The statistical distribution of wrist movements during sleep

A Temporal Threshold for Distinguishing Off-Wrist from Inactivity Periods: A Retrospective Actigraphy Analysis

(1) Background. To facilitate accurate actigraphy data analysis, inactive periods have to be distinguished from periods during which the device is not being worn. The current analysis investigates the degree to which off-wrist and inactive periods can be automatically identified. (2) Methods. In total, 125 actigraphy records were manually scored for ‘off-wrist’ and ‘inactivity’ (99 collected with the Motionlogger AMI, 26 (sampling frequency of 60 (n = 20) and 120 (n = 6) s) with the Philips Actiwatch 2.) Data were plotted with cumulative frequency percentage and analyzed with receiver operating characteristic curves. To confirm findings, the thresholds determined in a subset of the Motionlogger dataset (n = 74) were tested in the remaining dataset (n = 25). (3) Results. Inactivity data lasted shorter than off-wrist periods, with 95% of inactive events being shorter than 11 min (Motionlogger), 20 min (Actiwatch 2; 60 s epochs) or 30 min (Actiwatch 2; 120 s epochs), correctly identifying 35, 92 or 66% of the off-wrist periods. The optimal accurate detection of both inactive and off-wrist periods for the Motionlogger was 3 min (Youden’s Index (J) = 0.37), while it was 18 (J = 0.89) and 16 min (J = 0.81) for the Actiwatch 2 (60 and 120 s epochs, respectively). The thresholds as determined in the subset of the Motionlogger dataset showed similar results in the remaining dataset. (4) Conclusion. Off-wrist periods can be automatically identified from inactivity data based on a temporal threshold. Depending on the goal of the analysis, a threshold can be chosen to favor inactivity data’s inclusion or accurate off-wrist detection.

Dynamics and Ultradian Structure of Human Sleep in Real Life

The temporal dynamics that characterize sleep are difficult to capture outside the sleep laboratory. Therefore, longitudinal studies and big-data approaches assessing sleep dynamics are lacking. Here, we present the first large-scale analysis of human sleep dynamics in real life by making use of longitudinal wrist movement recordings of >16,000 sleep bouts from 573 subjects. Through non-linear conversion of locomotor activity to "Locomotor Inactivity During Sleep" (LIDS), movement patterns are exposed that directly reflect ultradian sleep cycles and replicate the dynamics of laboratory sleep parameters. Our current analyses indicate no sex differences in LIDS-derived sleep dynamics, whereas especially age but also shift work have pronounced effects, specifically on decline rates and ultradian amplitude. In contrast, ultradian period and phase emerged as remarkably stable across the tested variables. Our approach and results provide the necessary quantitative sleep phenotypes for large field studies and outcome assessments in clinical trials.

Automatic detection of wakefulness and rest intervals in actigraphic signals: a data-driven approach

Actigraphy is an useful tool for evaluating the activity pattern of a subject; activity registries are usually processed by first splitting the signal into its wakefulness and rest intervals and then analyzing each one in isolation. Consequently, a preprocessing stage for such a splitting is needed. Several methods have been reported to this end but they rely on parameters and thresholds which are manually set based on previous knowledge of the signals or learned from training. This compromises the general applicability of this methods. In this paper we propose a new method in which thresholds are automatically set based solely on the specific registry to be analyzed. The method consists of two stages: (1) estimation of an initial classification mask by means of the expectation maximization algorithm and (2) estimation of a final refined mask through an iterative method which re-estimates both the mask and the classifier parameters at each iteration step. Results on real data show that our methodology outperforms those so far proposed and can be more effectively used to obtain derived sleep quality parameters from actigraphy registries.

Compression of the optic chiasm is associated with permanent shorter sleep duration in patients with pituitary insufficiency

OBJECTIVE

Patients with pituitary insufficiency often experience some degree of impaired sleep. Sleep-wake rhythm is regulated to a large extent by the suprachiasmatic nucleus (SCN). Because the SCN is located just superior to the optic chiasm, we hypothesized that a history of compression of the optic chiasm (CC) due to a tumour with suprasellar extension is associated with altered sleep patterns in patients with pituitary insufficiency.

DESIGN

Case-control study.

PATIENTS

We studied 38 patients (mean age 55·7 ± 13·1 years; 71·1% men) with CC and 18 patients (mean age 53·3 ± 16·6 years, 38·9% men) without CC.

MEASUREMENTS

Objective measures of sleep patterns were assessed by wrist actigraphy. Validated sleep questionnaires were used to evaluate subjective sleep parameters.

RESULTS

Objective total sleep duration was 36 min shorter in patients with CC than in patients without CC [454 (295-553) vs 490 (432-740) min, P = 0·034]. Moreover, patients with CC had a later habitual bedtime [23:15 (22:30-03:00) vs 22:55 (20:00-02:00) h, P = 0·044] and a later actigraphic sleep onset [23:57 (22:31-01:33) vs 23·16 (19:47-03:04) h, P = 0·020]. Linear regression analysis confirmed the difference in total sleep duration after adjustment for age, sex, body mass index, cranial radiotherapy and pituitary/hypothalamic surgery. Subjective sleep parameters were similar in both groups.

CONCLUSIONS

Compression of the optic chiasm due to a tumour with suprasellar extension is associated with permanent changes in total sleep duration in patients with pituitary insufficiency.

Automatic annotation of actigraphy data for sleep disorders diagnosis purposes

The diagnosis of Sleep disorders, highly prevalent in the western countries, typically involves sophisticated procedures and equipments that are intrusive to the patient. Wrist actigraphy, on the contrary, is a non-invasive and low cost solution to gather data which can provide valuable information in the diagnosis of these disorders. The acquired data may be used to infer the Sleep/Wakefulness (SW) state of the patient during the circadian cycle and detect abnormal behavioral patterns associated with these disorders. In this paper a classifier based on Autoregressive (AR) model coefficients, among other features, is proposed to estimate the SW state. The real data, acquired from 23 healthy subjects during fourteen days each, was segmented by expert medical personal with the help of complementary information such as light intensity and Sleep e-Diary information. Monte Carlo tests with a Leave-One-Out Cross Validation (LOOCV) strategy were used to assess the performance of the classifier which achieves an accuracy of 96%.

Statistical characterization of actigraphy data during sleep and wakefulness states

Human activity can be measured with actimetry sensors used by the subjects in several locations such as the wrists or legs. Actigraphy data is used in different contexts such as sports training or tele-medicine monitoring. In the diagnosis of sleep disorders, the actimetry sensor, which is basically a 3D axis accelerometer, is used by the patient in the non dominant wrist typically during an entire week. In this paper the actigraphy data is described by a weighted mixture of two distributions where the weight evolves along the day according to the patient circadian cycle. Thus, one of the distributions is mainly associated with the wakefulness state while the other is associated with the sleep state. Actigraphy data, acquired from 20 healthy patients and manually segmented by trained technicians, is used to characterize the acceleration magnitude during sleep and wakefulness states. Several mixture combinations are tested and statistically validated with conformity measures. It is shown that both distributions can co-exist at a certain time with varying importance along the circadian cycle.