论文标题
基于节能树的脑电图检测
Energy-Efficient Tree-Based EEG Artifact Detection
论文作者
论文摘要
在癫痫监测的背景下，脑电图伪影通常由于振幅和频率的形态相似性而被误认为癫痫发作，从而使癫痫发作检测系统易受较高的错误警报率。在这项工作中，我们介绍了基于平行超低功率（PULP）嵌入式平台上数量最少的EEG通道的人工制品检测算法的实现。分析基于TUH EEG Artifact语料库数据集，并专注于时间电极。首先，我们使用自动化的机器学习框架在频域中提取最佳特征模型，达到93.95％的精度，4个颞eeg eeg通道设置的0.838 F1得分。所达到的准确性水平超过了近20％。然后，这些算法是并行化的，并针对纸浆平台进行了优化，与最先进的人工制品检测框架相比，能源有效的5.21倍提高。将该型号与低功率癫痫发作检测算法相结合，可以在300 mAh电池上进行300h的连续监控，并以可穿戴的外形和功率预算进行。这些结果为实施负担得起的，可穿戴的，长期的癫痫监测解决方案铺平了道路，其虚假阳性率和高灵敏度，满足患者和看护人的要求。
In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. 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. The analyses are based on the TUH EEG Artifact Corpus dataset and focus on the temporal electrodes. First, we extract optimal feature models in the frequency domain using an automated machine learning framework, achieving a 93.95% accuracy, with a 0.838 F1 score for a 4 temporal EEG channel setup. The achieved accuracy levels surpass state-of-the-art by nearly 20%. Then, these algorithms are parallelized and optimized for a PULP platform, achieving a 5.21 times improvement of energy-efficient compared to state-of-the-art low-power implementations of artifact detection frameworks. Combining this model with a low-power seizure detection algorithm would allow for 300h of continuous monitoring on a 300 mAh battery in a wearable form factor and power budget. These results pave the way for implementing affordable, wearable, long-term epilepsy monitoring solutions with low false-positive rates and high sensitivity, meeting both patients' and caregivers' requirements.
