Arousal Detection Research Articles

Identification of Electroencephalographic Arousals in Multichannel Sleep Recordings

Electroencephalographic arousals are defined as abrupt shifts in electroencephalogram (EEG) frequency during sleep. Occurrence of arousals results in fragmented sleep, being one of the most important causes of daytime sleepiness among sleep disorders. Detection of arousals requires a polysomnographic (PSG) recording to be made during the patient's sleep. The resulting PSG is then analyzed offline by the physician. This is a time-consuming task, hence, automation of this process is pursued. The analysis, which involves the correlation of various events in time occurring among the different channels, in conjunction with the complexity of the related biomedical signals, makes this task also difficult to achieve in the computer algorithm. In this paper, we present a method for the detection of EEG arousals working on multichannel PSGs. The algorithm detects arousals using the information available through two EEG channels and the electromyography. A signal-processing technique is first proposed for the analysis of biomedical signals and extraction of relevant information. Individual events are detected from the signals and subsequently are related in time. Finally, a classification phase carries out the final decision on the presence of the event. Classifiers based on Fisher's linear and quadratic discriminants, support vector machines and artificial neural networks are compared at this phase. Experiments conducted on 20 patients reported a sensitivity and specificity respectively of 0.86 and 0.76 in the detection of the arousal events.

Processing of Signals Recorded Through Smart Devices: Sleep-Quality Assessment

In this paper, we discuss the possibility of performing a sleep evaluation from signals, which are not usually used for this purpose. In particular, we take into consideration the heart rate variability (HRV) and respiratory signals for automatic sleep staging, arousals detection, and apnea recognition. This is particularly useful for wearable or textile devices that could be employed for home monitoring of sleep. The HRV and the respiration were analyzed in the frequency domain, and the statistics on the spectral and cross-spectral parameters put into evidence the possibility of a sleep evaluation on their basis. Comparison with traditional polysomnography (PSG) revealed a classification accuracy of 89.9% in rapid eye movement (REM) non-REM sleep separation and an accuracy of 88% for sleep apnea detection. Additional information can be achieved from the number of microarousals recognized in correspondence of typical modifications in the HRV signal. The obtained results support the idea of automatic sleep evaluation and monitoring through signals that are not traditionally used in clinical PSG, but can be easily recorded at home through wearable devices (for example, a sensorized T-shirt) or systems integrated into the environment (a sensorized bed). This is a first step for the development of systems for sleep screening on large populations that can constitute a complement for the traditional clinical evaluation.

Automatic EEG arousal detection for sleep apnea syndrome

Electroencephalographic (EEG) arousals are seen in EEG recordings as an awakening response of the human brain. Sleep apnea is a serious sleep disorder. Severe sleep apnea brings about EEG arousals and sleep for patients with sleep apnea syndrome (SAS) is thus frequently interrupted. The number of respiratory-related arousals during the whole night on PSG recordings is directly related to the quality of sleep. Detecting EEG arousals in the PSG record is thus a significant task for clinical diagnosis in sleep medicine. In this paper, a method for automatic detection of EEG arousals in SAS patients was proposed. To effectively detect respiratory-related arousals, threshold values were determined according to pathological events as sleep apnea and electromyogram (EMG). If resumption of ventilation (end of the apnea interval) was detected, much lower thresholds were adopted for detecting EEG arousals, including relatively doubtful arousals. Conversely, threshold was maintained high when pathological events were undetected. The proposed method was applied to polysomnographic (PSG) records of eight patients with SAS and accuracy of EEG arousal detection was verified by comparative visual inspection. Effectiveness of the proposed method in clinical diagnosis was also investigated.

A comparison of the ability of the EEGo and BIS monitors to assess emergence following neurosurgery

