34 research outputs found
Detecting Slow Wave Sleep Using a Single EEG Signal Channel
Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal.
New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences.
Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812.
Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods
ΠΠ΅ΡΠΎΠ΄ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π³ΠΈΠΏΠ½ΠΎΠ³ΡΠ°ΠΌΠΌΡ
Π Π°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΡΡΡ ΠΌΠ΅ΡΠΎΠ΄ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ°ΡΠΏΠΎΠ·Π½Π°Π²Π°Π½ΠΈΡ ΡΡΠ°Π΄ΠΈΠΉ ΡΠ½Π° ΠΈ ΠΏΠΎΡΡΡΠΎΠ΅Π½ΠΈΡ Π³ΠΈΠΏΠ½ΠΎΠ³ΡΠ°ΠΌΠΌΡ. ΠΠ»Ρ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ Π½Π° ΡΠ΅Π³ΠΌΠ΅Π½ΡΡ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΎΠΌΠ½ΠΎΠ³ΡΠ°ΠΌΠΌΡ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΠΎΠΉ Π² ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³Π° ΡΠ½Π° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°, Π°Π½Π°Π»ΠΈΠ·ΠΈΡΡΠ΅ΡΡΡ ΡΠ½Π΅ΡΠ³ΠΈΡ ΡΠΈΠ³Π½Π°Π»Π° Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π½Π΅Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠ³ΠΎ ΡΠ½Π΅ΡΠ³Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡΠ°. Π Π°ΡΡΡΡ ΡΠ°ΡΡΠΎΡΠ½ΠΎ-Π²Π·Π²Π΅ΡΠ΅Π½Π½ΠΎΠΉ ΡΠ½Π΅ΡΠ³ΠΈΠΈ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ Π΄Π»Ρ Π²ΡΠ΅Ρ
ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ², Π΄Π°Π»Π΅Π΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡΡΡ ΡΡΡΠ΅Π΄Π½Π΅Π½ΠΈΠ΅ ΠΈ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΠ΅ Π½Π° ΡΠ΅Π³ΠΌΠ΅Π½ΡΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΡΡΠ΅ΠΌΡΡ
ΡΠΈΠ³Π½Π°Π»ΠΎΠ². ΠΠ»Ρ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΎΠ² ΡΠΎΡΠΌΠΈΡΡΠ΅ΡΡΡ Π²Π΅ΠΊΡΠΎΡ Π²ΡΠΎΡΠΈΡΠ½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, ΠΊΠΎΡΠΎΡΡΠΉ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ ΠΏΡΠΈ ΠΏΠ΅ΡΠ΅Ρ
ΠΎΠ΄Π΅ ΠΎΡ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠΎΠ² ΠΊ ΡΠΏΠΎΡ
Π°ΠΌ ΡΠΈΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ. ΠΠΊΠΎΠ½ΡΠ°ΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠΈΡΠ²ΠΎΠ΅Π½ΠΈΠ΅ ΡΠΏΠΎΡ
Π΅ ΡΠΎΠΉ ΠΈΠ»ΠΈ ΠΈΠ½ΠΎΠΉ ΡΡΠ°Π΄ΠΈΠΈ ΡΠ½Π° ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. Π’ΠΎΡΠ½ΠΎΡΡΡ ΡΠ°Π·ΡΠ°Π±Π°ΡΡΠ²Π°Π΅ΠΌΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΡΠ²ΡΠ·Π°Π½Π° Ρ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎΠΌ ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΠΌΡΡ
Π²ΠΎ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π²ΡΠΎΡΠΈΡΠ½ΡΡ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ, ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΠΎ ΠΏΠΎΠ΄ΡΠΎΠ±Π½ΠΎΠ³ΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² ΡΡΠ°Π΄ΠΈΠΉ ΡΠ½Π° ΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ°Ρ
, ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²Π»Π΅Π½Π½ΡΡ
Π²ΡΡΡΠ½ΡΡ
Technique of automated hypnogram construction
The technique of automated sleep stage recognition and hypnogram construction has been considered. For partition of initial polysomnogram by segments obtained as a result of patient sleep monitoring the signal energy is analyzed using nonlinear energy controller. Frequency weighted energy is calculated for all registered signals then averaging and segmentation occur according to monitored signals behavior. Secondary index vector which is used at transition from segments to fixed duration periods is formed for segments. One or another sleep stage is finally assigned to the period by correlation analysis. Accuracy of the developed algorithm is connected with quantity of considered secondary indices, maximally detailed description of sleep stage characteristics and realization of training by manually prepared example
A Survey on Feature Selection Algorithms
One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and feature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.
DOI: 10.17762/ijritcc2321-8169.16043
AlertNet: Deep convolutional-recurrent neural network model for driving alertness detection
Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driverβs alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively
A Development of Cognitive Assessment Tool based on Brain-Computer Interface for Accident Prevention
A number of Brain-Computer Interface (BCI) studies have been performed to assess the cognitive status through EEG signal. However, there are a few studies trying to prevent user from unexpected safety-accident in BCI study. The EEGs were collected from 19 subjects who participated in two experiments (rest & event-related potential measurement). There was significant difference in EEG changes of both spontaneous and event-related potential. Beta power and P300 latency may be useful as a biomarker for prevention of response to safety-accident.ope
Supervised neuronal approaches for EEG signal classification: experimental studies
Using artificial neural networks for Electroencephalogram (EEG) signal interpretation is a very challenging tasks for several reasons. The first class of reasons refers to the nature of data. Such signals are complex and difficult to process. The second class of reasons refers to the nature of underlying knowledge. Expertise is manifold and difficult to formalize and to be made compatible with a numerical processing. In previous studies we have deeply described that expertise and explained, from theoretical and bibliographical studies, why artificial neural networks could be interesting candidates to perform such a signal interpretation. In this paper, we report recent experiments that we have made on real EEG data in a classification framework. These results are interesting with regard to the state of the art. They also indicate that further work must be done on expertise integration in our neuronal platform
Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks
Current sleep medicine relies on the analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. This analysis currently requires supervision of a trained expert. Convolutional neural networks (CNN) provide an interesting framework to automated classification of sleep epochs based on raw EEG, EOG and EMG waveforms. In this study, we apply CNN approaches from the literature to four databases from pathological and physiological subjects. The best performing model resulted in Cohenβs Kappa of k = 0.75 on healthy subjects and k = 0.64 on patients suffering from a variety of sleep disorder. Further, we show the advantages of using additional sensor data such as EOG and EMG. Last, to cope with smaller datasets of less prevalent diseases, we propose a transfer learning procedure using large freely available databases for pre-training. This procedure is demonstrated using a private REM Behaviour Disorder database, improving sleep classification by 24.4%