Towards the standardization of hypnograms construction for sleep analysis
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1 1 Towards the standardization of hypnograms construction for sleep analysis Ángel Fernández-Leal 1, Vicente Moret-Bonillo 1, Mariano Cabrero-Canosa 1 1 Laboratory for Research and Development in Artificial Intelligence (LIDIA), Department of Computer Science, University of A Coruña, A Coruña, Spain angel.fleal@udc.es; vicente.moret@udc.es Abstract Sleep can be considered as a mechanism of selfregulation and resting that occurs in the majority of mammals in 24-hour cycles approimately, alternating with states of wakefulness. As a whole, sleep is a heterogeneous state presenting different stages. These stages can be identified through the recording and analysis of certain physiological parameters. From a medical point of view the analysis of the sleep is useful in the diagnosis of health problems that receive the generic name of "sleep disorders". Sleep disorders can be grouped into four main categories: (a) problems to fall asleep and stay asleep, (b) problems to stay awake, (c) problems to maintain a regular schedule of sleep, and (d) unusual behaviors during sleep. This article presents an overview of the evolution of sleep research, with special attention to the most relevant milestones that have led to the systematic and automatic analysis of the sleep, and the establishment of standards for the construction of the so-called Hypnograms. Keywords Sleep analysis, Electroencephalography, Hypnogram. The act of sleeping (hereinafter "sleep") can be defined as a physiological state that has associated inaction and suspension of the senses and all voluntary movement. Several investigations have allowed to verify that the identification of the different phases through which passes the sleep of a subject is useful in the diagnosis of certain sleep disorders, since some of these disorders occur during specific stages of sleep of the patient. To facilitate the analysis of these stages, and also their temporal sequence, clinicians often use a graphical representation of the chronological appearance of the different stages of sleep. This representation is called "hypnogram", and has now become an essential tool in the sleep analysis task. The hypnogram is built in the first quadrant of a Cartesian plane, in which the X-ais is time and the Y ais represents the sleep stages of the patient. The sequence of sleep stages is illustrated by a plot in which the horizontal lines indicate the duration of a particular sleep phase, and the vertical lines indicate changes in the sleep phases. Figure 1 shows an eample of a typical hypnogram. The hypnogram, in conjunction with the analysis of other physiological parameters such as heart rate, or the amount of oygen in arterial blood, has become a valuable clinical tool for physicians to diagnose different clinical problems that may occur during sleep or that may cause a bad quality of the patient s sleep. However, until the 20th century, the lack of tools and techniques that allow analytical studies of neurophysiological signals has hindered the diagnosis of pathologies related to sleep disorders. In the 20th century the electroencephalograph appear, and the analysis of records obtained through this new tool made it possible to identify the elements that characterize the different phases of the sleep of a patient. Fig. 1 Eample of representation of a hypnogram I. INTRODUCTION II. ELECTROENCEPHALOGRAPHY AND CEREBRAL ACTIVITY In 1929, the German neurologist Hans Berger demonstrated -after developing electroencephalography (EEG)- that the electrical activity of the brain is different if the patient is asleep or if the patient is awake. He also identified some patterns associated with each of these two states. In this contet, Berger documented and defined the alpha waves as EEG oscillations in the range of 8-12 Hz, and the beta waves as EEG oscillations in the range of Hz. Thereafter knowledge about the electrical activity of the brain increased and new patterns of electrical signals during sleep and wakefulness respectively were identified. On the other hand, in 1936, Walter described the delta waves as EEG oscillations in the range of Hz, and showed that delta waves are useful for locating brain tumors and epilepsy-related injuries. Finally, in 1944, Walter and Dovey eplored the relevance of EEG in detecting tumors. To accomplish the task they employed a special type of electrodes. In their study they highlighted the role of a kind of activity that corresponds to EEG oscillations in the range of 4-7 Hz. They called these EEG oscillations theta waves. Apart from the studies on the brain activity previously discussed, in 1937 Loomis, Harvey and Hobart determined
