Relation between heart beat fluctuations and cyclic alternating pattern during sleep in insomnia patients.

Insomnia is a condition that affects the nervous and muscular system. Thirty percent of the population between 18 and 60 years suffers from insomnia. The effects of this disorder involve problems such as poor school or job performance and traffic accidents. In addition, patients with insomnia present changes in the cardiac function during sleep. Furthermore, the structure of electroencephalographic A-phases, which builds up the Cyclic Alternating Pattern during sleep, is related to the insomnia events. Therefore, the relationship between these brain activations (A-phases) and the autonomic nervous system would be of interest, revealing the interplay of central and autonomic activity during insomnia. With this goal, a study of the relationship between A-phases and heart rate fluctuations is presented. Polysomnography recording of five healthy subjects, five sleep misperception patients and five patients with psychophysiological insomnia were used in the study. Detrended Fluctuation Analysis (DFA) was used in order to evaluate the heart rate dynamics and this was correlated with the number of A-phases. The results suggest that pathological patients present changes in the dynamics of the heart rate. This is reflected in the modification of A-phases dynamics, which seems to modify of heart rate dynamics.

On separability of A-phases during the cyclic alternating pattern.

A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around the onset and offset of the A-phases were used to evaluate the separability between A-phases and basal sleep stage oscillations. In addition, a classifier was trained to separate the different A-phase types (A1, A2 and A3). Temporal, energy and complexity measures were used as descriptors for the classifier. The results show a percentage of separation between onset and preceding basal oscillations higher than 85 % for all A-phases types. For Offset separation from following baseline, the accuracy is higher than 80 % but specificity is around 75%. Concerning to A-phase type separation, A1-phase and A3-phase are well separated with accuracy higher than 80, while A1 and A2-phases show a separation lower than 50%. These results encourage the design of automatic classifiers for Onset detection and for separating among A-phases type A1 and A3. On the other hand, the A-phase Offsets present a smooth transition towards the basal sleep stage oscillations, and A2-phases are very similar to A1-phases, suggesting that a high uncertainty may exist during CAP annotation.

Characterization of the autonomic system during the cyclic alternating pattern of sleep.

Evaluation of the RR variability was carried out during the Cyclic Alternating Pattern (CAP) in sleep. CAP is a central phenomenon formed by short events called A-phases that break basal electroencephalogram (EEG) oscillations of the sleep stages. A-phases are classified in three types (A1, A2 and A3) based on the EEG desynchronization during A-phase. However, the relation of A-phases with other systems, such as cardiovascular system, is unclear and a deep analysis is required. For the study, six patients with Nocturnal Front Lobe Epilepsy (NFLE) and other six healthy controls patients underwent whole night polysomnographic recordings with CAP and hypnogram annotations. Amplitude reduction and time delay of the RR intervals minimum with respect to A-phases onset were computed. In addition, the same process was computed over randomly chosen RR interval segments during the NREM sleep for further comparison. The results suggest that the onset of the A-phases is correlated with a significative increase of the heart rate that peaks at around 4s after the Aphase onset, independently of the A-phase subtype.

Análisis multicanal de un sensor no obstructivo para la detección del Síndrome de Apnea-Hipopnea del Sueño / Multichannel Analysis of an Unobtrusive Sensor for Sleep Apnea-Hypopnea Detection

Este artículo presenta un método no obstructivo para la detección del síndrome de apnea-hipopnea del sueño (SAHS). El flujo respiratorio es medido indirectamente a través de un colchón sensorizado (PBS Pressure Bed Sensor) que incluye 8 transductores de presión. Mediante la transformada de Hilbert se obtiene la amplitud instantánea de las señales respiratorias y se reduce la información a través del análisis de componentes principales (ACP). Los eventos respiratorios (ERs apneas/hipopneas) se localizan como una reducción en la amplitud instantánea resultante y se contabilizan en el índice de eventos respiratorios (IER), un índice de severidad similar al oficial apnea-hypopnea index (AHI). El PBS se analiza agrupando primero la información de pares de canales y después utilizando los 8 canales. Los IER se evalúan comparándolos con el AHI en diferentes niveles de severidad. En el diagnóstico de pacientes sanos y patológicos se obtuvo una sensibilidad, especificidad y exactitud de 92%, 100% y 96% respectivamente, utilizando la información de dos u ocho canales. Con estos resultados podemos proponer el uso del PBS como una alternativa para el diagnóstico del SAHS en ambientes fuera del hospital, ya que no requiere la presencia de un clínico especialista para su uso.

