A 41.2 nJ/class, 32-Channel On-Chip Classifier for Epileptic Seizure Detection

Taghavi, Milad; Haghi, Benyamin; Farivar, Masoud; Shoaran, Mahsa; Emami, Azita

doi:10.1109/embc.2018.8513243

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…In [25], Random Forest is the best performing among KNN, Naive-Bayes, and other classical methods using Empirical Wavelet Transform as preprocessing. Further, Random Forests are widely employed in dedicated hardware implementations, achieving classification energy as low as 41.2 nJ with a slightly lower score (88.1 % specificity, 83.7 % sensitivity) [31], [32].…”

Section: Related Workmentioning

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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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%

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.

View full text Add to dashboard Cite

show abstract

“…Due to severity of refractory epilepsy, open-source epileptic EEG datasets (both scalp and intracranial) are largely available, as well as established animal models for device validation and preclinical studies. Therefore, several groups have integrated various biomarkers and machine learning algorithms on ASIC for automated seizure detection [11], [12], [14], [73], [90]- [95] and for controlling an on-chip stimulator [13], [16], [17], [29], [88], [89].…”

Section: A Implants and Wearables For Epilepsymentioning

confidence: 99%

Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection

Zhu

Shin

Shoaran

2021

Preprint

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The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closedloop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware codesign enables the energy-and memory-optimized design of treebased models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

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“…BMIs are intended to operate as wireless, implantable systems that require low-power circuits, small physical size, wireless power delivery, and low temperature deltas (≤ 1 • C) [37,26,38]. By choosing efficient algorithms that map well to CMOS technologies, Application Specific Integrated Circuit (ASIC) implementations could offer substantial power and mobility benefits.…”

Section: Hardware Implementation Potentialmentioning

confidence: 99%

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

Haghi

Kellis

Shah

et al. 2019

Preprint

Self Cite

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We present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single-and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.Recently, nonlinear machine learning algorithms have shown promise in attaining high performance and robustness in BMIs. For instance, Wessberg et al.[5] apply a fully-connected neural network to neural data recorded from a monkey. Shpigelman et al. [6] show that a Gaussian kernel outperforms a linear kernel in a Kernel Auto-Regressive Moving Average (KARMA) algorithm when decoding 3D kinematics from macaque neural activity. Sussillo et al. [7] apply a large FORCE Dynamic Recurrent Neural Network (F-DRNN) on neural data recorded from the primary motor cortex in two monkeys, and then they test the stability of the model over multiple days [8]. Zhang et al. [9] and Schwemmer et al. [10] extract wavelet based features of motor cortex neural data of a human subject to classify intended hand movements by using a nonlinear support vector machine (SVM) and a large deep neural network, respectively. Hosman et al. [11] pass motor cortex neural firing rates to an LSTM and a Kalman filter to compare their performances for decoding intended cursor velocity of a human subject. These nonlinear learning-based decoders have shown more stability over multiple days and have improved performance compared to prior linear methods. However, they all have been applied to motor cortex data by mostly using neural firing rates as input features, which show more variability over long periods [2]. Recent work has demonstrated that neural activity in the posterior parietal cortex (PPC) can be used to support BMIs [12,13,14,15,16,17,18], although the encoding of movement kinematics appears to be complex. PPC processes a rich set of high-level aspects of movement including sensory integration, planning, an...

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A 41.2 nJ/class, 32-Channel On-Chip Classifier for Epileptic Seizure Detection

Cited by 10 publications

References 12 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

Closed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection

Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

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