用傅里叶逼近持久图特征提升鼾声信号睡眠分期准确率
Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves
- 用傅里叶展开替代赫米特展开，捕捉空气流量信号的持久性特征
- 在1155例儿童数据上，相比基线方法提升4.9%准确率
- 适合做无脑电睡眠分期的轻量化算法研究者参考
睡眠分期传统上依赖睡眠技师手动分析脑电图等生物信号。近年研究尝试基于受试者呼吸气流信号实现自动化分期。已有工作采用拓扑数据分析中的持久图赫米特函数展开（HEPC）进行特征提取，但有限阶数仅能捕获部分信息。本文提出傅里叶逼近持久图（FAPC）方法，结合XGBoost模型在1155例来自全国儿童医院睡眠数据库（NCHSDB）的儿科睡眠研究中评估性能。结果表明，FAPC可补充HEPC的不足，使分期准确率较基线方法提升4.9%。
原文摘要 · Abstract (English)
Sleep staging is a challenging task, typically manually performed by sleep technologists based on electroencephalogram and other biosignals of patients taken during overnight sleep studies. Recent work aims to leverage automated algorithms to perform sleep staging not based on electroencephalogram signals, but rather based on the airflow signals of subjects. Prior work uses ideas from topological data analysis (TDA), specifically Hermite function expansions of persistence curves (HEPC) to featurize airflow signals. However, finite order HEPC captures only partial information. In this work, we propose Fourier approximations of persistence curves (FAPC), and use this technique to perform sleep staging based on airflow signals. We analyze performance using an XGBoost model on 1155 pediatric sleep studies taken from the Nationwide Children's Hospital Sleep DataBank (NCHSDB), and find that FAPC methods provide complimentary information to HEPC methods alone, leading to a 4.9% increase in performance over baseline methods.
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