This manuscript presents an unobtrusive method for sleep apneahypopnea syndrome (SAHS) detection. The airflow is indirectly measured through a sensitive mattress (Pressure Bed sensor, PBS) that incorporates multiple pressure sensors into a bed mattress. The instantaneous amplitude of each sensor signal is calculated through Hilbert transform, and then, the information is reduced via principal component analysis. The respiratory events (ERs -apneas/hypopneas) are detected as a reduction in the resulting instantaneous amplitude and accounted in the respiratory event index (IER), which is a severity indicator similar to the offcial apnea-hypopnea index (AHI). The respiratory signals extracted from PBS are analyzed first by clustering the information coming from channel pairs, and then using the eight channels. The IER performance is compared with the AHI for different severity categories. For the diagnosis of healthy and pathological patients we obtain a sensitivity, specificity and accuracy of 92%, 100% and 96%, respectively using two or eight PBS channels. These results suggest the possibility to propose PBS as an alternative tool for SAHS diagnosis in home environment.

Insomnia types and sleep microstructure dynamics.

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

Nonlinear analysis of the change points between A and B phases during the Cyclic Alternating Pattern under normal sleep.

This study analyzes the nonlinear properties of the EEG at transition points of the sequences that build the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon built up by consecutive sequences of activations and non-activations observed during the sleep time. The sleep condition can be evaluated from the patterns formed by these sequences. Eleven recordings from healthy and good sleepers were included in this study. We investigated the complexity properties of the signal at the onset and offset of the activations. The results show that EEG signals present significant differences (p<0.05) between activations and non-activations in the Sample Entropy and Tsallis Entropy indices. These indices could be useful in the development of automatic methods for detecting the onset and offset of the activations, leading to significant savings of the physician's time by simplifying the manual inspection task.

A new method to estimate abundances of multiple components using multi-spectral fluorescence lifetime imaging microscopy.

Multi-Spectral Fluorescent Lifetime Imaging Microscopy (m-FLIM) is a technique that aims to perform noninvasive in situ clinical diagnosis of several diseases. It measures the endogenous fluorescence of molecules, recording their lifetime decay in different wavelength bands. This signal is a mixed response of multiple fluorescent components present in a tissue sample. The goal is to decompose the mixture and estimate the proportional contributions of its constituents. Estimation of such quantitative description will help to characterize the molecular constitution of a given sample. This paper presents a new method to estimate the abundances of multiple components present in a mixture measured using m-FLIM data. It provides a closed-form solution under the fully constrained linear unmixing model and assuming the number of components as well as their ideal lifetime decays are known. Its performance is tested using synthetic samples with three components, where performance can be measured accurately and the percentage error is around 6%. The algorithm was also validated performing unmixing of ex vivo data samples from atherosclerotic human tissue containing collagen, elastin and low-density lipoproteins. These experiments were validated against ground-truth maps, which only give a quantitative description, and the estimated accuracy was around 88%.

Sleep-wake detection based on respiratory signal acquired through a pressure bed sensor.

