude coupling (PAC) and verify whether PAC analysis can diagnose epileptogenic zones during seizures., Methods: We analyzed 30 seizures in 10 patients with mesial temporal lobe epilepsy who had ictal discharges with preictal spiking followed by low-voltage fast activity patterns on intracranial electroencephalography. We used the amplitude of two high-frequency bands (ripples: 80-200 Hz, fast ripples: 200-300 Hz) and the phase of three slow wave bands (0.5-1 Hz, 3-4 Hz, and 4-8 Hz) for modulation index (MI) calculation from 2 minutes before seizure onset to seizure termination. We evaluated the accuracy of epileptogenic zone detection by MI, in which a combination of MI was better for diagnosis and analyzed patterns of chronological changes in MI during seizures., Results: MI Ripples/3-4 Hz and MI Ripples/4-8 Hz in the hippocampus were significantly higher than those in the peripheral regions from seizure onset. Corresponding to the phase on intracranial electroencephalography, MI Ripples/3-4 Hz decreased once and subsequently increased again. MI Ripples/4-8 Hz showed continuously high values., Conclusions: Continuous measurement of MI Ripples/3-4 Hz and MI Ripples/4-8 Hz could help identify epileptogenic zones., Significance: PAC analysis of ictal epileptic discharges can help epileptogenic zone identification., (Copyright © 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: Because of SUDEP (Sudden and unexpected death in epilepsy) and other direct consequences of generalized tonic-clonic seizures, the use of efficient seizure detection tool may be helpful for patients, relatives and caregivers. We aimed to evaluate an under-mattress detection tool (EMFIT®) in real-life hospital conditions, in particular its sensitivity and false alarm rate (FAR), as well as its impact on patient care., Methods: We carried out a retrospective study on a cohort of patients with epilepsy admitted between September 2017 and June 2021 to Amiens University Hospital for a video-EEG of at least 24 h, during which at least one epileptic seizure was recorded. All video-EEGs records were analyzed visually in order to assess the sensitivity of the under-mattress tool (triggering of the alarm) and to classify the seizure type (convulsive/non convulsive). We also considered whether nurses intervened during the seizure, and the time of their intervention if applicable. An additional prospective survey was conducted over 272 days to analyze the FAR of the tool., Results: A total of 220 seizures were included in the study, from 55 patients, including 23 convulsive seizures from 15 patients and 197 non-convulsive seizures. Sensitivity for convulsive seizure detection was 69.6%. As expected, none of the non-convulsive seizures was detected. The false alarm rate was 0.007/day. Median trigger time was 74 s, decreasing to 5 s for generalized tonic-clonic seizure. The frequency of nurses' intervention during convulsive seizures was significantly greater in case of the alarm triggering (100% vs 57%, p<0.02)., Significance: These results suggest that EMFIT® sensor is able to detect convulsive seizures with good sensitivity and low FAR, and allows caregivers to intervene more often in the event of a nocturnal seizure. This would be an interesting complementary tool to better secure the patients with epilepsy during hospitalization or at home., Competing Interests: Declaration of Competing Interest None of the authors has any conflict of interest to disclose., (Copyright © 2023 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: Under- and overtreatment of seizures may result in adverse outcomes; thus, early, reliable seizure identification is crucial. Continuous conventional ElectroEncephaloGram (cEEG) is the gold standard, but amplitude integrated EEG (aEEG) is most widely-used in the Neonatal Intensive Care Unit (NICU). We aimed to determine whether a novel pathway combining cEEG and aEEG for seizure detection would improve real-time seizure identification., Methods: A single-center, prospective quality improvement project cohort. Patients at-risk of seizures were monitored by cEEG and aEEG concurrently, with the option for the neonatologist to contact a neurologist for real-time cEEG interpretation. The primary outcome was correct identification of seizures by the new combined pathway compared to aEEG alone., Results: Seizure detection using aEEG had a sensitivity of 46.2%; specificity of 93.3%; PPV of 66.7%; and NPV of 85.7%. Utilizing the new on-demand, real-time cEEG interpretation by a neurologist, correct seizure identification increased by 27% (95%CI: 10-56%). Over-diagnosis of seizures was avoided in 33.3% (95% CI: 12.1-64.6%) and misuse of anti-seizure medication was prevented., Conclusions: Combining aEEG with on-demand cEEG interpretation improved accurate seizure detection in a high-risk NICU population, with the potential to avoid over-treatment., Significance: We describe a novel combined EEG monitoring pathway to improve seizure detection, and prevent unnecessary treatment., (Crown Copyright © 2022. Published by Elsevier B.V. All rights reserved.)

