论文标题
使用卷积神经网络预测睡眠质量
Predicting Sleeping Quality using Convolutional Neural Networks
论文作者
论文摘要
识别睡眠阶段和模式是诊断和治疗睡眠障碍的重要组成部分。随着智能技术的发展，可以轻松捕获与睡眠模式相关的传感器数据。在本文中，我们提出了一个卷积神经网络（CNN）体系结构，以改善分类性能。特别是，我们从不同的3个公开睡眠数据集中基于不同方法的分类性能，包括不同方法的分类性能，包括传统的机器学习方法，例如逻辑回归（LR），决策树（DT），K-Nearest邻居（K-NN），Naive Bayes（NB）和支持向量机（SVM）。报告了准确性，敏感性，特异性，精度，召回和F得分，并将作为将来在该方向模拟研究的基线。
Identifying sleep stages and patterns is an essential part of diagnosing and treating sleep disorders. With the advancement of smart technologies, sensor data related to sleeping patterns can be captured easily. In this paper, we propose a Convolution Neural Network (CNN) architecture that improves the classification performance. In particular, we benchmark the classification performance from different methods, including traditional machine learning methods such as Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbour (k-NN), Naive Bayes (NB) and Support Vector Machine (SVM), on 3 publicly available sleep datasets. The accuracy, sensitivity, specificity, precision, recall, and F-score are reported and will serve as a baseline to simulate the research in this direction in the future.
