Abstract
Recent work in unobtrusive sleep/wake classification has shown that cardiac and respiratory features can help improve classification performance. Nevertheless, actigraphy remains the single most discriminative modality for this task. Unfortunately, it requires the use of dedicated devices in addition to the sensors used to measure electrocardiogram (ECG) or respiratory effort. This paper proposes a method to estimate actigraphy from the body movement artifacts present in the ECG and respiratory inductance plethysmography (RIP) based on the time-frequency analysis of those signals. Using a continuous wavelet transform to analyze RIP, and ECG and RIP combined, it provides a surrogate measure of actigraphy with moderate correlation (for ECG+RIP, , p < 0.001) and agreement (mean bias ratio of 0.94 and 95% agreement ratios of 0.11 and 8.45) with reference actigraphy. More important, it can be used as a replacement of actigraphy in sleep/wake classification: after cross-validation with a data set comprising polysomnographic (PSG) recordings of 15 healthy subjects and 25 insomniacs annotated by an external sleep technician, it achieves a statistically non-inferior classification performance when used together with respiratory features (average κ of 0.64 for 15 healthy subjects, and 0.50 for a dataset with 40 healthy and insomniac subjects), and when used together with respiratory and cardiac features (average κ of 0.66 for 15 healthy subjects, and 0.56 for 40 healthy and insomniac subjects). Since this method eliminates the need for a dedicated actigraphy device, it reduces the number of sensors needed for sleep/wake classification to a single sensor when using respiratory features, and to two sensors when using respiratory and cardiac features without any loss in performance. It offers a major benefit in terms of comfort for long-term home monitoring and is immediately applicable for legacy ECG and RIP monitoring devices already used in clinical practice and which do not have an accelerometer built-in.
Highlights
Recent years have seen significant advances on unobtrusive sleep measurements, overnight polysomnographic recordings (PSG) by expert technicians in dedicated laboratories remain the gold standard for sleep medicine (Iber et al 2007)
This paper proposes a method to estimate actigraphy from the body movement artifacts present in the ECG and respiratory inductance plethysmography (RIP) based on the time-frequency analysis of those signals
It can be used as a replacement of actigraphy in sleep/wake classification: after cross-validation with a data set comprising polysomnographic (PSG) recordings of 15 healthy subjects and 25 insomniacs annotated by an external sleep technician, it achieves a statistically non-inferior classification performance when used together with respiratory features, and when used together with respiratory and cardiac features
Summary
Recent years have seen significant advances on unobtrusive sleep measurements, overnight polysomnographic recordings (PSG) by expert technicians in dedicated laboratories remain the gold standard for sleep medicine (Iber et al 2007). Laboratory facilities, dedicated equipment and qualified personnel are very expensive; in addition, PSG is uncomfortable and can have a negative impact on normal sleep, being basically impossible to perform on long-term beyond one or two consecutive nights All these factors motivated research in the area of unobtrusive sleep monitoring which can be used in a home setting. The amount of body movements is quantified into so-called ‘activity counts’ in epochs of fixed size (typically 30 s), which are used to estimate whether the subject was awake or asleep during that period (Cole et al 1992) It has been indicated by the American Academy of Sleep Medicine (AASM) as a valid auxiliary method to evaluate patients with circadian disorders and sleep-wake disturbances, and to evaluate their response to treatments of insomnia and circadian disorders (Morgenthaler et al 2007)
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