Objective: Ultra long-term monitoring with subcutaneous EEG (sqEEG) offers objective outpatient recording of electrographic seizures as an alternative to self-reported epileptic seizure diaries. This methodology requires an algorithm-based automatic seizure detection to indicate periods of potential seizure activity to reduce the time spent on visual review. The objective of this study was to evaluate the performance of a sqEEG-based automatic seizure detection algorithm., Methods: A multicenter cohort of subjects using sqEEG were analyzed, including nine people with epilepsy (PWE) and 12 healthy subjects, recording a total of 965 days. The automatic seizure detections of a deep-neural-network algorithm were compared to annotations from three human experts., Results: Data reduction ratios were 99.6% in PWE and 99.9% in the control group. The cross-PWE sensitivity was 86% (median 80%, range 69-100% when PWE were evaluated individually), and the corresponding median false detection rate was 2.4 detections per 24 hours (range: 2.0-13.0)., Conclusions: Our findings demonstrated that step one in a sqEEG-based semi-automatic seizure detection/review process can be performed with high sensitivity and clinically applicable specificity., Significance: Ultra long-term sqEEG bears the potential of improving objective seizure quantification., Competing Interests: Conflict of interest JDH and LSR are employees of UNEEG medical A/S. TWK consults for UNEEG medical A/S. PFV and AMKO received a payment from UNEEG medical A/S for data annotation. SW has received support from UNEEG medical A/S. MPR has been a member of ad hoc advisory boards for UNEEG medical A/S. FF and MH are employees of AIT Austrian Institute of Technology GmbH, which has developed UNEEG™ EpiSight Analyzer., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEG TM SubQ, Neuroview TM , Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier B.V.)

Objective: To explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection., Methods: Thirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies., Results: Three patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research., Conclusions: Multi-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG., Significance: This study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Purpose: Seizure detection algorithms (SDAs) based on electroencephalography (EEG) have been described in previous studies, but the imbalanced data distribution of ictal and interictal states continue to pose a technical challenge. This study proposes a novel algorithm to address the imbalanced classification problem and improve seizure detection performance., Method: The proposed algorithm is designed based on hybrid sampling and a cost-sensitive (CS) classifier. Hybrid sampling resamples the imbalanced EEG data at data-level, and the CS classifier, which is used as an algorithm-level tool, reduces the overall misclassification cost of seizure detection. The synthetic minority oversampling technique and undersampling TomekLink technique are combined to reduce the imbalanced ratio between ictal and interictal states while retaining the generalization ability. Finally, CS support vector machine classifies the resampled EEG feature vectors, assigning different cost sensitive parameters to moderate the poor performance resulting from the imbalanced distribution problem., Result: The proposed algorithm improved the average sensitivity and AUC by 46.67% and 0.0482, respectively, compared with the original results without using the imbalanced EEG data processing (IEDP) technique. Experimental results showed that an average sensitivity and AUC of 86.34% and 0.9837, respectively, could be obtained across all cases. Finally, a performance evaluation showed that the proposed algorithm outperformed published methods in terms seizure detection., Conclusion: A fusion algorithm combining data- and algorithm-level methods can achieve high sensitivity and AUC compared with existing IEDP methods. Thus, SDA performance can be improved, enabling their clinical use with EEG-based SMSs., (Copyright © 2021. Published by Elsevier Ltd.)

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility., (Copyright © 2021 International Federation of Clinical Neurophysiology, Inc., International League Against Epilepsia. Published by Elsevier B.V. All rights reserved.)

