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Proceedings ArticleDOI

Sleep stages classification using wavelettransform & neural network

TL;DR: The feature extraction of the EEG Signal is done by computing the Discrete Wavelet Transform by using neural network which provides more accurate sleep stage classification compared to other techniques.
Abstract: In this paper the feature extraction of the EEG Signal is done by computing the Discrete Wavelet Transform. The wavelet transform coefficients compress the number of data points into few features. Various statistics were used to further reduce the dimensionality. The Classification of the EEG sleep stages is done by using neural network which provides more accurate sleep stage classification compared to other techniques.
Citations
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Journal ArticleDOI
23 Aug 2016-Entropy
TL;DR: A novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals is presented.
Abstract: Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

243 citations


Cites methods from "Sleep stages classification using w..."

  • ...org/physiobank/database/sleep-edfx/) and has been widely used in the literature [2,7,11,12,16,18,24,33,36,38,48,49,57,87,91,105,106,108,109,114]....

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Proceedings ArticleDOI
07 Sep 2015
TL;DR: DoppleSleep provides a single sensor solution to track sleep-related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat as well as several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency.
Abstract: In this paper, we present DoppleSleep -- a contactless sleep sensing system that continuously and unobtrusively tracks sleep quality using commercial off-the-shelf radar modules. DoppleSleep provides a single sensor solution to track sleep-related physical and physiological variables including coarse body movements and subtle and fine-grained chest, heart movements due to breathing and heartbeat. By integrating vital signals and body movement sensing, DoppleSleep achieves 89.6% recall with Sleep vs. Wake classification and 80.2% recall with REM vs. Non-REM classification compared to EEG-based sleep sensing. Lastly, it provides several objective sleep quality measurements including sleep onset latency, number of awakenings, and sleep efficiency. The contactless nature of DoppleSleep obviates the need to instrument the user's body with sensors. Lastly, DoppleSleep is implemented on an ARM microcontroller and a smartphone application that are benchmarked in terms of power and resource usage.

155 citations


Cites methods from "Sleep stages classification using w..."

  • ...Since EEG has been established as an accurate method to monitor sleep stages [21, 25], commercial sleep sensors like the Zeo [7] assess sleep quality by measuring electrical activity in the brain....

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Journal ArticleDOI
Junming Zhang1, Yan Wu1
TL;DR: A new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages, which can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract Features from raw data.
Abstract: Traditionally, automatic sleep stage classification is quite a challenging task because of the difficulty in translating open-textured standards to mathematical models and the limitations of handcrafted features. In this paper, a new system for automatic sleep stage classification is presented. Compared with existing sleep stage methods, our method can capture the sleep information hidden inside electroencephalography (EEG) signals and automatically extract features from raw data. To translate open sleep stage standards into machine rules recognized by computers, a new model named fast discriminative complex-valued convolutional neural network (FDCCNN) is proposed to extract features from raw EEG data and classify sleep stages. The new model combines complex-valued backpropagation and the Fisher criterion. It can learn discriminative features and overcome the negative effect of imbalance dataset. More importantly, the orthogonal decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron are proven. A speed-up algorithm is proposed to reduce computational workload and yield improvements of over an order of magnitude compared to the normal convolution algorithm. The classification performances of handcrafted features and different convolutional neural networks are compared with that of the FDCCNN. The total accuracy and kappa coefficient of the proposed method are 92% and 0.84, respectively. Experiment results demonstrated that the performance of our system is comparable to those of human experts.

82 citations

Journal ArticleDOI
Junming Zhang1, Yan Wu1
TL;DR: Results show that unsupervised training and automatic feature extraction on sleep data are possible, which are very important for home sleep monitoring.

39 citations

Journal ArticleDOI
21 Jul 2017-Sensors
TL;DR: A novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique is presented.
Abstract: Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

27 citations


Cites background from "Sleep stages classification using w..."

  • ...First, the PSG device extracts electroencephalography (EEG) information from EEG sensors attached to a patient’s head [5,6]....

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References
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Book
16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.

29,130 citations

Journal ArticleDOI
Olivier Rioul1, Martin Vetterli
TL;DR: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes, which includes nonstationary signal analysis, scale versus frequency,Wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing.
Abstract: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes. The discussion includes nonstationary signal analysis, scale versus frequency, wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing. The main definitions and properties of wavelet transforms are covered, and connections among the various fields where results have been developed are shown. >

2,945 citations


"Sleep stages classification using w..." refers methods in this paper

  • ...Every 15-30 min epochs are classified in different sleep stages, according to the structure of the signal and rules defined by Rechtschaffen and Kales [7] [8]....

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Journal Article
TL;DR: The motivations for, the fundamentals and some applications of Neural Networks are discussed and this exciting new area to statisticians is introduced and hopefully will serve to stimulate statistical research into the subject.
Abstract: Currently an ever increasing number of articles on Neural Networks are appearing in especially the engineering and computer science journals. Neural Networks are applied to a wide variety of problems, but considerable successes have been obtained when applying Neural Networks to classification problems. Statisticians concern themselves with such problems and have, over the years, proposed various techniques for solving classification problems. In this article we will discuss the motivations for, the fundamentals and some applications of Neural Networks. The paper will introduce this exciting new area to statisticians and hopefully will serve to stimulate statistical research into the subject.

9 citations

Proceedings ArticleDOI
27 May 2008
TL;DR: The paper proposed a new ERP extraction algorithm using artificial neural network and discussed the configuration, learning and running of the designed network.
Abstract: The event related potentials (ERPs) is an electrical change recorded from the brain in relation to an event that occurs either in the external world or within the brain itself. Here we are to design a system capable of learning a particular mapping between ERPs and different mental tasks is of great significance. ERPs is traditionally extracted by averaging. The paper proposed a new ERP extraction algorithm using artificial neural network. It discussed the configuration, learning and running of the designed network. The partial least square regression was introduced to train the neural network. Series experiments showed that the method is effective and is suitable for single-trail ERP estimation.

8 citations

Proceedings ArticleDOI
28 Oct 1993
TL;DR: The overall spike detection accuracy comparing with the inspection of esperienced physicians is 88.7% and the scheme uses 16 heuristic features as the input of a back propagation neural network.
Abstract: This paper proposes a scheme for detecting epileptiform of EEG. The scheme uses 16 heuristic features as the input of a back propagation neural network. 300 sharp transients (ST), i.e. spike and sharp waves (SSW), obtained from 12 epilepsy patients and 1000 non-SSW obtained from 10 nonnal subjects are used lo evaluate the performance of this classifier. The overall spike detection accuracy comparing with the inspection of esperienced physicians is 88.7%.

2 citations