School of Electrical and Computer Engineering, Science, Engineering and Health, RMIT University, 376-392 Swanston Street, GPO Box 2476V, Melbourne, VIC, 3001, Australia.
Med Biol Eng Comput. 2010 Dec;48(12):1261-9. doi: 10.1007/s11517-010-0696-9. Epub 2010 Nov 3.
Sleep apnoea is a sleep breathing disorder which causes changes in cardiac and neuronal activity and discontinuities in sleep pattern when observed via electrocardiogram (ECG) and electroencephalogram (EEG). Using both statistical analysis and Gaussian discriminative modelling approaches, this paper presents a pilot study of assessing the cross-correlation between EEG frequency bands and heart rate variability (HRV) in normal and sleep apnoea clinical patients. For the study we used EEG (delta, theta, alpha, sigma and beta) and HRV (LF(nu), HF(nu) and LF/HF) features from the spectral analysis. The statistical analysis in different sleep stages highlighted that in sleep apnoea patients, the EEG delta, sigma and beta bands exhibited a strong correlation with HRV features. Then the correlation between EEG frequency bands and HRV features were examined for sleep apnoea classification using univariate and multivariate Gaussian models (UGs and MGs). The MG outperformed the UG in the classification. When EEG and HRV features were combined and modelled with MG, we achieved 64% correct classification accuracy, which is 2 or 8% improvement with respect to using only EEG or ECG features. When delta and acceleration coefficients of the EEG features were incorporated, then the overall accuracy improved to 71%.
睡眠呼吸暂停是一种睡眠呼吸障碍，当通过心电图（ECG）和脑电图（EEG）观察时，会导致心脏和神经元活动的变化，并导致睡眠模式的不连续。本文使用统计分析和高斯判别建模方法，对正常和睡眠呼吸暂停临床患者的脑电图频段与心率变异性（HRV）之间的交叉相关性进行了初步研究。在研究中，我们使用了来自频谱分析的 EEG（δ、θ、α、σ和β）和 HRV（LF（nu）、HF（nu）和 LF/HF）特征。不同睡眠阶段的统计分析表明，在睡眠呼吸暂停患者中，脑电图的δ、σ和β频段与 HRV 特征具有很强的相关性。然后使用单变量和多变量高斯模型（UG 和 MG）检查 EEG 频段与 HRV 特征之间的相关性，以进行睡眠呼吸暂停分类。MG 在分类中表现优于 UG。当 EEG 和 HRV 特征与 MG 结合并建模时，我们实现了 64%的正确分类准确率，与仅使用 EEG 或 ECG 特征相比，提高了 2 或 8%。当将 EEG 特征的δ和加速度系数纳入时，整体准确率提高到 71%。