Epileptic seizure detection is of great significance in the diagnosis of epilepsy and relieving the heavy workload of visual inspection of electroencephalogram (EEG) recordings. This paper presents a novel method for seizure detection using the Stein kernel-based sparse representation (SR) for EEG recordings. Different from the traditional SR scheme that works with vector data in Euclidean space, the Stein kernel-based SR framework is constructed for seizure detection in the space of the symmetric positive definite (SPD) matrices, which form a Riemannian manifold. Due to the non-Euclidean geometry of the Riemannian manifold, the Stein kernel on the manifold permits the embedding of the manifold in a high-dimensional reproducing kernel Hilbert space (RKHS) to perform SR. In the Stein kernel-based SR framework, EEG samples are described by SPD matrices in the form of covariance descriptors (CovDs). Then, a test EEG sample is sparsely represented on the training set, and the test sample is classified as a member of the class, which leads to the minimum reconstructed residual. Finally, by using three widely used EEG datasets to evaluate the detection performance of the proposed method, the experimental results demonstrate that it achieves good classification accuracy on each dataset. Furthermore, the fast computational speed of the Stein kernel-based SR also meets the basic requirements for real-time seizure detection., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose To quantify the influence of prior knowledge about the patient and the EEG circumstances on the EEG-based seizure detection rate. Methods A sample of 95 EEGs with epileptic seizure patterns matched with 95 seizure-free control sequences were extracted from EEG video monitoring data. They were stripped of all additional information. These plain EEG recordings were evaluated by two board certified EEG reviewers. The results were compared with the interpretations of the original video monitoring evaluations. Results Using the plain EEG sequences, epileptic seizure patterns were detected with a sensitivity and specificity of 0.758 and 0.958, respectively. The classification of the seizure pattern localization and lateralization differed in 56% and 50%, respectively, from the results of the video monitoring evaluations. Conclusion Additional information about the patient and the events during an EEG recording leads to a clinically and statistically significant increase in the seizure detection rates. These results imply that the human evaluation of a plain EEG without further information may not be seen as the gold standard in EEG evaluation. The performance estimation of automated EEG evaluation methods should take this into account., (Copyright © 2021 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

EEG signals carry rich information about brain activity and play an important role in the diagnosis and recognition of epilepsy. Numerous algorithms using EEG signals to detect seizures have been developed in recent decades. However, most of them require well-designed features that highly depend on domain-specific knowledge and algorithm expertise. In this study, we introduce the unigram ordinal pattern (UniOP) and bigram ordinal pattern (BiOP) representations to capture the different underlying dynamics of time series, which only assumes that time series derived from different dynamics can be characterized by repeated ordinal patterns. Specifically, we first transform each subsequence in a time series into the corresponding ordinal pattern in terms of the ranking of values and consider the distribution of ordinal patterns of all subsequences as the UniOP representation. Furthermore, we consider the distribution of the cooccurrence of ordinal patterns as the BiOP representation to characterize the contextual information for each ordinal pattern. We then combine the proposed representations with the nearest neighbor algorithm to evaluate its effectiveness on three publicly available seizure datasets. The results on the Bonn EEG dataset demonstrate that this method provides more than 90% accuracy, sensitivity, and specificity in most cases and outperforms several state-of-the-art methods, which proves its ability to capture the key information of the underlying dynamics of EEG time series at healthy, seizure-free, and seizure states. The results on the second dataset are comparable with the state-of-the-art method, showing the good generalization ability of the proposed method. All performance metrics on the third dataset are approximately 89%, which demonstrates that the proposed representations are suitable for large-scale datasets., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: Undetected and prolonged epileptic seizures can result in hypoxic brain damage or death and occur most often when the victim is in bed alone or unsupervised. Sudden unexpected death in epilepsy may not always be preventable but it is believed that timely assistance with rescue medication and body re-positioning may overcome respiratory compromise in some cases. A novel algorithm based on a real time moving 9 s epoch, calculating 25 % percentage heart rate change and/or an oxygen saturation trigger level of <85 % was developed using photoplethysmography and incorporated into a prototype data storage device., Methods: The algorithm was clinically evaluated in this multicentre trial in the detection of clinically significant epileptic seizures. A range of epileptic seizures and normal physiological events were recorded and classified by reference standard EEG Videotelemetry and time-synchronised event data recorded by the prototype device incorporating the pre-specified cut-off points prospectively and retrospective analysis of all events., Results: 119 participants who were attending electroencephalographic (EEG) videotelemetry as part of their clinical management of their epilepsy consented to take part in the trial. 