This study proposes an automatic method for the sleep-wake staging in normal and pathologic sleep based only on respiratory effort acquired from a Pressure Bed Sensor (PBS). Motion and respiratory movements were obtained through a PBS and sleep-wake staging was evaluated from those time series. 20 all night polysomnographies, with annotations, used as gold standard and the time series coming from the PBS were used to develop and to evaluate the automatic wake-sleep staging. The database was built up by: 10 healthy subjects and 10 patients with severe sleep apnea. The agreement of the statistical measures between the automatic classification and the human scoring were: 83.59 ± 6.79 of sensitivity, 83.60 ± 15.13 of specificity and 81.91 ± 6.36 of accuracy. These results suggest that some important indexes, such as sleep efficiency, could be computed through a contactless technique.

A novel method to assist the detection of the Cyclic Alternating Pattern (CAP).

This study proposes a novel method to assist the detection of the components that build up the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon formed by consecutive sequences of activations (A1, A2, A3) and non-activations during nonREM sleep. The main importance of CAP evaluation is the possibility of defining the sleep process more accurately. Ten recordings from healthy and good sleepers were included in this study. The method is based on inferential statistics to define the initial and ending points of the CAP components based only on an initialization point given by the expert. The results show concordance up to 95% for A1, 85% for A2 and 60% for A3, together with an overestimation of 1.5 s in A1, 1.3 s in A2 and 0 s in A3. The total CAP rate presents a total underestimation of 7 min. Those results suggest that the method is able to accurately detect the initial and ending points of the activations, and may be helpful for the physicians by reducing the time dedicated to the manual inspection task.

Predicting EEG complexity from sleep macro and microstructure.

This work investigates the relation between the complexity of electroencephalography (EEG) signal, as measured by fractal dimension (FD), and normal sleep structure in terms of its macrostructure and microstructure. Sleep features are defined, encoding sleep stage and cyclic alternating pattern (CAP) related information, both in short and long term. The relevance of each sleep feature to the EEG FD is investigated, and the most informative ones are depicted. In order to quantitatively assess the relation between sleep characteristics and EEG dynamics, a modeling approach is proposed which employs subsets of the sleep macrostructure and microstructure features as input variables and predicts EEG FD based on these features of sleep micro/macrostructure. Different sleep feature sets are investigated along with linear and nonlinear models. Findings suggest that the EEG FD time series is best predicted by a nonlinear support vector machine (SVM) model, employing both sleep stage/transitions and CAP features at different time scales depending on the EEG activation subtype. This combination of features suggests that short-term and long-term history of macro and micro sleep events interact in a complex manner toward generating the dynamics of sleep.

Assessment of the EEG complexity during activations from sleep.

The present study quantitatively analyzes the EEG characteristics during activations (Act) that occur during NREM sleep, and constitute elements of sleep microstructure (i.e. the Cyclic Alternating Pattern). The fractal dimension (FD) and the sample entropy (SampEn) measures were used to study the different sleep stages and the Act that build up the sleep structure. Polysomnographic recordings from 10 good sleepers were analyzed. The complexity indexes of the Act were compared with the non-activation (NAct) periods during non-REM sleep. In addition, complexity measures among the different Act subtypes (A1, A2 and A3) were analyzed. A3 presented a quite similar complexity independently of the sleep stage, while A1 and A2 showed higher complexity in light sleep than during deep sleep. The current results suggest that Act present a hierarchic complexity between subtypes A3 (higher), A2 (intermediate) and A1 (lower) in all sleep stages.

Elastic registration based on particle filter in radiotherapy images with brain deformations.

This paper presents the evaluation of the accuracy of an elastic registration algorithm, based on the particle filter and an optical flow process. The algorithm is applied in brain CT and MRI simulated image datasets, and MRI images from a real clinical radiotherapy case. To validate registration accuracy, standard indices for registration accuracy assessment were calculated: the dice similarity coefficient (DICE), the average symmetric distance (ASD) and the maximal distance between pixels (Dmax). The results showed that this registration process has good accuracy, both qualitatively and quantitatively, suggesting that this method may be considered as a good new option for radiotherapy applications like patient's follow up treatment.

CAP sleep in insomnia: new methodological aspects for sleep microstructure analysis.