Deep anesthesia during microvascular decompression (MVD) for trigeminal neuralgia and cerebral aneurysm clipping may delay emergence. A new electroencephalographic (EEG) monitor, the EEGo, processes a raw EEG signal using time-delay analysis to display a reproducible signal transition from deep anesthesia through the excitement state to the awake state. We hypothesized that the EEGo monitor would be superior to the bispectral (BIS) monitor, not only in aiding emergence but also in detecting sudden changes in levels of hypnosis. Twenty-one patients undergoing neurosurgery were studied (16 MVD, 5 cerebral aneurysm clipping). Each patient had both EEGo and BIS monitoring with only one monitor available for viewing by the anesthesiologist. The anesthetic was titrated based on the available monitor, and the time to emergence was measured. Intraoperative detection of arousal and the timing of burst suppression during propofol administration were also examined. In the MVD patients, there was no statistical difference in wake-up times between the EEGo and BIS groups. Additionally, there were no episodes of intraoperative awareness and no differences in patient satisfaction. Compared to EEGo waveform output, a decrease in BIS output was delayed in four patients receiving propofol for burst suppression during cerebral aneurysm clipping, indicating enhanced hypnosis. One case of intraoperative arousal, which occurred at a BIS reading of 43 arbitrary BIS units, was detected earlier with the EEGo. While the EEGo was faster than the BIS at indicating planned changes in levels of hypnosis throughout propofol administration prior to temporary clipping during aneurysm surgery, the EEGo was not superior to the BIS monitor in facilitating a more rapid emergence following neurosurgery.

Data mining techniques for detection of sleep arousals

Arousals are considered one of the main causes of daytime sleepiness. They impede the proper flow of sleep cycles and cause weariness. Manual scoring of arousals is time-consuming, requires expert knowledge, and has high inter-scorer variability. A major difficulty in detecting arousals automatically is the existing variance across patients. Based on data mining techniques, we present a different approach to the automatic detection of arousals that overcomes the hurdle of differences in signal characteristics across patients. Offline we used a training-set of adult patients to define a set of general rules to detect arousals (termed meta-rules). This was done by analyzing the correlations between occurrences of arousals and the EEG, EMG, pulse and SaO2 signals as follows: (1) each signal was mathematically projected into several spaces (termed projected-signals); (2) from each such projected-signal, the algorithm extracted time points that indicated meaningful changes (termed critical-points); (3) data mining techniques were applied to all the critical-points to discover patterns of repeating behavior; (4) classes of patterns which were highly correlated with manually scored arousals were formalized as meta-rules. Online we used a test-set of adult patients from two other different sleep laboratories. Using the meta-rules, the algorithm extracted individual rules for each patient (termed actual-rules), and used them to automatically detect the patients’ arousals. These arousals were significantly correlated ( R = 0.88, p < 0.0001; sensitivity = 75.2%, positive predictive value = 76.5%) with those detected manually by experts. Since the total number of arousals is a measure of sleep quality, this algorithm constitutes a novel approach to automatically estimate sleep quality.

Irregular Respiration as a Marker of Wakefulness During Titration of CPAP

Regularity of respiration is characteristic of stable sleep without sleep disordered breathing. Appearance of respiratory irregularity may indicate onset of wakefulness. The present study examines whether one can detect transitions from sleep to wakefulness using only the CPAP flow signal and automate this recognition. Prospective study with blinded analysis Sleep disorder center, academic institution. 74 subjects with obstructive sleep apnealhypopnea syndrome (OSAHS) INTERVENTIONS: n/a. 74 CPAP titration polysomnograms in patients with OSAHS were examined. First we visually identified characteristic patterns of ventilatory irregularity on the airflow signal and tested their relation to conventional detection of EEG defined wake or arousal. To automate recognition of sleep-wake transitions we then developed an artificial neural network (ANN) whose inputs were parameters derived exclusively from the airflow signal. This ANN was trained to identify the visually detected ventilatory irregularities. Finally, we prospectively determined the accuracy of the ANN detection of wake or arousal against EEG sleep/wake transitions. A visually identified irregular respiratory pattern (IrREG) was highly predictive of appearance of EEG wakefulness (Positive Predictive Value [PPV] = 0.89 to 0.98 across subjects). Furthermore, we were able to automate identification of this irregularity with an ANN which was highly predictive for wakefulness by EEG (PPV 0.66 to 0.86). Despite not detecting all wakefulness, the high positive predictive value suggests that analysis of the respiration signal alone may be a useful indicator of CNS state with potential utility in the control of CPAP in OSAHS. The present study demonstrates the feasibility of automating the detection of IrREG.

Automatic EEG Arousals Detection for Obstructive Sleep Apnea Syndrome

Abstract EEG arousals are seen in EEG records as awakening response of human brain. Obstructive sleep apnea (OSA) is one of serious sleep disorders. Sevier OSA brings about EEG arousals and sleep of patients with OSAS is frequently interrupted. Number of respiratory-related arousals during the whole night PSG recordings is directly concerned with the quality of patients’ sleep. Therefore, to detect EEG arousals in PSG record is significant task for clinical diagnosis. In this paper, the method for automatic detection of EEG arousals was proposed. In order to detected respiratory-related arousals effectively, threshold values were determined according to the pathological events as sleep apnea and EMG. If the resumption of ventilation (end of apnea) was detected, lower thresholds were adopted for detecting EEG arousals including relatively doubtful arousals. On the other hand, threshold maintains high when the pathological events were not detected. Proposed method was applied to the data of eight patients with OSAS, and accuracy of EEG arousals detection was verified by comparing the visual inspection. Effectiveness of the proposed method in clinical diagnosis was investigated.