2 2 that sleep is an active state in which different phases or stages can be identified [1]. For this purpose they used EEG records and classified 5 different levels of sleep which were referenced using the letters A to E. They also represented their sequence of occurrence in time through hypnograms, in such a way that on the vertical ais are the sleep phases (i.e., A, B, C, D and E) while the horizontal ais represents time of sleep. Figure 2 shows an eample of the hypnogram of Loomis. The studies of Loomis suggest that changes in the stage within the sleep frequently correspond to a movement, and are associated with a short ecitation. In addition, for the identification of the sleep phases it is necessary to take into account certain characteristic patterns of the signals of the brain. As a result of these observations we have now the first references to the fundamental patterns for the correct classification and identification of the stages of sleep: (a) sleep spindles, (b) K complees, and (c) verte sharp waves. Sleep spindles, also known as sigma rhythm, are a type of EEG activity with a frequency of Hz lasting a minimum of 0.5 seconds and about 50 µv of amplitude with higher voltage in central regions, appearing briefly and intermittent. The first reference to sleep spindles was reported by Loomis in 1936 [2]. On the other hand, K complees are slow waves presenting a first phase negative followed by a slower phase largescale and smaller amplitude, lasting a minimum of 0.5 seconds, and they may appear spontaneously or in response to a sensory stimulus of any kind. The first document mentioning the K complees was published by the Loomis group in 1938 [3]. Independently to Loomis, in 1944, Liberson documented verte sharp waves (VSW) [4]. Verte sharp waves are one of the patterns of sleep that occur early during the night at the end of the process of sleep onset, just before the onset of sleep spindles and K complees. Liberson described verte sharp waves as strong waves located at the beginning of sleep, with a frequency of 3-6 Hz. Verte sharp waves, although often grouped together with the K comple, in the strict sense, are different since they are shorter in duration, and lower in amplitude. The identification of these waves permitted an improvement in the classification of the B and C sleep stages. III. PATTERNS AND CLASSIFICATION Kleitman, Aserinsky and Dement were pioneers in sleep research. Kleitman and Aserinsky discovered rapid eye movements (REM) [5]. Subsquently, Kleitman and Dement discovered the recurring pattern of REM sleep and non- REM sleep (NREM) [6]. NREM sleep was divided into 4 stages, numbered 1, 2, 3 and 4. The sleep usually begins with stage 1, continuing with episodes of the rest of the stages. In stages 3 and 4 sleep becomes deeper. With respect to REM sleep, also known as stage 5, it appears to be associated with stage 1. Moreover, they observed that the K complees appear more frequently during periods of a low voltage spindling EEG, and they are generally of greater amplitude and more frequent during the stage 2 EEG preceeding an eye movement period than following. A B C D E....? D N Fig. 2 An eample of Loomis hypnogram This new classification of sleep stages introduced changes in the representation of hypnograms, giving them the fundamental form with which are represented today. Figure 3 shows an eample of the hypnogram conceived by Dement and Kleitman. In this hypnogram, sleep stages are represented on the vertical ais (awake -A- and the four stages). The thick bars immediately above the EEG lines indicate periods during which rapid eye movements were observed. Longer vertical lines indicate important movements (whole body position changes) and the shorter lines represent minor movement. The arrows indicate the end of a cycle of EEG and the beginning of the net. From these works, in 1962, Berger, Olley, Oswald and Schwartz introduced an improvement in the identification of the stage REM describing sawtooth waves as an activity in the EEG of medium-low amplitude ranging from 2 to 6 Hz in frequency. They also describe a temporary association of sawtooth waves with series of rapid eye movements. A HOURS Fig. 3 Eample of the new hypnogram according with Dement and Kleitman IV. TOWARDS STANDARDIZATION During the decades of 1950 and 1960 the lack of standards for classification and identification of sleep phases made the scientific results obtained from sleep studies difficult to be compared. Thus, in 1961, the International Federation of Electroencephalography and Clinical Neurophysiology Committee made a proposal with the aim to unify the
3 3 terminology used in the sleep studies [7]. As a result of this proposal, in 1968, a committee conducted by Rechtschaffen and Kales established a set of rules with the purpose of standardizing the classification of the stages of sleep: the so-called 'R&K paradigm' [8]. The above mentioned paradigm involves parameters, techniques and wave patterns obtained from electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). In addition, the whole set of signals is analyzed in epochs of 20 or 30 seconds (i.e., time intervals of seconds). This approach specifies the fundamental characteristics of the stages of sleep (insomnia, time of movement, steps NREM sleep, and REM sleep), which allowed further standardize the construction of hypnograms. Despite the standardization efforts made to establish R&K rules, some researchers found difficulties in its implementation, especially in the mechanization of computing the classification of sleep, since many vague and ambiguous areas were found. This circumstance led to several research teams to look for effective alternatives that would help to resolve the problems encountered. In the decade of 1990, a revision of the R&K rules was addressed in Japan, with the purpose of minimizing the low level of congruence between the automatic and visual analysis in the identification