This work aims to propose new methodologies for the quantitative characterization of insomnia. Sleep microstructure, as expressed by Cyclic Alternatic pattern (CAP) sleep, is studied and differences between normal sleepers and insomniacs are investigated. The dynamic in the structure of CAP activation events is studied by use of wavelet analysis and the content of events, i.e. EEG dynamics, is studied in terms of complexity analysis. Both in structure and content, features exhibiting statistically significant differences are proposed, opening new perspectives for the understanding and the quantitative characterization of sleep and its disorders.

EEG complexity during sleep: on the effect of micro and macro sleep structure.

This work investigates the relation between EEG complexity measures, in particular Fractal Dimension and Sample Entropy, and sleep structure, in terms of both macrostructure, i.e. sleep stages, and microstructure, i.e. phase A activation of CAP sleep. Activation phases are compared with the non-activation periods of non-REM sleep. The study suggests that complexity features can serve as consistent descriptors of sleep dynamics and can potentially assist in the classification of sleep stages.

Long-term correlations and complexity analysis of the heart rate variability signal during sleep. Comparing normal and pathologic subjects.

BACKGROUND: Physiological sleep is characterized by different cyclic phenomena, such as REM, nonREM phases and the Cyclic Alternating Pattern (CAP), that are associated to characteristic patterns in the heart rate variability (HRV) signal. Disruption of such rhythms due to sleep disorders, for example insomnia or apnea syndrome, alters the normal sleep patterns and the dynamics of the HRV recorded during the night. OBJECTIVES: In this paper we analyze long-term and complexity dynamics of the HRV signal recorded during sleep in different groups of subjects. The aim is to investigate whether the calculated indices are able to capture the different characteristics and to discriminate among the groups of subjects, classified according sleep disorders or cardiovascular pathologies. METHODS: Parameters, able to detect the fractal-like behavior of a signal and to measure the regularity and complexity of a time series, are calculated on the HRV signal acquired during the night. Different groups of subjects were analyzed: healthy subjects with high sleep efficiency, healthy subjects with low sleep efficiency, subjects affected by insomnia, heart failure patients, subjects affected by obstructive sleep apnea. RESULTS: The evaluated parameters show significant differences in the groups of subjects considered in this work. In particular heart failure patients have significant lower entropy and complexity values, whereas apnea patients show an increased irregularity when compared with normal subjects with high sleep efficiency. CONCLUSIONS: This work proposes indices that can be used as global descriptors of the dynamics of the whole night and can discriminate among different groups of subjects.

Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis.

This study analyses two different methods to detect obstructive sleep apnea (OSA) during sleep time based only on the ECG signal. OSA is a common sleep disorder caused by repetitive occlusions of the upper airways, which produces a characteristic pattern on the ECG. ECG features, such as the heart rate variability (HRV) and the QRS peak area, contain information suitable for making a fast, non-invasive and simple screening of sleep apnea. Fifty recordings freely available on Physionet have been included in this analysis, subdivided in a training and in a testing set. We investigated the possibility of using the recently proposed method of empirical mode decomposition (EMD) for this application, comparing the results with the ones obtained through the well-established wavelet analysis (WA). By these decomposition techniques, several features have been extracted from the ECG signal and complemented with a series of standard HRV time domain measures. The best performing feature subset, selected through a sequential feature selection (SFS) method, was used as the input of linear and quadratic discriminant classifiers. In this way we were able to classify the signals on a minute-by-minute basis as apneic or nonapneic with different best-subset sizes, obtaining an accuracy up to 89% with WA and 85% with EMD. Furthermore, 100% correct discrimination of apneic patients from normal subjects was achieved independently of the feature extractor. Finally, the same procedure was repeated by pooling features from standard HRV time domain, EMD and WA together in order to investigate if the two decomposition techniques could provide complementary features. The obtained accuracy was 89%, similarly to the one achieved using only Wavelet analysis as the feature extractor; however, some complementary features in EMD and WA are evident.