683 epileptic seizures (77 clinically significant seizures) and 2648 normal physiological events were captured. When using pre-specified cut-off point 25 % heart rate change and/or oxygen desaturation <85 % on the basis of one/other, the device showed a sensitivity of 87 % for detecting clinically significant seizures. False Alarm Rate 4.5 (24 h FAR), detection latency of 58 s using heart rate percentage change., Conclusions: The results indicate that the novel algorithm can be used in detecting clinically significant seizures., (Copyright © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Automatic seizure detection technology not only reduces workloads of neurologists for epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A novel seizure detection method based on the deep bidirectional long short-term memory (Bi-LSTM) network is proposed in this paper. To preserve the non-stationary nature of EEG signals while decreasing the computational burden, the local mean decomposition (LMD) and statistical feature extraction procedures are introduced. The deep architecture is then designed by combining two independent LSTM networks with the opposite propagation directions: one transmits information from the front to the back, and another from the back to the front. Thus the deep model can take advantage of the information both before and after the currently analyzing moment to jointly determine the output state. A mean sensitivity of 93.61% and a mean specificity of 91.85% were achieved on a long-term scalp EEG database. The comparisons with other published methods based on either traditional machine learning models or convolutional neural networks demonstrated the improved performance for seizure detection., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Purpose: Complete visual review of prolonged video-EEG recordings at an EMU (Epilepsy Monitoring Unit) is time consuming and can cause problems in times of paucity of educated personnel. In this study we aimed to show non inferiority for electroclinical diagnosis using sampled review in combination with EEG analysis softreferware (P13 software, Persyst Corporation), in comparison to complete visual review., Method: Fifty prolonged video-EEG recordings in adults were prospectively evaluated using sampled visual EEG review in combination with automated detection software of the complete EEG record. Visually assessed samples consisted of one hour during wakefulness, one hour during sleep, half an hour of wakefulness after wake-up and all clinical events marked by the individual and/or nurses. The final electro-clinical diagnosis of this new review approach was compared with the electro-clinical diagnosis after complete visual review as presently used., Results: The electro-clinical diagnosis based on sampled visual review combined with automated detection software did not differ from the diagnosis based on complete visual review. Furthermore, the detection software was able to detect all records containing epileptiform abnormalities and epileptic seizures., Conclusion: Sampled visual review in combination with automated detection using Persyst 13 is non-inferior to complete visual review for electroclinical diagnosis of prolonged video-EEG at an EMU setting, which makes this approach promising., Competing Interests: Declaration of competing interest None of the authors has any conflict of interest to disclose., (Copyright © 2020 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Purpose: A phase I feasibility study to determine the accuracy of identifying seizures based on audio recordings., Methods: We systematically generated 166 audio clips of 30 s duration from 83 patients admitted to an epilepsy monitoring unit between 1/2015 and 12/2016, with one clip during a seizure period and one clip during a non-seizure control period for each patient. Five epileptologists performed a blinded review of the audio clips and rated whether a seizure occurred or not, and indicated the confidence level (low or high) of their rating. The accuracy of individual and consensus ratings were calculated., Results: The overall performance of the consensus rating between the five epileptologists showed a positive predictive value (PPV) of 0.91 and a negative predictive value (NPV) of 0.66. The performance improved when confidence was high (PPV of 0.96, NPV of 0.70). The agreement between the epileptologists was moderate with a kappa of 0.584. Hyperkinetic (PPV 0.92, NPV 0.86) and tonic-clonic (PPV and NPV 1.00) seizures were most accurately identified. Seizures with automatisms only and non-motor seizures could not be accurately identified. Specific seizure-related sounds associated with accurate identification included disordered breathing (PPV and NPV 1.00), rhythmic sounds (PPV 0.93, NPV 0.80), and ictal vocalizations (PPV 1.00, NPV 0.97)., Conclusion: This phase I feasibility study shows that epileptologists are able to accurately identify certain seizure types from audio recordings when the seizures produce sounds. This provides guidance for the development of audio-based seizure detection devices and demonstrate which seizure types could potentially be detected., Competing Interests: Declaration of Competing Interest Author DF receives salary support for consulting and clinical trial related activities performed on behalf of The Epilepsy Study Consortium, a non-profit organization. DF receives no personal income for these activities. NYU receives a fixed amount from the Epilepsy Study Consortium towards DF’s salary. Within the past year, The Epilepsy Study Consortium received payments for research services performed by DF from: Adamas, Axcella, Biogen, Crossject, Engage Pharmaceuticals, Eisai, GW Pharmaceuticals, Pfizer, SK Life Science, Takeda, Xenon, and Zynerba. DF has also served as a paid consultant for Eisai. DF has received travel support from Medtronics, Eisai and the Epilepsy Foundation. DF receives research support from the CDC, NINDS, Epilepsy Foundation, Epitel, and Neuropace. DF serves on the scientific advisory board for Receptor Life Sciences. DF holds equity interests in Neuroview Technology and Receptor Life Sciences. Author RSF has done consulting for Medtronic and has stock options in Smart-Watch, Avails Medical, Cerebral Therapeutics, Zeto, Irody, Eysz. Author PD receives research support from the NIH and NeuroPace, Inc. PD has received honoraria for educational materials from NeuroPace, Inc. and travel reimbursement from Medtronic and NeuroPace, Inc. The remaining authors have no conflicts of interest., (Copyright © 2020 British Epilepsy Association. All rights reserved.)

