An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

Sharifshazileh, Mohammadali; Burelo, Karla; Sarnthein, Johannes; Indiveri, Giacomo

doi:10.1038/s41467-021-23342-2

Cited by 101 publications

(83 citation statements)

References 84 publications

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“…Guo et al [ 45 ] developed a hypergraph-based detector to automatically detect HFOs in 2021, which achieved an accuracy of 90.7%, a sensitivity of 80.9%, and a specificity of 96.9%. What is more, in 2021, Sharifshazileh et al [ 46 ] presented a neuromorphic system that combines a neural recording headstage with a spiking neural network, which achieved a high sensitivity of 100% but a very low specificity of 33% and an accuracy of 78%.…”

Section: Resultsmentioning

confidence: 99%

“…In early studies, researchers mostly focused on the performance of signal detection, so in terms of dividing experimental data, most of them randomly divide training set and test set from the candidate pool [ 21 , 27 , 43 , 45 , 46 ]. In clinical application, when considering a new patient, it is desirable to transfer the a priori knowledge learned from previous existing cases to the judgment of the new patient.…”

Section: Discussionmentioning

confidence: 99%

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[Retracted] Automatic Detection of High‐Frequency Oscillations Based on an End‐to‐End Bi‐Branch Neural Network and Clinical Cross‐Validation

Liu

Wei

Wang

et al. 2021

Computational Intelligence and Neuroscience

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Accurate identification of high-frequency oscillation (HFO) is an important prerequisite for precise localization of epileptic foci and good prognosis of drug-refractory epilepsy. Exploring a high-performance automatic detection method for HFOs can effectively help clinicians reduce the error rate and reduce manpower. Due to the limited analysis perspective and simple model design, it is difficult to meet the requirements of clinical application by the existing methods. Therefore, an end-to-end bi-branch fusion model is proposed to automatically detect HFOs. With the filtered band-pass signal (signal branch) and time-frequency image (TFpic branch) as the input of the model, two backbone networks for deep feature extraction are established, respectively. Specifically, a hybrid model based on ResNet1d and long short-term memory (LSTM) is designed for signal branch, which can focus on both the features in time and space dimension, while a ResNet2d with a Convolutional Block Attention Module (CBAM) is constructed for TFpic branch, by which more attention is paid to useful information of TF images. Then the outputs of two branches are fused to realize end-to-end automatic identification of HFOs. Our method is verified on 5 patients with intractable epilepsy. In intravalidation, the proposed method obtained high sensitivity of 94.62%, specificity of 92.7%, and F1-score of 93.33%, and in cross-validation, our method achieved high sensitivity of 92.00%, specificity of 88.26%, and F1-score of 89.11% on average. The results show that the proposed method outperforms the existing detection paradigms of either single signal or single time-frequency diagram strategy. In addition, the average kappa coefficient of visual analysis and automatic detection results is 0.795. The method shows strong generalization ability and high degree of consistency with the gold standard meanwhile. Therefore, it has great potential to be a clinical assistant tool.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

[Retracted] Automatic Detection of High‐Frequency Oscillations Based on an End‐to‐End Bi‐Branch Neural Network and Clinical Cross‐Validation

Liu

Wei

Wang

et al. 2021

Computational Intelligence and Neuroscience

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“…These attributes make them useful for recent signal collectors like wearable EEG. On the other hand, although they have shown to be highly scalable and adaptable, their high cost per bit is a major pitfall [103,104].…”

Section: Spiking Neural Network: Artificial Neural Network As a Model...mentioning

confidence: 99%

Generative Models of Brain Dynamics -- A review

Panahi¹,

Abrevaya²,

Gagnon-Audet³

et al. 2021

Preprint

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The principled design and discovery of biologically-and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, datadriven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesisand data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.

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“…The choice of these algorithms was also motivated by the commercial availability of specialized hardware tailored for implementing these algorithms. Based on neuromorphic architecture [32], this hardware has been engineered to improve the accuracy of pattern recognition and, more importantly, decrease the elapsed time between signal input and the output of results, and has been used recently by many researchers [33]. We use NeuroStack [34] board from General Vision (Petaluma, CA, USA) for our application, which has multiple neuromorphic chips and enables multiple such boards to be daisy-chained, significantly increasing its ability for pattern learning.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG

Ambati

Raja

Alhameed

et al. 2022

Sensors

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Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.

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An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

Cited by 101 publications

References 84 publications

[Retracted] Automatic Detection of High‐Frequency Oscillations Based on an End‐to‐End Bi‐Branch Neural Network and Clinical Cross‐Validation

[Retracted] Automatic Detection of High‐Frequency Oscillations Based on an End‐to‐End Bi‐Branch Neural Network and Clinical Cross‐Validation

Generative Models of Brain Dynamics -- A review

Neuromorphic Architecture Accelerated Automated Seizure Detection in Multi-Channel Scalp EEG

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