Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals

Abstract

A method for the detection of sleep apnoea, suitable for use in the home environment, is presented. The method automatically analyses night-time electrocardiogram (ECG) and oximetry recordings and identifies periods of normal and sleep-disordered breathing (SDB). The SDB is classified into one of six classes: obstructive, mixed and central apnoeas, and obstructive, mixed and central hypopnoeas. It also provides an estimated apnoea, hypopnoea and apnoea-hypopnoea index. The basis of the method is a pattern recognition system that identifies episodes of apnoea by analysing the heart variability, an ECG-derived respiration signal and blood oximetry values. The method has been tested on 183 subjects with a range of apnoea severities who have undergone a full overnight polysomnogram study. The results show that the method separates control subjects from subjects with clinically significant sleep apnoea with a specificity of 83 per cent and sensitivity of 95 per cent. These results demonstrate that home-based screening for sleep apnoea is a viable alternative to hospital-based tests with the added benefit of low cost and minimal waiting times.

Guidelines for the Recording and Evaluation of Pharmaco-Sleep Studies in Man: The International Pharmaco-EEG Society (IPEG)

“…For blood pressure, the systolic, diastolic and mean values need to be calculated per beat in order to detect rapid changes. Rapid changes are found with arousals and with pathologies [217]. Moderate variations are found with changing sleep stages.…”

“…Nevertheless, the ECG parameters derived even from a recording with a lower F S are still good enough to investigate heart rate variability changes with sleep stages, with arousals and sleep disorders. Many algorithms have been developed to recognise sleep-disordered breathing from the sleep ECG [216,217]. Heart rate variability analysis is recognised as a useful tool to calculate sympathetic and parasympathetic activity in an indirect way [218].…”

Usefulness of Artificial Neural Networks in the Diagnosis and Treatment of Sleep Apnea-Hypopnea Syndrome

“…However, the increasing research in the context of biomedical signal processing allows physicians to derive essential information directly from signals monitored during the PSG [44]. In this regard, blood oxygen saturation (SpO 2 ) from oximetry and heart rate variability (HRV) from electrocardiogram (ECG) are the most widely used.…”

Portable monitoring in sleep apnea

“…The pressure wave may be detected with the photoplethysmography used for oxygen saturation anyhow and can be used to detect all forms of respiratory events [31] and cardiovascular risk as associated with sleep apnea [32,33].…”

“…The derived respiratory curve is called ECG-derived respiration [42] and correlates with respiratory effort. This can be used to detect sleep-disordered breathing [31,43]. By combining ECGderived respiration and sleep apnea related heart rate variability a detection of sleep apnea is possible [44•].…”