We propose a new algorithm for detecting epileptic seizures. 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 prototype vectors representing ictal (during seizures) and interictal (between seizures) brain states are constructed. These vectors can be computed at different spatial scales ranging from a single electrode up to many electrodes. This flexibility allows our algorithm to identify the 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 cross-validation and all electrodes, our algorithm surpasses state-of-the-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.

中文翻译：

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一组超维分类器：具有自动 iEEG 电极选择的硬件友好型短延迟癫痫发作检测

我们提出了一种检测癫痫发作的新算法。我们的算法首先提取三个特征，即平均幅度、线长度和局部二进制模式，这些特征被馈送到使用超维 (HD) 计算的分类器集合。这些特征被嵌入到代表发作期（癫痫发作期间）和发作间期（癫痫发作之间）大脑状态的原型向量中。这些向量可以在从单个电极到多个电极的不同空间尺度上计算。这种灵活性使我们的算法能够识别最能区分发作期和发作间期大脑状态的电极。我们在 SWEC-ETHZ iEEG 数据集上评估了我们的算法，该数据集包括 99 次短时 iEEG 癫痫发作，使用来自 16 名耐药性癫痫患者的 36 到 100 个电极记录。使用$千$ -折叠交叉验证和所有电极，我们的算法超越了最先进的算法，在癫痫发作检测中产生显着更短的延迟（8.81 秒对 11.57 秒），以及更高的特异性（97.31% 对 94.84%）和准确性（96.85% 对 95.42%）。通过允许更高百分比的误报（2% 的特异性损失），我们可以进一步将算法的延迟降低到 3.74 秒。仅使用我们的算法排名前 10% 的电极，与使用所有电极的其他算法相比，我们仍然保持卓越的延迟、灵敏度和特异性。我们最终证明了我们的算法适用于部署在低成本嵌入式硬件平台上，这要归功于它对影响信号的噪声/伪影的鲁棒性，它的低计算复杂度，