One of the areas of gradual development of Naxon refers to applications that evaluate the quality of sleep and can be used personally and individually by users of portable EEG devices and professionals in the areas of Clinical Psychology and Sleep Medicine. The advantages of applying this technology to this area are multiple, for example, remote monitoring and better patient follow up. At Naxon, through the development of our integrated software platform with pattern analysis tools, we seek one of the applications to be the staging of sleep and the completion of preliminary analysis of the person's sleep quality. Below we show the current problems in the area and how this new technology is introduced to it. 

Sleep disorders in today's world

Sleep disorders are one of the most relevant health problems currently. The importance of a good quality of sleep is not only fundamental as a determinant factor of health, but as an element that promotes a good quality of life (Sierra, Jiménez-Navarro, & Martín-Ortiz, 2002).

Sleep disorders refer to a wide variety of conditions, such as insomnia disorders, sleep-related breathing disorders, central disorders of hypersomnolence, circadian sleep-wake rhythm disorders, sleep-related movement disorders and parasomnias, among others (Sateia, 2014).

In a sample from the United States, a general prevalence of current or previous sleep disorders in adults of 52.1% was found. Specifically, the results show a prevalence of insomnia of 42.5%, 11.2% of nightmares, 7.1% of excessive sleep, 5.3% of somnolence and 2.5% of sleepwalking. These conditions were often chronic and usually started at an early age (Bixler, Kales, Soldatos, Kales, & Healey, 1979).

The sleep problems of older people are different from those of younger people. Sleep apnea and periodic leg movements (PLMS) during sleep are extremely common in older people. Complaints of insomnia, snoring, daytime sleepiness and lack of sleep, which are also very common in this population, should not be taken lightly, as they can be symptoms of these physiological sleep disorders. Also, subjective reports of lack of sleep, insomnia, snoring and excessive daytime sleepiness should not be taken lightly and should not be assumed to be a normal sign of aging (Ancoli-Israel, 1989).

It has been estimated that among people over 65 years of age, half of those who live in their own homes and 60% of those who live in residences suffer from a sleep disorder, of which , the difficulty to start and maintain sleep and Daytime sleepiness are more common. On the other hand, in the United States, 25 million prescriptions of hypnotics are extended every year and 40% of them are used by the elderly. In the case of Spain, 58% of people who consume hypnotics are elderly and, as is observed in other countries, the recommendations for their use are incorrectly followed, thus, drugs with intermediate or long-term action are used inappropriately. (Benetó, 2000).

On the other hand, approximately 25% of all children experience some type of sleep problem at some point during childhood. Several studies have examined the prevalence of sleep complaints reported by parents and children in large samples of healthy children and adolescents and with typical development, many of these have also delineated the association between interrupted sleep and behavioral concerns. Sleep problems are even more frequent in children and adolescents with chronic medical conditions, neurological and psychiatric development issues (Owens, 2007). Even in healthy children, a prevalence of 37.4% of sleep disorders was found. 40.7% of these children have problems such as resistance to sleep/fear of sleeping alone and between parasomnias, nightmares and night terrors are present (Convertini, Krupitzky, Tripodi, & Carusso 2003).

On the other hand, syndromes such as attention deficit hyperactivity disorder (ADHD) and sleep disorders have a high correlation and are very frequent in a neuropediatric clinic. By improving the sleep of these children, the symptoms of ADHD diminish and the administration of psychostimulants whose undesirable side effects generate great anxiety in the parents of these children is avoided (Fursow, Betancourt, & León 2006).

On the other hand, several studies have shown the high prevalence of sleep disorders among adolescents. As a cause, psychological, hormonal, puberal factors and inadequate sleep habits are invoked. In a sample of 1,155 students, 38.55% reported poor quality sleep, 23.1% difficulty in conciliation, 38.2% night awakenings and 15.9% early awakening. 17.7% reported a complaint of sleep plus some symptom of insomnia; the prevalence of insomnia found was 9.9%. Snoring (20.5%), Somnilochia (45.4%) and nightmares (29.5%) are the most frequent parasomnias. Additionally, 53% complain of excessive daytime sleepiness (Jiménez et al., 2004).

In university students, certain results show that approximately 30% have poor sleep quality, excessive latency and poor sleep efficiency; no differences were found between men and women in any component, with the exception of hypnotics use, where women had a higher score (Sierra, Jiménez-Navarro, & Martín-Ortiz, 2002).

In addition, hospitalized adult patients often have sleep problems. Most studies have focused mainly on patients in the intensive care unit. However, many medical and surgical disorders can alter the normal architecture of sleep (Aguilera Olivares, Díaz, & Sánchez, 2012).