Sleep apnea syndrome: improved detection of respiratory events and cortical arousals using oxymetry pulse wave amplitude during polysomnography

Respiratory events (RE) during sleep induce cortical arousals (A) and marked changes in autonomic markers in sleep apnea syndrome (SAS). The aims of the study were double. First, we assessed whether pulse wave amplitude (PWA) added to polysomnography (PSG) could improve RE and A detection; second, we wanted to know whether the quality of detection of these two parameters could be improved using PWA. Respiratory disturbance index (RDI) and A were randomly scored twice by the same observer in 12 male patients with SAS. The first scoring was done using conventional PSG signals, the second scoring adding PWA to PSG. We also measured interobserver agreement by randomly selecting and reading 100 PSG sequences of 5 min with and without PWA by two observers. Adding PWA to PSG parameters allowed to detect significantly more RDI (53.9 +/- 21.6 h(-1) versus 48.3 +/- 22.3 h(-1), p < 0.001) and more A (68.0 +/- 14.4 versus 59.4 +/- 16.5, p < 0.001). Moreover, after using PWA, there was no significant disagreement between two observers for detecting RE, showing better quality of RE detection. PWA is a simple and cheap parameter that improves the diagnostic value of conventional PSG in sleep apnea syndrome by better detecting respiratory events and A.

Determining a continuous marker for sleep depth

Detection and quantification of sleep arousals is an important issue, as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging, electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG, non-rapid eye movement sleep is staged into four somewhat rough categories. In this study, we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep. In order to develop such a marker, we obtained the time–frequency map of two EEG channels around sleep arousals and identified the frequency bands that show the most change during arousals. We then evaluated classification performance of the potential signals for representing the depth of sleep, using receiver operating characteristic analysis. Our comparisons based on the area under the curve values revealed that the sum of absolute powers in alpha and beta bands is a good continuous marker to represent the depth of sleep. Higher values of this marker indicate low-quality sleep and vice versa. We believe that use of this marker will lead to a better quantification of sleep quality.

EEG Arousal Norms by Age

Brief arousals have been systematically scored during sleep for more than 20 years. Despite significant knowledge concerning the importance of arousals for the sleep process in normal subjects and patients, comprehensive age norms have not been published. Seventy-six normal subjects (40 men) without sleep apnea or periodic limb movements of sleep, aged 18 to 70 years, slept in the sleep laboratory for 1 or more nights. Sleep and arousal data were scored by the same scorer for the first night (comparable to clinical polysomnograms) and summarized by age decade. There were no statistically significant differences for sex or interaction of sex by age (p > .5 for both). The mean arousal index increased as a function of age. Newman-Keuls comparisons (.05) showed arousal index in the 18- to 20-year and 21- to 30-year age groups to be significantly less than the arousal index in the other 4 age groups. Arousal index in the 31-to 40-year and 41-to 50-year groups was significantly less than the arousal index in the older groups. The arousal index was significantly negatively correlated with total sleep time and all sleep stages (positive correlation with stage 1 and wake). Brief arousals are an integral component of the sleep process. They increase with other electroencephalographic markers as a function of age. They are highly correlated with traditional sleep-stage amounts and are related to major demographic variables. Age-related norms may make identification of pathologic arousal easier.

The Visual Scoring of Sleep in Adults

The 1968 Rechtschaffen and Kales (R & K) sleep scoring manual was published 15 years after REM sleep was discovered. Advances in the ensuing 28 years warranted a re-look at visual scoring of sleep stages. This paper describes the work of the AASM Visual Scoring Task Force, including methodology, a literature review and the rationale behind the new rules. Reliability studies of R & K scoring were reviewed; reliability was low for stage one and moderate for slow wave sleep. Evidence indicated that K complexes and slow waves are expressed maximal frontally, spindles centrally and alpha rhythm over the occipital region. Three derivations of EEG, two of electro-oculography, and one of chin EMG were recommended. Scoring by 30-second epochs was retained. New terminology for sleep stages was proposed. Attenuation of alpha rhythm was determined to be the most valid electrophysiological marker of sleep onset. Alternative measures were proposed for non-alpha generating subjects. K complexes associated with arousals were determined to be insufficient alone to define the new stage N2. No evidence was found to justify dividing slow wave sleep into two stages. No reasons were found to alter the current slow wave amplitude criteria at any age. The phenomena of REM sleep were defined. The rules for defining onset and termination of REM sleep periods were simplified. Movement time was eliminated and major body movements defined. Studies are needed to test the reliability of the new rules. Future advances in technology may require modification of these rules with time.