Introduction: Epileptologists could benefit from a diagnosis support system that automatically detects seizures because visual inspection of long-term electroencephalograms (EEGs) is extremely time-consuming. However, the diversity of seizures among patients makes it difficult to develop universal features that are applicable for automatic seizure detection in all cases, and the rarity of seizures results in a lack of sufficient training data for classifiers., Methods: To overcome these problems, we utilized an autoencoder (AE), which is often used for anomaly detection in the field of machine learning, to perform seizure detection. We hypothesized that multichannel EEG signals are compressible by AE owing to their spatio-temporal coupling and that the AE should be able to detect seizures as anomalous events from an interictal EEG., Results: Through experiments, we found that the AE error was able to classify seizure and nonseizure states with a sensitivity of 100% in 22 out of 24 available test subjects and that the AE was better than the commercially available software BESA and Persyst for half of the test subjects., Conclusions: These results suggest that the AE error is a feasible candidate for a universal seizure detection feature., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. The deep learning architecture is made up of two convolutional layers for feature extraction and three fully-connected layers for classification. We evaluated several EEG signal transforms for generating the inputs to the deep neural network: Fourier, wavelet and empirical mode decomposition. This analysis was carried out using two public datasets (Bern-Barcelona EEG and Epileptic Seizure Recognition datasets) obtaining significant improvements in accuracy. For the Bern-Barcelona EEG, we obtained an increase in accuracy from 92.3% to 98.9% when classifying between focal and non-focal signals using the empirical mode decomposition. For the Epileptic Seizure Recognition dataset, we evaluated several scenarios for seizure detection obtaining the best results when using the Fourier transform. The accuracy increased from 99.0% to 99.5% for classifying non-seizure vs. seizure recordings, from 91.7% to 96.5% when differentiating between healthy, non-focal and seizure recordings, and from 89.0% to 95.7% when considering healthy, focal and seizure recordings., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Objective: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures., Methods: A deep Long Short-Term Memory (LSTM) network is first used to learn the high-level representations of different EEG patterns. Then, a Fully Connected (FC) layer is adopted to extract the most robust EEG features relevant to epileptic seizures. Finally, these features are supplied to a softmax layer to output predicted labels., Results: The results on a benchmark clinical dataset reveal the prevalence of the proposed approach over the baseline techniques; achieving 100% classification accuracy, 100% sensitivity, and 100% specificity. Our approach is additionally shown to be robust in noisy and real-life conditions. It maintains high detection performance in the existence of common EEG artifacts (muscle activities and eye movement) as well as background noise., Conclusions: We demonstrate the clinical feasibility of our seizure detection approach achieving superior performance over the cutting-edge techniques in terms of seizure detection performance and robustness., Significance: Our seizure detection approach can contribute to accurate and robust detection of epileptic seizures in ideal and real-life situations., (Copyright © 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural networks because their visual recognition ability is comparable to that of humans. We explored image-based seizure detection by applying convolutional neural networks to long-term EEG that included epileptic seizures. After filtering, EEG data were divided into short segments based on a given time window and converted into plot EEG images, each of which was classified by convolutional neural networks as 'seizure' or 'non-seizure'. These resultant labels were then used to design a clinically practical index for seizure detection. The best true positive rate was obtained using a 1-s time window. The median true positive rate of convolutional neural networks labelling by seconds was 74%, which was higher than that of commercially available seizure detection software (20% by BESA and 31% by Persyst). For practical use, the median of detected seizure rate by minutes was 100% by convolutional neural networks, which was higher than the 73.3% by BESA and 81.7% by Persyst. The false alarm of convolutional neural networks' seizure detection was issued at 0.2 per hour, which appears acceptable