of stages 1, 3 and 4. As a result of this study, in 2001, the Sleep Computing Committee of the Japanese Society of Sleep Research Society (JSSR) proposed additional criteria and correction of R&K criteria in order to improve classification and identification of sleep stages [9]. With the same motivation, between 2004 and 2006, the Task Force Scoring of Polysomnographic Recordings of the German Sleep Society (DGSM) conducted a study to improve the implementation of computer systems for the analysis of the sleep [10]. As a result of this study the DGSM set the basics to refine the identification of relevant signal in the EEG patterns: (a) alpha waves, (b) theta waves, (c) delta waves (d) verte sharp waves VSW-, (e) K comple, (f) sleep spindles, and (g) sawtooth waves. Also, in 2007, the American Academy of Sleep Medicine (AASM) carried out a study in order to establish more precise definitions and a set of more appropriate rules for the detection of sleep events and for the correct classification of sleep stages [11]. As a result of this study the following classification of sleep stages was redefined: (a) Stage W (awake), (b) Stage N1 (NREM 1), (c) Stage N2 (NREM 2), (d) Stage N3 (NREM 3), and (e) Stage R (REM). It is important to highlight that the N3 stage represents the slow waves sleep and replaces the R&K nomenclature of the stages 3 and 4. For the identification of the stages of sleep a classification using 30 seconds intervals is used, in such a way that a stage is assigned to each interval, taking into account that if two or more stages co-eist during the same interval the corresponding stage is assigned to the interval of longer duration. Table 1 shows the correspondence between the classification of stages of sleep according to Loomis et al, Dement and Kleitman, R&K, AASM, and the characteristic EEG activity at each stage. In 2012, 2014 and 2015 the AASM published versions 2.0 [12], 2.1 [13] and 2.2 [14], respectively, of the previously established criteria in The latest revision introduces the stage T (transitional), analogous to the previously used terminology of indeterminate sleep, in the sleep staging rules for infants, and is currently the gold standard for the classification and identification of sleep stages and for the construction of hypnograms. Figure 4 shows an eample of hypnogram built according to the standard AASM W R N1 N2 N HOURS Fig. 4 Eample of hypnogram corresponding to the standard AASM-2015 V. AUTOMATIC SLEEP STAGING The advent of computers in the second half of the twentieth century and the rapid increase in their capabilities led to the realization of multiple studies on methods of automatic analysis of sleep and its stages. Several techniques were implemented [15]: (a) etracting characteristics of signals using frequency analysis and frequency over time, such as discrete wavelet transform (DWT) or Huang Hilbert transform (FFT), or (b) parametric and non-parametric methods for the classification of events and stages of sleep, such as random forest classifiers, artificial neural networks (ANN), fuzzy logic, the nearest neighbor, linear discriminant analysis (LDA), support vector machine (SVM) and kernel logistic regression (KLR). The different procedures of validation that employ eisting methods, prevent a rigorous and useful comparative analysis of the results and of their ability to perform a correct classification of sleep stages. However, the results of the some studies indicates that correct identification of signals characteristic patterns (especially sleep spindles [16], K comple [17][18], verte sharp waves and sawtooth waves), and events such as arousals (abrupt shifts in EEG frequency during sleep) [19], is a critical and essential element to make a correct classification of sleep stages. Moreover, implementing rules published by the AASM involves treatment of signals using a division of the records at intervals, continuing the methodology established by the R&K rules. This division limits the ability to record and
4 4 analyze events lasting less than 30 seconds, and ignores the fact that the evolution of the biological processes occurring continuously, which implies a smooth transition between sleep stages. To overcome the limitations of a system based on time intervals, several methods have been developed recently. For eample, Alvarez-Estevez et al. developed a method for the automatic analysis of the macrostructure of sleep continuously [20]. This method is based on the use of fuzzy inference to avoid categorical classifications, and permits the representation of smooth transitions of sleep through their different stages. The results of this method suggest the desirability of advancing in the study of mechanisms dealing with sleep signals as a continuous record. arousals, and signal patterns, especially of sleep spindles, K comple, verte sharp waves and sawtooth waves. These studies will provide fundamental knowledge for the improvement of methods for the automatic analysis of sleep and for the construction of hypnograms. ACKNOWLEDGMENT This work has been supported by the Spanish Ministerio de Economía y Competitividad, MINECO, under research project TIN P, co-funded by the European FEDER (ERDF). Table 1 Correspondence of the classification of sleep stages according to Loomis, Dement and Kleitman, R&K and AASM 2007 Loomis et al Stages A and B