The direct and indirect costs of sleep disorders are high. An analysis of this was carried out in Australia. The total financial cost (independent of the cost of suffering) of $ 4524 million represents 0.8% of the Australian gross domestic product. The cost of suffering of $ 2.97 billion is 1.4% of the total burden of the disease in Australia (Hillman, Murphy, Antic, & Pezzullo, 2006).

Polysomniography

The study of sleep in the laboratory is carried out through the polysomnographic record (PSG), which consists of the simultaneous recording of the EEG, the electrooculogram (EOG), the electromyogram (EMG) of the submental muscles, the electrocardiogram (ECG) and breathing. In addition, other parameters can be monitored: rectal temperature, blood pressure, limb movements, blood gases, endosophageal pressure, penile erection and electrodermal reaction (Peraita-Adrados, 2005).

PSG measures the cycles and stages of sleep, either rapid eye movement (REM) sleep or synchronized sleep, or non rapid eye movement (NREM) sleep or desynchronized sleep, which has three stages that can be detected by means of the brain waves (EEG).

Polysomnography can be carried out both in a specialized sleep clinic and in the person's own home. At a specialized center, you are asked to arrive approximately 2 hours before bedtime. The person will sleep in a bed in this center. The exam is often done during the night. Electrodes are placed on the chin, scalp and on the outer edge of the eyelids. There are monitors to record the heart rate and breathing attached to the chest, which remain in place while you sleep. The electrodes record signals while one is awake (with closed eyes) and during sleep. In the exam, the amount of time it takes to fall asleep is measured, as well as the time it takes to enter the rapid eye movements sleep. A trained specialist observes while one is asleep and notes any change in heart or respiratory rate. There are also monitors that record movements during the session. Sometimes a camcorder records movements during sleep. Due to the particular circumstances of doing it in a specialized center, with all the monitors and register equipment required, users usually report some difficulty to be able to fall asleep. In the case of doing it at home one can use a polysomnograph to help diagnose sleep apnea. In a sleep center the device is picked up or a specialized therapist comes to your home to prepare that device.

Home exams may be used if the sleep specialist thinks that you have obstructive sleep apnea and there are no other sleep disorders or other serious problems, such as heart disease or lung disease.

Portable EEG applications

Several studies have ventured into testing new technologies similar to portable EEG devices with smaller electrodes to perform sleep studies, obtaining very good results.

There have been numerous studies of portable monitors in the last two or three decades. The US government and medical societies have extensively reviewed this literature several times in an attempt to determine whether portable monitoring should be used more widely to diagnose disorders such as obstructive sleep apnea (OSA), for example. It is anticipated that portable monitoring as a diagnostic modality for OSA for example will be used more frequently. Physicians and others who consider the use of portable monitors should fully understand the advantages and limitations of this technology (Collop, 2008).

A couple of years ago, portable PSG was already being tested. Mykytyn, Sajkov, Neill and Mc Evoy (1999) compared the validity of the portable polysomnographic recorder with a laboratory-based polysomnographic system. The quality of the signals was comparable between the PSGP and PSGL studies. There was no significant discordance between PSGP and PSGL in the final diagnostic formulations, concluding that portable polysomnography is a viable alternative to laboratory-based polysomnography and can be further improved by a better sensor connection.

Fry, DiPhillipo, Curran, Goldberg and Baran (1998) evaluated the unattended complete PSG registered in the home and assessed the ability to acquire, store and analyze polysomnographic data. Using a digital recording system with miniature preamplifiers and the ability to record 18 channels of polysomnographic data, they found that full PSG without supervision can be performed at home with reliable and high quality recordings. The full PSG can be extended to a larger patient population, because it is no longer limited by the number of beds, and there is a reduction in costs due to the elimination of night staff and the cost of installation.

More recently, Rosen et al. (2012) conclude that a strategy in the home for diagnosis and treatment compared to PSG in the laboratory was not inferior in terms of acceptance, compliance, treatment time and functional improvements.

According to Lucey et al. (2016), a precise study on home sleep to assess sleep stages based on EEG and EEG potency would be advantageous both for clinical and research purposes, such as longitudinal studies that measure changes in sleep stages of the EEG over time. According to the authors, given that even limited PSG to monitor only sleep stages can be prohibitive for finances, unattended sleep monitoring that includes sleep stages and EEG potency but does not require placement of sensors by a technician would have an important clinical and scientific application for longitudinal research studies and clinical trials.