O0056 Detection of respiratory effort-related arousals using a single flow signal

O0056 Detection of respiratory effort-related arousals using a single flow signal

Detection of Cortical Arousals in Children Using Frontal EEG Leads in Addition to Conventional Central Leads

This study was designed to assess the efficacy of using Fz as an additional electrode in screening arousals during polysomnography in children. Polysomnograms from 24 children were randomly selected from a sleep-study database of children from different diagnostic categories. Of the children whose polysomnograms selected, 5 were normal, 5 had severe obstructive sleep apnea syndrome, 5 had mild obstructive sleep apnea syndrome, 5 had snoring, and 4 had periodic limb movement disorder. American Academy of Sleep Medicine criteria for arousal were applied to Fz only, C3/C4 only, or both electrode sites combined. C3/C4 electrode sites picked up 96.33% of arousals in the polysomnograms of children, compared with 87.46% arousals identified at Fz. An additional 3.3% of arousals were identified at Fz that were not detected at C3/C4. The average median number of arousals per subject, accompanied by the interquartile range, was 29 (19.2-39.7) at C3/C4 and 27.5 (19.2-33.7) at FZ, reflecting a significant difference between the two (p = .005). The use of both electrodes sites resulted in the detection of the highest number of arousals per subject, with a median of 29 (20.2-40.7). Unlike findings recently reported in adults, our study detected a higher number of arousals from central electrodes rather than frontal, with the maximum number of arousals obtained using a combination of frontal and central electrodes. A possible explanation for differences in the optimal location for detection of arousals between adults and children is discussed.

Micro- and macrostructure of sleep EEG

Electroencephalogram (EEG) provides important and unique information about the sleeping brain. Polysomnography was the major method of sleep analysis and the main diagnostic tool in sleep medicine. The standard interpretation of polysomnographic recordings describes their macrostructure in terms of sleep stages, delineated according to R&K scoring criteria. Several descriptors of sleep microstructure rely on the quantification of sleep spindles and slow wave activities, detection of arousals, etc. However, these descriptors are usually assessed by means of substantially different signal processing (or visual) methods. This hinders possibilities of combining their results into a coherent description of the sleep process. This study proposes a solution to these problems in terms of a framework based upon adaptive time-frequency approximations - a recent, advanced method of signal processing. The proposed approach provides compatibility with the visual EEG analysis and standard definitions of EEG structures and describes both the macro- and microstructure of sleep EEG. Adaptive time-frequency approximations of signals calculated by means of the matching pursuit (MP) algorithm allow for the discrimination between series of unrelated structures and oscillatory activity. The detection, parametrization, and description of all these features of sleep are based upon the same unifying approach

Do additional frontal EEG Leads (Fz-A2), in Sleep Studies, Lead to Detection of Cortical Arousals Following Periodic Limb Movements that are Clinically Relevant?

PURPOSE: Periodic limb movements (PLM) are characterized by repetitive leg movements, detected by surface EMG of tibialis anterior muscles, during sleep. EEG arousals are considered to be associated with leg movements if they occurred before, simultaneously or 10 s after a PLM. Studies have indicated that PLM's are not primary but rather an epiphenomenon associated with an underlying arousal disorder. Recently O’Malley et. al. 2003 postulated that the addition of a single frontal EEG lead yielded additional respiratory related arousals that were physiologically relevant. We hypothesized that PLM related arousals detected by an additional Fz-A2 EEG lead would correlate with a measure of excessive daytime sleepiness(EDS) and offer clarity when reporting PLM's.

Quantitative analysis of sleep EEG microstructure in the time–frequency domain

A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time–frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time–frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.

Quantitative analysis of sleep EEG microstructure in the time?frequency domain

A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time–frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time–frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.

The addition of frontal EEG leads improves detection of cortical arousal following obstructive respiratory events.