Dement & kleitman Stage 1 Stage C Stage 2 Stages D and E Stages D and E Stage 3 Stage 4 Stage 1+ REM (after the first stage 1) R&K AASM EEG activity Stage W (wakefulness) Movement Time (MT) Stage 1 (S1) Stage 2 (S2) Stage 3 (S3) Stage 4 (S4) Stage REM Stage W Stage N1 Stage N2 Stage N3 Stage R VI. CONCLUDING REMARKS Beta, Alpha Alpha, Theta, Verte sharp waves Theta, Spindles, K complees Theta, Delta Theta, Alfa, Sawtooth waves It can be considered that the boost in the sleep study was carried out with the advent of electroencephalography. On the other hand, the hypnogram is mainly used as a qualitative method to display the duration of each stage of sleep, and the number of transitions between stages. However, the combined analysis of the hypnogram with records of certain physiological processes allows to obtain more relevant quantitative measures. After the last standard published by the AASM for the correct classification of sleep stages, it appears to be clear that, in order to be able to undertake the task successfully it necessary that the recording of signals and the corresponding interpretation mechanisms be as reliable possible. This is why, recently, lots of work is being performed focusing on the correct identification of relevant events, such as CONFLICT OF INTEREST The authors declare that they have no conflict of interest. REFERENCES 1. Loomis, A. L.; Harvey, E. N.; Hobart, G. A. (1937) Cerebral states during sleep, as studied by human brain potentials. Journal of Eperimental Psychology, Vol 21(2), doi: /h Loomis, A. L., Harvey, E. N. and Hobart, G. A. (1936) Electrical potentials of the human brain. J. Ep. Psychol., 19: Loomis, A. L., Harvey, E. N. and Hobart, G. A. (1938) Distribution of disturbance patterns in the human electroencephalogram, with special reference to sleep. J. Neurophysiol., 1: Liberson WT. (1944) Problem of sleep and mental disease. Digest Neurol Psychiat 12: Aserinsky E, Kleitman N (1953) Regularly Occurring Periods of Eye Motility, and Concomitant Phenomena, during Sleep. Science, New Series, Vol. 118, No , pp Dement, W; Kleitman, N. (1957) Cyclic variations in EEG during sleep and their relation to eye movements, body motility, and dreaming. Electroencephalography and Clinical Neurophysiology 9 (4): doi: / (57) PMID Brazier, M. A. B. et al. (1961) Proposal for an LEG terminology by the terminology committee of the International Federation for Electroencephalography and Clinical Neurophysiology. Electroenceph. Clin. Neurophysiol., 13: Reschtschaffen, A. and Kales, A. (1968) A Manual of Standarized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects. Washington Public Health Service. US Government Printing Office, Washington DC. 9. Sleep Computing Committee of the Japanese Society of Sleep Research Society (JSSR). Tadao Hori et al. (2001) Proposed supplements and amendments to A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects, the Rechtschaffen & Kales (1968) standard. Psychiatry and Clinical Neurosciences 55, Task Force Scoring of Polysomnographic Recordings of the German Sleep Society (DGSM): Hartmut Schulz et al. (2006) A Review of Sleep EEG Patterns. Part I: A Compilation of Amended Rules for Their Visual Recognition according to Rechtschaffen and Kales.
5 5 11. Iber C, Ancoli-Israel S, Chesson AL, Quan SF. (2007) The AASM manual for the scoring of sleep and associated events: Rules, terminology, and technical specification. Version 1. Westchester, IL: American Academy of Sleep Medicine X 12. Berry R, Brooks R, Gamaldo C, et al. (2012) The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specification, Version 2.0. Darien, IL: American Academy of Sleep. 13. Berry R, Brooks R, Gamaldo C, et al. (2014) The AASM manual for the scoring of sleep and associated events: rules, terminology, and technical specification, Version 2.1. Darien, IL: American Academy of Sleep. 14. Berry RB, Brooks R, Gamaldo CE, Harding SM, Lloyd RM, Marcus CL and Vaughn BV (2015) The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, Version Darien, Illinois: American Academy of Sleep Medicine. 15. Khalighi S, Sousa T, Pires G, Nunes U (2013) Automatic sleep staging: A computer assisted approach for optimal combination of features and polysomnographic channels. Epert Systems with Applications 40, Nonclercq A, Urbain C, Verheulpen D, Decaestecker C, Van Bogaert P, Peigneu P (2013) Sleep spindle detection through amplitude frequency normal modelling. Journal of Neuroscience Methods. Volume 214, Issue 2, pages , doi: /j.jneumeth Camilleri T.A., Camilleri K.P., Fabri S.G. (2014) Automatic detection of spindles and K-complees in sleep EEG using switching multiple models. Biomedical Signal Processing and Control. Volume 10, pages , doi: /j.bspc Hernández-Pereira E, Bolón-Canedo V, Sánchez-Maroño N, Álvarez-Estévez D, Moret-Bonillo V, Alonso-Betanzos A (2016) A comparison of performance of K-comple classification methods using feature selection. Information Sciences. Volume 328, Pages Alvarez-Estevez, D., & Moret-Bonillo, V. (2011) Identification of electroencephalographic arousals in multichannel sleep recordings. IEEE Transactions on Biomedical Engineering, 58(1), Alvarez-Estevez, D. et al. (2013) A method for the automatic analysis of the sleep macrostructure in continuum. Epert Systems with Applications, 40(5),
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