Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data

We propose a method to discover sleep patterns via clustering of sound events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in sound processing and popular kernel functions to the proposed method to evaluate and compare performance. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that sound events can be directly correlated to an individual's sleep patterns. In addition, by visualizing the transition of cluster dynamics, sleep-related sound events were found to relate to the various stages of sleep. Therefore, these results empirically warrant future study into the assessment of personal sleep quality using sound data.

[1]  S. Chokroverty,et al.  The visual scoring of sleep in adults. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[2]  Masayuki Numao,et al.  Neighborhood-Based Smoothing of External Cluster Validity Measures , 2012, PAKDD.

[3]  João S. Domingos,et al.  A review of current sleep screening applications for smartphones , 2013, Physiological measurement.

[4]  Fabrice Rossi,et al.  Batch kernel SOM and related Laplacian methods for social network analysis , 2008, Neurocomputing.

[5]  D. Dickinson,et al.  A practical validation study of a commercial accelerometer using good and poor sleepers , 2016, Health psychology open.

[6]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[7]  Stan Davis,et al.  Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Se , 1980 .

[8]  Yunhao Liu,et al.  Intelligent sleep stage mining service with smartphones , 2014, UbiComp.

[9]  Balaji Goparaju,et al.  Consumer sleep monitors: is there a baby in the bathwater? , 2015, Nature and science of sleep.

[10]  Masayuki Numao,et al.  Sequence-based SOM: Visualizing transition of dynamic clusters , 2008, 2008 8th IEEE International Conference on Computer and Information Technology.

[11]  Péter András.  Kernel-Kohonen Networks , 2002, Int. J. Neural Syst..

[12]  Simon Haykin,et al.  The Cocktail Party Problem , 2005, Neural Computation.

[13]  Vangelis Metsis,et al.  Non-invasive analysis of sleep patterns via multimodal sensor input , 2012, Personal and Ubiquitous Computing.

[14]  Rebecca M. C. Spencer,et al.  Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography , 2016, Sensors.

[15]  T. Kato,et al.  Masseter EMG activity during sleep and sleep bruxism. , 2011, Archives italiennes de biologie.

[16]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[17]  Fanglin Chen,et al.  Unobtrusive sleep monitoring using smartphones , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[18]  Guoliang Xing,et al.  iSleep: unobtrusive sleep quality monitoring using smartphones , 2013, SenSys '13.

[19]  G. Lavigne,et al.  Sleep Bruxism: Validity of Clinical Research Diagnostic Criteria in a Controlled Polysomnographic Study , 1996, Journal of dental research.

[20]  Olli Simula,et al.  Process Monitoring and Visualization Using Self-Organizing Maps , 1995 .

[21]  R. Stickgold,et al.  Dreaming and the brain: Toward a cognitive neuroscience of conscious states , 2000, Behavioral and Brain Sciences.

[22]  Kiyoyuki Yamazaki,et al.  The assessment of sleep stage utilizing body pressure fluctuation measured by water mat sensors , 2009 .

[23]  David Gozal,et al.  Sleep Duration, Sleep Regularity, Body Weight, and Metabolic Homeostasis in School-aged Children , 2011, Pediatrics.

[24]  Sudhansu Chokroverty,et al.  Sleep Disorders Medicine. Basic Science, Technical Considerations and Clinical Aspects. , 1999 .

[25]  Teuvo Kohonen,et al.  Essentials of the self-organizing map , 2013, Neural Networks.

[26]  Eun Kyoung Choe,et al.  Opportunities for computing to support healthy sleep behavior , 2010, CHI Extended Abstracts.

[27]  Masayuki Numao,et al.  Visualization of Damage Progress in Solid Oxide Fuel Cells , 2011 .

[28]  I Feinberg,et al.  Changes in sleep cycle patterns with age. , 1974, Journal of psychiatric research.

[29]  Masayuki Numao,et al.  Sleep Pattern Discovery via Visualizing Cluster Dynamics of Sound Data , 2016, IEA/AIE.

[30]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..