Low Latency Real-Time Seizure Detection Using Transfer Deep Learning

• URL: http://arxiv.org/abs/2202.07796v1
• Date: Wed, 16 Feb 2022 00:03:00 GMT
• Title: Low Latency Real-Time Seizure Detection Using Transfer Deep Learning
• Authors: Vahid Khalkhali, Nabila Shawki, Vinit Shah, Meysam Golmohammadi, Iyad Obeid, Joseph Picone
• Abstract summary: Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure events. Most popular approaches to seizure detection using deep learning do not jointly model this information or require multiple passes over the signal, which makes the systems inherently non-causal. In this paper, we exploit both simultaneously by converting the multichannel signal to a grayscale image and using transfer learning to achieve high performance. The proposed system is trained end-to-end with only very simple pre- and postprocessing operations which are computationally lightweight and have low latency, making them conducive to clinical applications that require real-time processing. We have achieved a performance of 42.05% sensitivity with 5.78 false alarms per 24 hours on the development dataset of v1.5.2 of the Temple University Hospital Seizure Detection Corpus. On a single-core CPU operating at 1.7 GHz, the system runs faster than real-time (0.58 xRT), uses 16 Gbytes of memory, and has a latency of 300 msec.

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)
• Time Scale Network: A Shallow Neural Network For Time Series Data [18.46091267922322]
  Time series data is often composed of information at multiple time scales. Deep learning strategies exist to capture this information, but many make networks larger, require more data, are more demanding to compute, and are difficult to interpret. We present a minimal, computationally efficient Time Scale Network combining the translation and dilation sequence used in discrete wavelet transforms with traditional convolutional neural networks and back-propagation.
  arXiv  Detail & Related papers  (2023-11-10T16:39:55Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware [50.380319968947035]
  Recent deep learning approaches have reached accuracy in such tasks, but their implementation on conventional embedded solutions is still computationally very and energy expensive. We propose a new benchmark for computing tactile pattern recognition at the edge through letters reading. We trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihimorphic chip for efficient inference. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy
  arXiv  Detail & Related papers  (2022-05-30T14:30:45Z)
• Energy-Efficient Tree-Based EEG Artifact Detection [17.085570466000906]
  In epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform.
  arXiv  Detail & Related papers  (2022-04-19T12:57:26Z)
• 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)
• Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals [6.004134549265193]
  We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic.
  arXiv  Detail & Related papers  (2021-08-31T22:44:56Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning [52.77024349608834]
  We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors. We find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert.
  arXiv  Detail & Related papers  (2020-06-02T10:20:11Z)
• Near-chip Dynamic Vision Filtering for Low-Bandwidth Pedestrian Detection [99.94079901071163]
  This paper presents a novel end-to-end system for pedestrian detection using Dynamic Vision Sensors (DVSs) We target applications where multiple sensors transmit data to a local processing unit, which executes a detection algorithm. Our detector is able to perform a detection every 450 ms, with an overall testing F1 score of 83%.
  arXiv  Detail & Related papers  (2020-04-03T17:36:26Z)

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.