EEG sleep stages identification based on weighted undirected complex networks

Sleep scoring is important in sleep research because any errors in the scoring of the patient's sleep electroencephalography (EEG) recordings can cause serious problems such as incorrect diagnosis, medication errors, and misinterpretations of patient's EEG recordings. The aim of this research is to develop a new automatic method for EEG sleep stages classification based on a statistical model and weighted brain networks. Methods each EEG segment is partitioned into a number of blocks using a sliding window technique. A set of statistical features are extracted from each block. As a result, a vector of features is obtained to represent each EEG segment. Then, the vector of features is mapped into a weighted undirected network. Different structural and spectral attributes of the networks are extracted and forwarded to a least square support vector machine (LS-SVM) classifier. At the same time the network's attributes are also thoroughly investigated. It is found that the network's characteristics vary with their sleep stages. Each sleep stage is best represented using the key features of their networks. Results In this paper, the proposed method is evaluated using two datasets acquired from different channels of EEG (Pz-Oz and C3-A2) according to the R&K and the AASM without pre-processing the original EEG data. The obtained results by the LS-SVM are compared with those by Naïve, k-nearest and a multi-class-SVM. The proposed method is also compared with other benchmark sleep stages classification methods. The comparison results demonstrate that the proposed method has an advantage in scoring sleep stages based on single channel EEG signals. Conclusions An average accuracy of 96.74% is obtained with the C3-A2 channel according to the AASM standard, and 96% with the Pz-Oz channel based on the R&K standard

Implicit night sleep monitoring using smartphones

Recent studies show that many people nowadays suffer from sleep disorders, which can severely threaten the public health. Sleep monitoring could play an important role; since it makes it possible to recognize them at the early stages and prevent them. Moreover, there are sort of methods, devices and special sensors as well as mobile phone applications, which try to realize the demand for sleep monitoring. Although all of these techniques require either a special device or sensor to be used or some user interactions, no approach has been proposed, that tracks sleep either in an unobtrusive way or without using any extra sensor. To put it in a nut shell, we have tried in this work to figure out if it is viable, and if so, how efficient it could be to monitor the nightly sleep using smartphones without any need to interact with the phone or without using separate devices and/or sensors.Bisherige Studien zeigen, dass immer mehr Menschen unter Schlafstörungen leiden, was sich negativ auf die Gesundheit auswirken kann. Schlafüberwachung bietet eine Möglichkeit, Schlafstörungen in einer früheren Entstehungsphase zu erkennen und ihnen vorzubeugen. Es existieren bereits zahlreiche Methoden, Geräte und Sensoren, sowie Smartphone Anwendungen mit welchen die Schlafüberwachung durchgeführt werden kann. Der Nachteil aller dieser Techniken liegt darin, dass diese zur Schlafüberwachung entweder einen zusätzlichen Sensor benötigen oder das Eingreifen des Benutzers erfordern. In dieser Arbeit wird die Frage untersucht, ob man mit Hilfe des Smartphones den Schlaf unauffällig überwachen kann, bzw. ob eine Schlafüberwachung ohne Bedarf von zusätzlichen Sensoren und/oder Interaktion mit dem Benutzer möglich ist