An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

• URL: http://arxiv.org/abs/2009.11245v2
• Date: Thu, 22 Oct 2020 14:22:30 GMT
• Title: An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
• Authors: Mohammadali Sharifshazileh (1 and 2), Karla Burelo (1 and 2), Johannes Sarnthein (2) and Giacomo Indiveri (1) ((1) Institute of Neuroinformatics, University of Zurich and ETH Zurich, (2) Klinik f\"ur Neurochirurgie, Universit\"atsSpital und Universit\"at Z\"urich)
• Abstract summary: We present a neuromorphic system that combines a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO) We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively) This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone. The device was fabricated using a standard 0.18$\mu$m CMOS technology node and has a total area of 99mm$^{2}$. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3$\mu$W. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing "neuromorphic intelligence" to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.

Related papers

• Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor [5.021433741823472]
  This study aims to detect interictal and ictal periods of epileptic seizures using a spiking neural network (SNN) Our proposed method has a high test accuracy of 93.3% and 92.9% when classifying ictal and interictal periods. 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.
  arXiv  Detail & Related papers  (2024-10-22T01:55:02Z)
• REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updates [54.96885726053036]
  This paper introduces a novel graph-based residual state update mechanism (REST) for real-time EEG signal analysis. By leveraging a combination of graph neural networks and recurrent structures, REST efficiently captures both non-Euclidean geometry and temporal dependencies within EEG data. Our model demonstrates high accuracy in both seizure detection and classification tasks.
  arXiv  Detail & Related papers  (2024-06-03T16:30:19Z)
• Artificial Neural Networks-based Real-time Classification of ENG Signals for Implanted Nerve Interfaces [7.335832236913667]
  We explore four types of artificial neural networks (ANNs) to extract sensory stimuli from the electroneurographic (ENG) signal measured in the sciatic nerve of rats. Different sizes of the data sets are considered to analyze the feasibility of the investigated ANNs for real-time classification. Our results show that some ANNs are more suitable for real-time applications, being capable of achieving accuracies over $90%$ for signal windows of $100$ and $200,$ms with a low enough processing time to be effective for pathology recovery.
  arXiv  Detail & Related papers  (2024-03-29T15:23:30Z)
• Neuro-BERT: Rethinking Masked Autoencoding for Self-supervised Neurological Pretraining [24.641328814546842]
  We present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
  arXiv  Detail & Related papers  (2022-04-20T16:48:18Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Functional Magnetic Resonance Imaging data augmentation through conditional ICA [44.483210864902304]
  We introduce Conditional Independent Components Analysis (Conditional ICA): a fast functional Magnetic Resonance Imaging (fMRI) data augmentation technique. We show that Conditional ICA is successful at synthesizing data indistinguishable from observations, and that it yields gains in classification accuracy in brain decoding problems.
  arXiv  Detail & Related papers  (2021-07-11T22:36:14Z)
• A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG [1.8464222520424338]
  High frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. We present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation.
  arXiv  Detail & Related papers  (2020-11-17T17:24:46Z)
• Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network [68.43026108936029]
  We propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment. We implement this algorithm in a real-time robotic system with a microphone array. The experiment results show a mean error azimuth of 13 degrees, which surpasses the accuracy of the other biologically plausible neuromorphic approach for sound source localization.
  arXiv  Detail & Related papers  (2020-07-07T08:22:56Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.