Integer Convolutional Neural Network for Seizure Detection

Truong, Nhan Duy; Nguyen, Anh Duy; Kuhlmann, Levin; Bonyadi, Mohammad Reza; Yang, Jiawei; Ippolito, Samuel J.; Kavehei, Omid

doi:10.1109/jetcas.2018.2842761

Cited by 62 publications

(31 citation statements)

References 16 publications

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“…To mimic an image-like classification, the CNNs are typically preceded by the Short-Time Fourier Transform (STFT) [16], [27]. In [27], a CNN network with STFT preprocessing is tested using full precision as well as with variable integer quantization, down to 1 bit, reaching up to 0.961 AUC on three datasets with intracranial and scalp electroencephalogram. An autoencoder with the same STFT is used in [16], reaching an accuracy of 94.37%.…”

Section: Related Workmentioning

confidence: 99%

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An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection

Burrello

Benatti

Schindler

et al. 2021

IEEE J. Biomed. Health Inform.

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We propose an intracranial electroencephalography (iEEG) based algorithm for detecting epileptic seizures with short latency, and with identifying the most relevant electrodes. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into an HD space where well-defined vector-space operations are used to construct prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states. Prototype vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes covering different brain regions. This flexibility allows our algorithm to identify the iEEG electrodes that discriminate best between ictal and interictal brain states. We assess our algorithm on the SWEC-ETHZ iEEG dataset that includes 99 short-time iEEG seizures recorded with 36 to 100 electrodes from 16 drug-resistant epilepsy patients. Using k-fold crossvalidation and all electrodes, our algorithm surpasses state-ofthe-art algorithms yielding significantly shorter latency (8.81 s vs. 11.57 s) in seizure onset detection, and higher specificity (97.31% vs. 94.84%) and accuracy (96.85% vs. 95.42%). We can further reduce the latency of our algorithm to 3.74 s by allowing a slightly higher percentage of false alarms (2% specificity loss). Using only the top 10% of the electrodes ranked by our algorithm, we still maintain superior latency, sensitivity, and specificity compared to the other algorithms with all the electrodes. We finally demonstrate the suitability of our algorithm to deployment on low-cost embedded hardware platforms, thanks to its robustness to noise/artifacts affecting the signal, its low computational complexity, and the small memory-footprint on a RISC-V microcontroller.

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Section: Related Workmentioning

confidence: 99%

“…STFT + CNN. We also consider a convolutional neural network (CNN) coupled with a short-time Fourier transform (STFT) used for seizure prediction [9] and detection [27]. The code used is provided by the authors.…”

Section: B Seizure Onset Detectionmentioning

confidence: 99%

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection

Burrello

Benatti

Schindler

et al. 2021

IEEE J. Biomed. Health Inform.

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“…As already stated, there is an imbalanced number of ictal and inter-ictal data, which is generally challenging for classifiers [26]. Truong et al [27] propose an approach to solve this problem by generating additional ictal data for training. Similar to the windowing process for the inter-ictal data a window of the dimension 23 × 256 is shifted over the ictal recordings.…”

Section: Data Structuringmentioning

confidence: 99%

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

et al. 2021

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The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly attractive option. For such implantable medical devices, efficient and low energy consumption, small size, and efficient processing architectures are essential. To meet these requirements, epileptic seizure detection by analysis and classification of brain signals with a convolutional neural network (CNN) is an attractive approach. This work presents a CNN for epileptic seizure detection capable of running on an ultra-low-power microprocessor. The CNN is implemented and optimized in MATLAB. In addition, the CNN is also implemented on a GAP8 microprocessor with RISC-V architecture. The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. After implementation of the CNN on the microcontroller, a sensitivity of 85% is reached. The classification of 1 s of EEG data takes t=35 ms and consumes an average power of P≈140 μW. The proposed detector outperforms related approaches in terms of power consumption by a factor of 6. The universal applicability of the proposed CNN based detector is verified with recording of epileptic rats. This results enable the design of future medical devices for epilepsy treatment.

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“…However, recent studies have investigated the use of CNNs for automatic analysis of brain data, including prediction of epileptic seizures, [18] epilepsy classification, [19] EEG artifact identification, [20] and detection of high-frequency EEG oscillations. [21] However, there has been limited work to date using CNNs for the identification and classification of seizure-like patterns in EEG, [22][23][24][25] grading the severity of HIE, [26] and in particular neonatal EEG seizure detection through multichannel EEG recordings. [14,15] The literature suggests that 1D time-series can also be directly fed into various formats of CNN architectures for seizure identification [23] and epilepsy detection.…”

Section: Cnns For Evaluation Of the Post-hi Eegmentioning

confidence: 99%

Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes

Abbasi

Gunn

Unsworth

2020

Advanced Intelligent Systems

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Hypoxic-ischemic (HI) insults before and during birth, secondary to events such as placental abruption or umbilical cord occlusion, are a significant contributor to neonatal brain injury (hypoxic-ischemic encephalopathy; HIE). [1,2] The preterm newborn is at greater risk of HIE. [1] In contrast to an overall incidence of 1-3/1000 live births at term in high-income countries, preterm babies born before 37 weeks have an HIE incidence of around 37.3/1000 babies born before 37 weeks of gestation, rising to an overall rate of HIE of 120/ 1000 in infants born before 28 weeks of gestation. [1,3] Survivors face lifelong neurodevelopmental problems, including learning and cognitive impairments, behavioral problems, cerebral palsy, and epilepsy. [4] The burden for individuals and their families and health and education economic costs are substantial. [4,5] To develop effective treatments or to appropriately utilize existing treatments requires that we fully understand how brain injury evolves and to identify biological markers (biomarkers) that allow us to determine phases of injury. Key to effective therapy is the knowledge that injury evolves in different phases over time-a latent phase of recovery of oxidative metabolism, which is followed by a secondary loss of cerebral energy metabolism during which time most brain cell injury occurs, followed by a tertiary phase of both repair and ongoing injury. [1,3,6] Therapeutic hypothermia (TH) is currently the only established treatment for HIE in babies born >36 weeks of gestation with moderate-severe HIE, and this treatment is now being cautiously explored for use in preterm babies. [1,2] The current clinical protocol for TH was based on our preclinical studies in term and preterm fetal sheep, which established that this therapy is only effective if started within 6 h after the end of an HI insult and continued for %3 days. [1-3] However, current clinical data show that many babies do not benefit from TH. [2] This in part reflects late recruitment of babies (typically 4-5 h) into treatment. However, it also reflects the fact that birth cannot always be taken as time zero. Many babies may also have experienced HI insults before birth, so that injury may have already evolved beyond the 6 h window of opportunity for TH efficacy.

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Integer Convolutional Neural Network for Seizure Detection

Cited by 62 publications

References 16 publications

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection

An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection With Automatic iEEG Electrode Selection

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network

Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes

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