用于识别体外微电极阵列记录的癫痫样模式的时间序列分割
Time series segmentation for recognition of epileptiform patterns recorded via Microelectrode Arrays in vitro
AI总结：
本文提出两种轻量级时间序列分割算法ZdensityRODE和AMPDE，用于从微电极阵列记录的局部场电位中识别癫痫样事件，在发作期检测上取得高精度和召回率，并支持微调用于发作间期检测和癫痫预测。
摘要：
癫痫是一种常见的神经系统疾病，影响全球约1%的人口。大约30-40%的患者对药物治疗无反应，导致其生活质量受到显著负面影响。闭环深部脑刺激（DBS）是对药物治疗无反应个体的一种有前景的治疗方法。为了实现有效的癫痫发作控制，算法在从局部场电位（LFPs）中识别相关电生理生物标志物以确定最佳刺激时机方面发挥着重要作用。在这方面，从持续脑活动中检测和分类事件，同时通过计算成本低廉的实现实现低功耗，是该领域的一个重大挑战。为了应对这一挑战，我们在此提出两种轻量级算法，ZdensityRODE和AMPDE，通过利用语义分割从LFPs中识别相关事件，语义分割涉及从LFP及其相关事件中提取不同层次的信息。作为案例研究，这些算法的性能针对由4-氨基吡啶在小鼠海马-皮层（CTX）切片中诱导并经微电极阵列记录的癫痫样活动进行了验证。ZdensityRODE算法在发作期事件检测中展示了93%的精确率和召回率，在发作间期事件检测中精确率为42%，而AMPDE算法在发作期事件检测中达到了96%的精确率和90%的召回率，在发作间期事件检测中精确率为54%。虽然这些算法最初专门针对发作期活动的检测进行训练，但它们可以进行微调以改善发作间期检测，从而瞄准癫痫发作预测。我们的结果表明，这些算法能够有效捕捉癫痫样活动；它们的轻量化为实时癫痫发作检测以及癫痫发作预测和控制开辟了新的可能性。
英文摘要：
Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Around 30-40% of patients do not respond to pharmacological treatment, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy. To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. In this regard, the detection and classification of events from ongoing brain activity, while achieving low power through computationally unexpensive implementations, represents a major challenge in the field. To address this challenge, we here present two lightweight algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing semantic segmentation, which involves extracting different levels of information from the LFP and relevant events from it. The algorithms performance was validated against epileptiform activity induced by 4-minopyridine in mouse hippocampus-cortex (CTX) slices and recorded via microelectrode array, as a case study. The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. While initially trained specifically for detection of ictal activity, these algorithms can be fine-tuned for improved interictal detection, aiming at seizure prediction. Our results suggest that these algorithms can effectively capture epileptiform activity; their light weight opens new possibilities for real-time seizure detection and seizure prediction and control.
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