Sleep staging classification based on HRV: time-variant analysis.

An algorithm to evaluate the sleep macrostructure based on heart rate fluctuations from ECG signal is presented. This algorithm is an attempt to evaluate the sleep quality out of sleep centers. The algorithm is made up by a) a time-variant autoregressive model used as feature extractor and b) a hidden Markov model used as classifier. Characteristics coming from the joint probability of HRV features were used to fed the HMM. 17 full polysomnography recordings from healthy subjects were used in the current analysis. When compared to Wake-NREM-REM given by experts, the automatic classifier achieved a total accuracy of 78.21+/-6.44% and a kappa index of 0.41+/-.1085 using two features and a total accuracy of 79.43+/-8.83% and kappa index of 0.42+/-.1493 using three features.

Automatic detection of sleep macrostructure based on bed sensors.

This study analyses the spectral components of the heart rate fluctuations of a new contact-less technology for sleep evaluation. Both heart beat interval (HBI) and movement activity were extracted from the multichannel ballistocardiographic (BCG) measurements, based on Emfit sensor foils placed into bed mattress. Powers spectral densities (PSD) of HBI have been compared with the ones obtained from the standard ECG during sleep stage 2. In addition, spectral features obtained from the contact-less technology and standard ECG has been used to automatically classify the sleep macrostructure through a time-varying autoregressive model and a Hidden Markov Model. Whole night recordings from six subjects were analyzed in this study. Spectral components did not show significant differences between the two measurements. Further, contactless technology achieved a total accuracy of 83 % and kappa index of 0.42, while standard ECG achieved an accuracy of 84 % and kappa index of 0.43 when compared to clinical sleep staging from polysomnography.

On arousal from sleep: time-frequency analysis.

Time-frequency analysis of the heart rate variability during arousal from sleep, with and without EMG activation, coming from five obese healthy subjects was performed. Additionally, a comparative analysis of three time-frequency distributions, smooth pseudo Wigner-Ville (SPWVD), Choi-Williams (CWD) and Born-Jordan distribution (BJD) is presented in this study. SPWVD showed higher capacity for eliminating the cross terms independently of the signal. After applying Hilbert transformation to real signals BJD and CWD lost some important mathematic properties as marginals, on the contrary PSWVD remains unchanged. BJD showed results comparable with CWD. During arousal episodes, analogous energy distribution and spectral indexes were obtained by the three time-frequency representations. Arousals with chin activity presented stronger changes in RR intervals and LF (related to sympathetic activity) component, being statistically different with respect to arousal without chin activity, only around the period of maximum change in beta activity on the EEG. These results suggest a more evident stress for the heart when an arousal is related to external muscular activity.

Automatic screening of Obstructive Sleep Apnea from the ECG based on Empirical Mode Decomposition and wavelet analysis.

This study proposes three different methods to evaluate Obstructive Sleep Apnea (OSA) during sleep time solely based on the ECG signal. OSA is a common sleep disorder produced by repetitive occlusions of the upper airways, which produces a characteristic pattern on the ECG. Extraction of ECG characteristics as the heart rate variability and the QRS peak area offer alternative measures for cheap, non-invasive and reliable pre-diagnosis of sleep apnea. 50 of the 70 recordings from the database of the Computers in Cardiology Challenge 2000, freely available on Physionet, have been used in this analysis, subdivided in a training and a testing set. We investigated the possibilities concerning the use of the recently proposed method Empirical Mode Decomposition in this application and compared it with the established Wavelet Analysis. From the results of these decompositions the eventual features were extracted, complemented with a series of standard HRV time domain measures and three extra non-linear measures. Of all features smoothed versions were calculated. From the obtained feature set, the best performing feature subset was used as the input of a Linear Discriminant Classifier. In this way we were able to classify the signal on a minute-by-minute basis as apneic or non-apneic with an accuracy of around 90% and to perfectly separate between apneic and normal patients, using around 20 to 40 features and with the possibility to do this in three alternative ways.