for clinical practice. Moreover, we demonstrated that seizure detection improved when training was performed using EEG patterns similar to those of testing data, suggesting that adding a variety of seizure patterns to the training dataset will improve our method. Thus, artificial visual recognition by convolutional neural networks allows for seizure detection, which otherwise currently relies on skillful visual inspection by expert epileptologists during clinical diagnosis., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The detection of seizure activity in electroencephalogram (EEG) segments is very important for the classification and localization of epileptic seizures. The evolution of a seizure in an EEG usually appears as a train of non-uniformly spaced spikes and/or as piecewise linear frequency modulated signals. If a seizure is present, then the energy of the EEG is concentrated along the time axis and the frequency axis in the time-frequency plane. However, in the absence of a seizure, the energy of the EEG signal is uniformly distributed along all directions in the time-frequency plane. Based on this observation, we propose a new approach for the detection of a seizure. In this paper, we develop a new feature that exploits the direction of the energy of the signal in the time-frequency domain to distinguish between seizures and non-seizures in an EEG. Our experimental results indicate the superiority of the proposed approach over other conventional time-frequency approaches; for example, the proposed feature set achieves a classification accuracy of 98.25% by only using five features., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Purpose: Automated seizure detection at home is mostly done using either patient-independent algorithms or manually personalized algorithms. Patient-independent algorithms, however, lead to too many false alarms, whereas the manually personalized algorithms typically require manual input from an experienced clinician for each patient, which is a costly and unscalable procedure and it can only be applied when the patient had a sufficient amount of seizures. We therefore propose a nocturnal heart rate based seizure detection algorithm that automatically adapts to the patient without requiring seizure labels., Methods: The proposed method initially starts with a patient-independent algorithm. After a very short initialization period, the algorithm already adapts to the patients' characteristics by using a low-complex novelty detection classifier. The algorithm is evaluated on 28 pediatric patients with 107 convulsive and clinical subtle seizures during 695 h of nocturnal multicenter data in a retrospective study that mimics a real-time analysis., Results: By using the adaptive seizure detection algorithm, the overall performance was 77.6% sensitivity with on average 2.56 false alarms per night. This is 57% less false alarms than a patient-independent algorithm with a similar sensitivity. Patients with tonic-clonic seizures showed a 96% sensitivity with on average 1.84 false alarms per night., Conclusion: The proposed method shows a strongly improved detection performance over patient-independent performance, without requiring manual adaptation by a clinician. Due to the low-complexity of the algorithm, it can be easily implemented on wearables as part of a (multimodal) seizure alarm system., (Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Purpose: Sudden unexpected death in epilepsy (SUDEP) is associated with generalized tonic-clonic seizures (GTCS) with most deaths occurring during sleep. Seizure detection devices have been suggested as a SUDEP prevention strategy. EMG-based GTCS detection can take advantage of the GTCS characteristic of sustained high-amplitude, high-frequency activity in the time-domain., Method: We present a GTCS-detection method based on median-filtered variance estimates on surface EMG measurements and describe its performance in a small exploratory proof-of-concept setting involving a group of 15 patients with 3 GTCS recorded with ear-EEG and another group of 6 patients with 11 GTCS recorded with scalp-EEG., Results: GTCS intervals were detected within 4.2-12.9 s of onset with 100% sensitivity (CI 29.2-100%) without false positives in 820.7 h of ear-EEG. The same detection method worked for the 11 GTCS from scalp EEG data with 100% sensitivity (CI 71.5-100%) and no false positives., Conclusions: Ear-EEG contains enough GTCS-specific EMG activity for GTCS detection to be feasible. Ear-EEG could be considered for nocturnal GTCS monitoring as a supplement to SUDEP preventive interventions., (Copyright © 2018 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objectives: To compare the diagnostic utility of electroencephalography (EEG) using reduced, 