Feature Extraction and Detection of Obstructive Sleep Apnea from Raw EEG Signal

Kumari, Ch. Usha; Kora, Padmavathi; Meenakshi, K.; Swaraja, K.; Padma, T. V.; Panigrahy, Asisa Kumar; Vignesh, N. Arun

doi:10.1007/978-981-15-1286-5_36

Cited by 21 publications

(12 citation statements)

References 17 publications

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“…From Figure 5 , it is evident that neural network-based classification is predominantly used for classification and identification of apneic events. Long short-term memory (LSTM) networks yielded a higher accuracy of 99% [ 61 , 94 , 95 ], followed by random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), Adaboost, linear regression (LR), and ANFIS classifier with accuracy around 97% [ 18 , 45 , 80 , 96 – 98 ]. The average accuracy of other classifiers is around 95%.…”

Section: Classificationmentioning

confidence: 99%

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[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

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Obstructive sleep apnea (OSA) is a sleep disorder characterized by periodic episodes of partial or complete upper airway obstruction caused by narrowing or collapse of the pharyngeal airway despite ongoing breathing efforts during sleep. Fall in the blood oxygen saturation and cortical arousals are prompted by this reduction in the airflow which lasts for at least 10 seconds. Impaired labor performance, debilitated quality of life, excessive daytime sleepiness, high snoring, and tiredness even after a whole night’s sleep are the primary symptoms of OSA. In due course, the long-standing contributions of OSA culminate in hypertension, arrhythmia, cerebrovascular disease, and heart failure. The traditional diagnostic approach of OSA is the laboratory-based polysomnography (PSG) overnight sleep study, which is a tedious and labor-intensive process that exaggerates the discomfort to the patient. With the advent of computer-aided diagnosis (CAD), automatic detection of OSA has gained increasing interest among researchers in the area of sleep disorders as it influences both diagnostic and therapeutic decisions. The research literature on sleep apnea published during the last decade has been surveyed, focusing on the varied screening approaches accustomed to identifying OSA events and the developmental knowledge offered by multiple contributors from the software perspective. The current study presents an overview of the pathophysiology of OSA, the detection methods, physiological signals related to OSA, the different preprocessing, feature extraction, feature selection, and classification techniques employed for the detection and classification of OSA. Consequently, the research challenges and research gaps in the diagnosis of OSA are identified, critically analyzed, and presented in the best possible light.

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Section: Classificationmentioning

confidence: 99%

“…Standard statistical features extracted have also proven to be effective in the classification of sleep apnea. Kumari et al [ 96 ] have extracted only statistical mean from the signal and, using the SVM classifier, has achieved an accuracy of 98%, and the result is compared with KNN.…”

Section: Classificationmentioning

confidence: 99%

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

JeyaJothi

Anitha

Rani

et al. 2022

BioMed Research International

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“…In [5], the R-peak detection algorithm is introduces based on the QRS spec-trum and tested to the MIT-BIH database. Detecting R positions for normal and abnormal QRS-complex is done and the advantage of DWT for normal and abnormal beats is pointed [1].…”

Section: Fig 2 Proposed Methods For Heart Rhythm Abnormality Classificationmentioning

confidence: 99%

“…Linear and Non-linear filtering schemes are used to input QRS detector and filtering is done on the databases to pre-process. This gives a set of vectors produced from noise and complexes [5].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Depolarization and Repolarization of Cardiac Signal for Arrhythmia Classification

Kumari

Manzar

et al. 2021

Int. J. Onl. Eng.

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Irregular heartbeat results in heart diseases. Cardiac deaths are most seen across the globe. Detecting the heart problems in early stage can reduce the death rate. Electrocardiogram (ECG) is one of the most popular method for diagnosing different arrhythmias. Arrhythmia means irregular activity of heart or abnormal heart rhythm. In this paper, cardiac signal peaks P-wave, QRS complex and T-wave are detected for classifying the type of arrhythmia. These are the main components of ECG signal. P-wave is of very small duration, it is ex- plains about the atrial depolarization. The QRS complex may include combination of Q-wave, R-wave, and S-wave. But every QRS complex may not contain Q-R-S waves. It explains about ventricular depolarization. Whereas T wave is about ventricular re-polarization. S-Golay filter is used for denoising. This is used for smoothing the data which thereby, increases the precision of data without distortion of signal tendency. The patient data is collected from MIT-BIH Arrhythmia database for analysis. The simulation is done in Matlab software

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“…In recent years, several supervised and unsupervised algorithms have been proposed to segment retinal vessels. However, manual feature extraction is necessary for training with supervised approaches for different applications [36], [37].In below we can see the workflow of supervised and unsupervised algorithms.…”

Section: E Segmentationmentioning

confidence: 99%

Deepfakes on Retinal Images using GAN

Salini¹,

Harikiran²

2022

IJACSA

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In Deep Learning (DL), Generative Adversarial Networks (GAN) are a popular technique for generating synthetic images, which require extensive and balanced datasets to train. These Artificial Intelligence systems can produce synthetic images that seem authentic, known as Deep Fakes. At present, datadriven approaches to classifying medical images are prevalent. However, most medical data is inaccessible to general researchers due to standard consent forms that restrict research to medical journals or education. Our study focuses on GANs, which can create artificial fundus images that can be indistinguishable from actual fundus images. Before using these fake images, it is essential to investigate privacy concerns and hallucinations thoroughly. As well as, reviewing the current applications and limitations of GANs is very important. In this work, we present the Cycle-GAN framework, a new GAN network for medical imaging that focuses on the generation and segmentation of retinal fundus images.DRIVE retinal fundus image dataset is used to evaluate the proposed model's performance and achieved an accuracy of 98.19%.

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Feature Extraction and Detection of Obstructive Sleep Apnea from Raw EEG Signal

Cited by 21 publications

References 17 publications

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

[Retracted] A Comprehensive Review: Computational Models for Obstructive Sleep Apnea Detection in Biomedical Applications

Automated Detection of Depolarization and Repolarization of Cardiac Signal for Arrhythmia Classification

Deepfakes on Retinal Images using GAN

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