These results indicate that the use of only ECG sensors can achieve good accuracies in the detection of sleep apnea. Moreover, the contamination level of each ECG segment can be used to automatically detect artefacts, and to highlight segments that require further visual inspection.

This paper proposes an approach to better estimate the sympathovagal balance (SB) and the respiratory sinus arrhythmia (RSA) after separating respiratory influences from the heart rate (HR). Methods: The separation is performed using orthogonal subspace projections and the approach is first tested using simulated HR and respiratory signals with different spectral properties. Then, RSA and SB are estimated during autonomic blockade and stress using the proposed approach and the classical heart rate variability (HRV) analysis. Both real and ECG-derived respiration (EDR) are used and the reliability of the EDR is evaluated. Results: Mean absolute percentage errors lower than 1% were obtained after removing previously known respiratory signals from simulated HR. The proposed indices were able to improve the quantification of SB during autonomic withdrawal. In the stress data, differences (p < 0.003) among relaxed and stressful phases were found with the proposed approach, using both the real respiration and the EDR, but they disappeared when using the classical HRV. Conclusion: A better assessment of the autonomic nervous system' response to pharmacological blockade and stress can be achieved after removing respiratory influences from HR, and this can be done using either the real respiration or the EDR. Significance: This work can be used to better identify vagal withdrawal and increased sympathetic activation when the classical HRV analysis fails due to the respiratory influences on HR. Furthermore, it can be computed using only the ECG, which is an advantage when developing wearable systems with limited number of sensors.

This paper presents a methodology to automatically screen for sleep apnea based on the detection of apnea and hypopnea events in the blood oxygen saturation (SpO) signal. It starts by detecting all desaturations in the SpO signal. From these desaturations, a total of 143 time-domain features are extracted. After feature selection, the six most discriminative features are used to construct classifiers to predict if desaturations are caused by respiratory events. From these, a random forest classifier yielded the best classification performance. The number of desaturations, classified as caused by respiratory events per hour of recording can then be used as an estimate of the apnea-hypopnea index (AHI), and to predict if a patient suffers from sleep apnea-hypopnea syndrome (SAHS) or not. All classifiers were developed based on a subset of 500 subjects of the Sleep Heart Health Study (SHHS) and tested on three different datasets, containing 8052 subjects in total. An averaged desaturation classification accuracy of 82.8 % was achieved over the different test sets. Subjects having SAHS with an AHI larger than 15 can be detected with an average accuracy of 87.6 %. The achieved SAHS screening outperforms SpO methods from the literature on the SHHS test dataset. Moreover, the robustness of the method was shown when tested on different independent test sets. These results show that an algorithm based on simple features of SpO desaturations can outperform more elaborate methods in the detection of apneic events and the screening of SAHS patients.