8-channel montage (rm-EEG) to full, 18-channel montage (fm-EEG) for detection of generalized or hemispheric seizures and rhythmic periodic patterns (RPPs) by neurologists with extensive EEG training, neurology residents with minimal EEG exposure, and medical students without EEG experience., Methods: We presented EEG samples in both fm-EEG (bipolar montage) and rm-EEG (lateral leads of bipolar montage) to 20 neurologists, 20 residents, and 42 medical students. Unanimous agreement of three senior epileptologists defined samples as seizures (n = 7), RPPs (n = 10), and normal or slowing (n = 20). Differences in median accuracy, sensitivity, and specificity were assessed using Wilcoxon signed-rank tests., Results: Full and reduced EEG demonstrated similar accuracy when read by neurologists (fm-EEG: 95%, rm-EEG: 95%, p  = 0.29), residents (fm-EEG: 80%, rm-EEG: 80%, p  = 0.05), and students (fm-EEG: 60%, rm-EEG: 51%, p  = 0.68). Moreover, neurologists' sensitivity for detecting seizure activity was comparable between fm-EEG (100%) and rm-EEG (98%) ( p  = 0.17). Furthermore, the specificity of rm-EEG for seizures and RPP (neurologists: 100%, residents: 90%, students: 86%) was significantly greater than that of fm-EEG (neurologists: 93%, p  = 0.03; residents: 80%, p  = 0.01; students: 69%, p  < 0.001)., Conclusions: The reduction of the number of EEG channels from 18 to 8 does not compromise neurologists' sensitivity for detecting seizures that are often a core reason for performing urgent EEG. It may also increase their specificity for detecting rhythmic and periodic patterns, and thereby providing important diagnostic information to guide patient's management., Significance: Our study is the first to document the utility of a reduced channel EEG above the hairline compared to full montage EEG in aiding medical staff with varying degrees of EEG training to detect generalized or hemispheric seizures.

Objectives: To measure changes in the visual interpretation of the EEG by the human expert for neonatal seizure detection when reducing the number of recording electrodes., Methods: EEGs were recorded from 45 infants admitted to the neonatal intensive care unit (NICU). Three experts annotated seizures in EEG montages derived from 19, 8 and 4 electrodes. Differences between annotations were assessed by comparing intra-montage with inter-montage agreement (K)., Results: Three experts annotated 4464 seizures across all infants and montages. The inter-expert agreement was not significantly altered by the number of electrodes in the montage (p = 0.685, n = 43). Reducing the number of EEG electrodes altered the seizure annotation for all experts. Agreement between the 19-electrode montage (K 19,19  = 0.832) was significantly higher than the agreement between 19 and 8-electrode montages (dK = 0.114; p < 0.001, n = 42) or 19 and 4-electrode montages (dK = 0.113, p < 0.001, n = 43). Seizure burden and number were significantly underestimated by the 4 and 8-electrode montage (p < 0.001). No significant difference in agreement was found between 8 and 4-electrode montages (dK = 0.002; p = 0.07, n = 42)., Conclusions: Reducing the number of EEG electrodes from 19 electrodes resulted in slight but significant changes in seizure detection., Significance: Four-electrode montages for routine EEG monitoring are comparable to eight electrodes for seizure detection in the NICU., (Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: In mesial temporal lobe (mTL) epilepsy, seizure onset can precede the appearance of a scalp EEG ictal pattern by many seconds. The ability to identify this early, occult mTL seizure activity could improve lateralization and localization of mTL seizures on scalp EEG., Methods: Using scalp EEG spectral features and machine learning approaches on a dataset of combined scalp EEG and foramen ovale electrode recordings in patients with mTL epilepsy, we developed an algorithm, SCOPE-mTL, to detect and lateralize early, occult mTL seizure activity, prior to the appearance of a scalp EEG ictal pattern., Results: Using SCOPE-mTL, 73% of seizures with occult mTL onset were identified as such, and no seizures that lacked an occult mTL onset were identified as having one. Predicted mTL seizure onset times were highly correlated with actual mTL seizure onset times (r=0.69). 50% of seizures with early mTL onset were lateralizable prior to scalp ictal onset, with 94% accuracy., Conclusions: SCOPE-mTL can identify and lateralize mTL seizures prior to scalp EEG ictal onset, with high sensitivity, specificity, and accuracy., Significance: Quantitative analysis of scalp EEG can provide important information about mTL seizures, even in the absence of a visible scalp EEG ictal correlate., (Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)