The staging of sleep and the measurement of spectral power with a single-channel EEG recorded on the forehead, as the Neurosky device has for example, presents multiple challenges compared to PSG that may affect the expected results. First and most obvious, the forehead canal will not detect an alpha rhythm and it is predicted that this will limit the evaluation of wakefulness and stage N1. Secondly, the waveforms used for sleep staging differ in the topographic distribution. The K complexes, for example, are distributed mainly over the mid-frontal branches, while the delta waves reach their maximum in both the frontal and occipital areas. In addition, the sleep spindles have a bimodal anterior-posterior distribution and the single EEG will allow the revision of the anterior sleep spindles. Also, age-related changes in spindle density, amplitude and duration are greater anteriorly. Thirdly, there are limitations on the ability of the single EEG to assess the topographic differences in slow wave activity (SWA) reported in adults or the subsequent change to previous SWA maximum activity reported during childhood. The NREM power also undergoes an anterior-posterior change during consecutive periods of NREM sleep that can not be assessed with a single forward lead. In addition, the measurement of SWA during REM may be less accurate than in a central derivation in a PSG due to eye movements (Lucey et al., 2016). However, the results of these authors show that the single-channel EEG provides results comparable to polysomnography in the evaluation of REM, combined sleep stages N2 and N3, and several other parameters, including the activity of slow forward wave. The data establishes that the single-channel EEG can be a useful research tool.

Sleep Staging and portable EEG: new data processing techniques

The staging of sleep involves the recording of the different stages of sleep and is part of the study of polysomniography, although a staging can also be carried out by means of an EEG device outside of a standard polysomniography, which usually involves the multiple additional records mentioned above (air flow in and out of the lungs during breathing, oxygen levels in the blood, body position, electrical activity of the muscles, heart rate, etc.). Staging allows us to have a first view of whether there may be problems at the level of sleep.

Several works have developed the possibility of automating the staging of sleep through various techniques. This suggests a great possibility to generate software applications that use portable EEG and that can be massively used remotely to evaluate basic aspects of sleep quality to allow the improvement of diagnosis and treatment, as well as generating applications that train people in hygiene of sleep.

The automatic classification of sleep is essential due to the fact that, conventionally, doctors have to visually analyze a large volume of data, which is onerous, time-consuming and prone to errors. Therefore, there is a great need for an automated sleep-staging scheme (Hassan & Bhuiyan, 2016). According to Principe, Gala and Chang (1989), the staging of sleep represents a problem of medical decision. The authors developed a model for automatic sleep staging by combining signal information, human heuristic knowledge in the form of rules and a mathematical framework. Liang, Kuo, Hu, Pan and Wang (2012) have proposed an automatic sleep scoring method that combines multiscale entropy (MEE) and autoregressive (AR) models for single-channel EEG to evaluate the performance of the method comparatively with the manual scoring in complete polysomnograms. Their results show that MEE is a useful and representative feature for sleep staging. It has high precision and good applicability for home care because only one EEG channel is used for sleep staging. 

In addition, Liang, Kuo, Hu and Cheng (2012) developed an automatic rule-based sleep staging method with an average precision and a kappa coefficient that reached 86.68% and 0.79, respectively. The algorithm can be integrated with a portable PSG system for home sleep assessment. On the other hand, Koley and Dey (2012) worked on the automatic identification of various stages of sleep, such as sleep stages 1, 2, slow wave sleep (sleep stages 3 and 4), REM sleep and wakefulness for single-channel EEG signal. The automatic punctuation of the sleep stages was carried out with the help of a pattern recognition technique that involves the extraction of characteristics, selection and finally classification. The agreement of the estimated sleep stages with those obtained by experts for all sleep stages of the training data set was 0.877 and for the independent test data set it reached 0.8572. The proposed method could be used as an efficient and cost-effective way to stage sleep with the advantage of reducing stress and the burden imposed on the subjects. 

On the other hand, Bajaj and Pachori (2013) applied a new method for the automatic classification of the sleep stage based on the time-frequency image (TFI) of the EEG signals. The results show good effectiveness of the proposed method for classifying the stages of sleep from EEG signals.

Also, Hassan and Bhuiyan (2016) proposed an automated sleep scoring method based on single channel EEG. A new technique of signal processing is used, the "TQWT" for the staging of sleep. The effectiveness of the method is confirmed by statistical analysis. The performance of the proposed scheme, compared to the existing ones, is promising. The proposed scheme will relieve the burden on physicians, accelerate the diagnosis of sleep disorder and accelerate sleep research.

Other advances have also been achieved regarding the nature of the EEG signals, which are are non-linear and non-stationary. It is difficult to perform sleep staging through visual interpretation and linear techniques (Acharya, Chua, Chua, Min, & Tamura, 2010). Therefore, the authors use a non-linear technique, the higher-order spectra (HOS), to extract information hidden in the EEG signal of the dream. Their results indicate that the proposed system is able to identify the stages of sleep with an accuracy of 88.7%.

