用知识增强的深度学习模型，精准识别渐冻症神经电活动特征。
FRAME-C: A knowledge-augmented deep learning pipeline for classifying multi-electrode array electrophysiological signals
- 融合手工特征与深度学习，从电极阵列数据中自动提取关键信号
- 真实数据分类准确率提升超11%，模拟数据最高提升25%
- 可分析特征重要性，帮助理解渐冻症的电生理表型
肌萎缩侧索硬化症（ALS）是一种致命的神经退行性疾病，以运动神经元退化为特征，神经兴奋性变化是关键指标。近年来，诱导多能干细胞（iPSC）技术使得人源iPSC衍生神经元培养成为可能，结合多电极阵列（MEA）电生理记录，可获取丰富的时空电生理数据。传统方法依赖基于不完善领域知识的手工特征，虽有用但难以捕捉全部数据特征。深度学习可从原始数据中自动学习特征，减少对人工特征的依赖。然而，手工特征在编码领域知识和提升可解释性方面仍至关重要，尤其在数据有限或噪声较多时。本研究提出FRAME-C——一种知识增强的机器学习流程，结合领域知识、原始尖峰波形数据与深度学习技术，用于分类MEA信号并识别ALS特异性表型。FRAME-C利用深度学习从尖峰波形中学习关键特征，同时保留如尖峰幅度、峰间间隔、尖峰持续时间等手工特征，以保持重要时空信息。我们在模拟与真实人类iPSC来源神经元培养的MEA数据上验证了FRAME-C，性能显著优于现有方法：真实数据提升超11%，模拟数据最高提升25%。此外，该模型可评估手工特征的重要性，揭示ALS表型的关键机制。
原文摘要 · Abstract (English)
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by motor neuron degeneration, with alterations in neural excitability serving as key indicators. Recent advancements in induced pluripotent stem cell (iPSC) technology have enabled the generation of human iPSC-derived neuronal cultures, which, when combined with multi-electrode array (MEA) electrophysiology, provide rich spatial and temporal electrophysiological data. Traditionally, MEA data is analyzed using handcrafted features based on potentially imperfect domain knowledge, which while useful may not fully capture all useful characteristics inherent in the data. Machine learning, particularly deep learning, has the potential to automatically learn relevant characteristics from raw data without solely relying on handcrafted feature extraction. However, handcrafted features remain critical for encoding domain knowledge and improving interpretability, especially with limited or noisy data. This study introduces FRAME-C, a knowledge-augmented machine learning pipeline that combines domain knowledge, raw spike waveform data, and deep learning techniques to classify MEA signals and identify ALS-specific phenotypes. FRAME-C leverages deep learning to learn important features from spike waveforms while incorporating handcrafted features such as spike amplitude, inter-spike interval, and spike duration, preserving key spatial and temporal information. We validate FRAME-C on both simulated and real MEA data from human iPSC-derived neuronal cultures, demonstrating superior performance over existing classification methods. FRAME-C shows over 11% improvement on real data and up to 25% on simulated data. We also show FRAME-C can evaluate handcrafted feature importance, providing insights into ALS phenotypes.
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