Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks

Sharma, Manish; Patel, Sohamkumar; Choudhary, Siddhant; Acharya, U. Rajendra

doi:10.1007/s13369-019-04197-8

Cited by 30 publications

(23 citation statements)

References 71 publications

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“…where σ 2 ω gives the frequency response and E gives the energy of the filter. In this work, to design the optimal equilateral wavelet filter X ( Z ) with reduced MSFL the following constraints should satisfy, such as zero moments or regularity 25 and equality of the order M 31 (ie, the X ( Z ) contain M zeros at Z = −1). We have changed the design problem into an optimization problem, 32 as:

\overset{\min}{x ((), n)} σ^{2} ω = \frac{1}{2 italicπE} \int_{- π}^{π} ω^{2} {|X_{0} (e^{jw})|}^{2} italicdω

…”

Section: Methodsmentioning

confidence: 99%

“…A recent survey by Chakladar et al 24 reported a comparison of the EEG signal classification of different authors based on low‐cost computation. We conclude it from the previous works, among signal transformation analysis; wavelet transform is the most applicable option to gain the dynamic nature of the signal in frequency domain and time domain 25 . In this research work, we used wavelet transform to analyze the EEG signal for wavelet components.…”

Section: Introductionmentioning

confidence: 95%

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A novel two‐band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals

Ashokkumar

MohanBabu

Anupallavi

2020

Int J Imaging Syst Tech

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One can determinate the occurrence of epileptic seizure from the electroencephalogram (EEG) signal. Nonautomatic epilepsy detection is onerous and may be prone to error. They have augmented automated detection of seizure methods to attain accurate results. In view of this research work, we designed a frequency localized optimal filter bank to assess their effectiveness for automatic detection of seizures from EEG records. The basic preferred requirement of optimal filters relies on low bandwidth in the discipline of biomedical signal processing. This work provides a novel filter bank method called optimal equilateral wavelet filter bank (OEWFB) to satisfy the regularity criteria. This regularity constraint is being satisfied with semi‐definite programming (SDP) framework, which specifically does nothing with any parameterization. Implementing the proposed filter banks, it disbands EEG signals into five wavelet sub‐bands. The fuzzy entropy (FuEn), Renyi's entropy (ReEn), and the Kraskov entropy (KrEn) are being used for extracting the features from the wavelet sub‐bands. The P values provide the distinctive ability of the features. Classification with 10‐fold cross‐validation for several classifiers such as quadratic discriminant, linear quadratic discriminant, K‐nearest neighbor, support vector machine, logistic regression, and complex tree is utilized to classify the EEG signals into seizure vs non‐seizure class and seizure‐free vs seizure affected class. The proposed research work has gained the highest accuracy, specificity, sensitivity, and positive predictive values of 99.4%, 99%, 99.66%, and 99.35%, respectively, for class‐1 (ABCD vs E). The performances of the proposed work using the Bonn EEG data set ensure validation concerning compatibility and robustness.

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\overset{\min}{x ((), n)} σ^{2} ω = \frac{1}{2 italicπE} \int_{- π}^{π} ω^{2} {|X_{0} (e^{jw})|}^{2} italicdω

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

A novel two‐band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals

Ashokkumar

MohanBabu

Anupallavi

2020

Int J Imaging Syst Tech

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“…The Pz-Oz channel of the S-EDF database is used according to the recommendations of various studies [ 3 , 4 , 5 , 6 , 7 ]. At the same time, for the DRMS database, as the researches [ 9 , 10 , 11 , 12 ] recommended, the Cz-A1 channel was adopted in this work. Moreover, for the ISRUC database, the C3-A2 channel is the best choice [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

“…Firstly, all the single-channel data will be extracted by the Matlab and EEGLAB [ 22 ] tools from the three database described previously. According to the prior work [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ], the 0–35 Hz low pass filter can be used to eject the most of artifact. Once the dataset is filtered, it will be exported as one-dimensional vector without time information and saved as txt file which also can be denoted as the Formula ( 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…The experimental results yields classification accuracies for the classification of six to two sleep stages 96.03%, 96.39%, 96.48%, 97.56% and 99.36%, respectively. Sharma et al computed the discriminatory features namely fuzzy entropy and log energy by the wavelet decomposition coefficients [ 9 ]. This approach yielded an accuracy of 91.5% and 88.5% for six-class classification task using small and large datasets, respectively.…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Shen

Ran

et al. 2020

Sensors

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The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen’s and Kale’s (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

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Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

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Sleep affects the functioning of all biological processes in the human body. Sleep disorders disrupt the well‐being of an individual and can potentially lead to other health complications. It affects a large part of the population, and their timely and efficient detection is crucial for improving the quality of health. Sleep disorders are generally analyzed using polysomnogram (PSG) signals, which must be captured in sophisticated laboratory settings. However, electrocardiogram (ECG) signals provide information about the electrical activity of the heart, which is strongly linked with disturbances in sleep and can be used as an indicator of disordered sleep. Along with that, the recording of ECG signals is much easier than other signals and can be done remotely without causing any significant disturbance to the subject. This brings practicality to the systems that are already being employed for sleep disorder detection. In this paper, we present a method for identifying types of sleep disorders using ECG signals. The objective of this paper is to investigate the use of ECG signals for the automated identification of insomnia, narcolepsy, periodic leg movement (PLM), rapid eye movement (REM) behavior disorder (RBD), and nocturnal frontal lobe epilepsy (NFLE) against healthy subjects. We aim to develop a machine learning‐based algorithm that automatically classifies ECG signals into these sleep disorder categories. The cyclic alternating pattern (CAP) sleep database was used in this study, which contains PSG recordings from individuals with and without sleep disorders, including insomnia, narcolepsy, PLM, RBD, and NFLE. A wavelet scattering network has been used to extract features from the ECG signals. Various classifiers were tested, and the ensemble bag of trees classifier provided the optimum performance. An overall accuracy of 98% was obtained for the identification of sleep disorders, along with a classification accuracy of 99.37%, 99.45%, 99.23%, 99.4%, and 99.65% for insomnia, narcolepsy, NFLE, PLM, and RBD, respectively, for binary classification against healthy subject data. Our results show that wavelet scattering network and ensemble of bagged tree (EbagT) classification can accurately identify various sleep disorders. They provided evidence that ECG signals can be used to identify and diagnose sleep disorders, which could lead to the development of accurate detection of these sleep disorders. Also, our study demonstrated the effectiveness of the proposed algorithm in identifying multiple sleep disorders, which could lead to more efficient and cost‐effective diagnosis and treatment of sleep disorders.

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Automated Detection of Sleep Stages Using Energy-Localized Orthogonal Wavelet Filter Banks

Cited by 30 publications

References 71 publications

A novel two‐band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals

A novel two‐band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

Sleep disorder identification using wavelet scattering on ECG signals

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