References

Acharya, U. R., Chua, E. C. P., Chua, K. C., Min, L. C., & Tamura, T. (2010). Analysis and automatic identification of sleep stages using higher order spectra. International journal of neural systems, 20(06), 509-521.

Aguilera Olivares, L., Díaz, S., & Sánchez, G. (2012). Trastornos del sueño en el paciente adulto hospitalizado.

Ancoli-Israel, S. (1989). Epidemiology of sleep disorders. Clinics in geriatric medicine, 5(2), 347-362.

Bajaj, V., & Pachori, R. B. (2013). Automatic classification of sleep stages based on the time-frequency image of EEG signals. Computer methods and programs in biomedicine, 112(3), 320-328.

Benetó, A. (2000). Trastornos del sueño en el anciano. Epidemiología. Revista de Neurología, 30(6), 581-585.

Bixler, E. O., Kales, A., Soldatos, C. R., Kales, J. D., & Healey, S. (1979). Prevalence of sleep disorders in the Los Angeles metropolitan area. The American journal of psychiatry.

Collop, N. A. (2008). Portable monitoring for the diagnosis of obstructive sleep apnea. Current opinion in pulmonary medicine, 14(6), 525-529.

Convertini, G., Krupitzky, S., Tripodi, M. R., & Carusso, L. (2003). Trastornos del sueño en niños sanos. Arch argent pediatr, 101(2), 99-105.

Fry, J. M., DiPhillipo, M. A., Curran, K., Goldberg, R., & Baran, A. S. (1998). Full polysomnography in the home. Sleep, 21(6), 635-642.

Fursow, Y. B., Betancourt, C. J., & León, J. C. J. (2006). Trastorno por déficit de atención e hiperactividad y trastornos del sueño. Revista de neurología, 42(2), 37-51.

Hassan, A. R., & Bhuiyan, M. I. H. (2016). A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features. Journal of neuroscience methods, 271, 107-118.

Hillman, D. R., Murphy, A. S., Antic, R., & Pezzullo, L. (2006). The economic cost of sleep disorders. Sleep, 29(3), 299-305.

Jiménez, M. A. G., Redondo-Martínez, M. P., Marcos-Navarro, A. I., Torrijos-Martínez, M. P., Aguilar, F. S., Monterde-Aznar, M. L., & Rodríguez-Almonacid, F. M. (2004). Prevalencia de los trastornos del sueño en adolescentes de Cuenca, España. Revista de neurología, 39(1), 18-24.

Koley, B., & Dey, D. (2012). An ensemble system for automatic sleep stage classification using single channel EEG signal. Computers in biology and medicine, 42(12), 1186-1195.

Liang, S. F., Kuo, C. E., Hu, Y. H., & Cheng, Y. S. (2012). A rule-based automatic sleep staging method. Journal of neuroscience methods, 205(1), 169-176.

Liang, S. F., Kuo, C. E., Hu, Y. H., Pan, Y. H., & Wang, Y. H. (2012). Automatic Stage Scoring of Single-Channel Sleep EEG by Using Multiscale Entropy and Autoregressive Models. IEEE Trans. Instrumentation and Measurement, 61(6), 1649-1657.

Lucey, B. P., Mcleland, J. S., Toedebusch, C. D., Boyd, J., Morris, J. C., Landsness, E. C., ... & Holtzman, D. M. (2016). Comparison of a single‐channel EEG sleep study to polysomnography. Journal of sleep research, 25(6), 625-635.

Mykytyn, I. J., Sajkov, D., Neill, A. M., & Mc Evoy, R. D. (1999). Portable computerized polysomnography in attended and unattended settings. Chest, 115(1), 114-122.

Owens, J. (2007). Classification and epidemiology of childhood sleep disorders. Sleep Medicine Clinics, 2(3), 353-361

Principe, J. C., Gala, S. K., & Chang, T. G. (1989). Sleep staging automaton based on the theory of evidence. IEEE Transactions on Biomedical Engineering, 36(5), 503-509.

Rosen, C. L., Auckley, D., Benca, R., Foldvary-Schaefer, N., Iber, C., Kapur, V., ... & Redline, S. (2012). A multisite randomized trial of portable sleep studies and positive airway pressure autotitration versus laboratory-based polysomnography for the diagnosis and treatment of obstructive sleep apnea: the HomePAP study. Sleep, 35(6), 757-767.

Sateia, M. J. (2014). International classification of sleep disorders. Chest, 146(5), 1387-1394.

Sierra, J. C., Jiménez-Navarro, C., & Martín-Ortiz, J. D. (2002). Calidad del sueño en estudiantes universitarios: importancia de la higiene del sueño. Salud mental, 25(6), 35-43.