To examine the hypothesis that respiratory events in obstructive sleep apnea syndrome (OSAS) end in arousal not detected by conventional electroencephalographic (EEG) leads. Observational. The study was conducted at a major metropolitan sleep disorders center. 10 patients with untreated OSAS and 5 patients undergoing continuous positive airway pressure (CPAP) titration for OSAS. Standard clinical nocturnal polysomnography recordings were supplemented to include frontal EEG leads and airflow measured by nasal cannula pressure. In 10 untreated subjects, 1465 obstructive events (apneas, hypopneas, and flow limitation events), and in 5 subtherapeutic CPAP-titrated subjects, 459 total events were identified during non-rapid eye movement (REM) sleep only. American Academy of Sleep Medicine arousal criteria applied to central leads and to frontal leads allowed detection of an additional 24% respiratory event-related arousals by frontal leads than by conventional leads. Frontal arousal detection differed by event type: 16% of apneas, 21% of hypopneas, and 35% of flow limitation events. Autonomic correlate (increased heart rate) of both conventional and frontal arousals was similar (in a subgroup of 8 untreated patients, analyzing only flow limitation events). Tabulating frontal arousals separately for each frontal lead indicated that Fz as the sole frontal lead (added to conventional leads) increased detection of arousals by 19% over using only conventional leads (total of 92% of all obstructive respiratory events). The addition of a single frontal lead (Fz) yields additional respiratory-related arousal information that appears physiologically relevant. Future studies are needed to assess clinical relevance to the evaluation and treatment of sleep-disordered breathing.

Autonomic Arousal Index: an Automated Detection Based on Peripheral Arterial Tonometry

Arousals from sleep are associated with increased sympathetic activation and are therefore associated with peripheral vasoconstriction. We hypothesized that digital vasoconstrictions as measured by peripheral arterial tonometery (PAT), combined with an increase in pulse rate, would accurately reflect arousals from sleep, and can provide an autonomic arousal index (AAI). Based on a previously studied group of 40 sleep apnea patients simultaneously recorded by both polysomnography (PSG) and PAT systems, an automated algorithm using the PAT signal (and pulse rate derived from it) was developed for detection of arousals from sleep. This was further validated in a separate group of 96 subjects (85 patients referred with suspected obstructive sleep apnea and 11 healthy volunteers mean age 46.2+/-14.4 years, BMI 28.5+/-5.4 kg/m2). All underwent a whole night PSG with simultaneous PAT recording. The PSG recordings were blindly manually analyzed for arousals based on American Academy of Sleep Medicine (AASM) criteria, while PAT was scored automatically. There was a significant correlation between PSG and PAT arousals (R=0.82, p<0.0001) with a good agreement across a wide range of values, with a ROC curve having an area under the curve (AUC) of 0.88. We conclude that automated analysis of the peripheral arterial tonometry signal can detect EEG arousals from sleep, in a relatively quick and reproducible fashion.

Non-Invasive detection of respiratory effort-related arousals (REras) by a nasal cannula/pressure transducer system.

The published AASM guidelines approve use of a nasal cannula/pressure transducer to detect apneas/hypopneas, but require esophageal manometry for Respiratory Effort-Related Arousals (RERAs). However, esophageal manometry may be poorly tolerated by many subjects. We have shown that the shape of the inspiratory flow signal from a nasal cannula identifies flow limitation and elevated upper-airway resistance. This study tests the hypothesis that detection of flow limitation events using the nasal cannula provides a non-invasive means to identify RERAs. N/A. N/A. 10 UARS/OSAS and 5 normal subjects N/A. All subjects underwent full NPSG. Two scorers identified events from the nasal cannula signal as apneas, hypopneas, and flow limitation events. Two additional scorers identified events from esophageal manometry. Arousals were scored in a separate pass. Interscorer reliability and intersignal agreement were assessed both without and with regard to arousal. The total number of respiratory events identified by the two scorers of the nasal cannula was similar with an Intraclass Correlation (ICC) =0.96, and was essentially identical to the agreement for the two scorers of esophageal manometry (ICC=0.96). There was good agreement between the number of events detected by the two techniques with a slight bias towards the nasal cannula (4.5 events/hr). There was no statistically significant difference (bias 0.9/hr, 95%CI -0.3-2.0) between the number of nasal cannula flow limitation events terminated by arousal and manometry events terminated by arousal (RERAs). The nasal cannula/pressure transducer provides a non-invasive reproducible detector of all events in sleep disordered breathing; in particular, it detects the same events as esophageal manometry (RERAs).