Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals

Sharma, Manish; Tiwari, Jainendra; Patel, Virendra; Acharya, U. Rajendra

doi:10.3390/electronics10131531

Cited by 32 publications

(14 citation statements)

References 50 publications

(47 reference statements)

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“…The Z-score normalization is calculated using MATLAB 2016 [ 3 , 36 , 37 ] for the BCG signals. The Z-score value for each BCG signals was calculated using Equation ( 1 ) as described in [ 36 , 38 , 39 ].

…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Rajput

Sharma

Kumar

et al. 2022

IJERPH

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Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.

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…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Rajput

Sharma

Kumar

et al. 2022

IJERPH

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“…Electroencephalograms (EEGs) can be used for sleep stage classification, which is highly desirable for many emerging technologies, including telemedicine and home healthcare [331]. Sharma et al [332] were able to identify six sleep disorder types using electroencephalography signals, including insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). Radha et al [333] designed a home-based system for monitoring sleep using heart rate variability (HRV) may be a cost-efficient and ergonomic alternative to polysomnography.…”

Section: Sleep Monitoringmentioning

confidence: 99%

Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery using BERT Modelling

Alqahtani¹,

Janbi²,

Sharaf³

et al. 2022

Preprint

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Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

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“…The doctor identifies sleep disorders on the basis of the patient's responses to questionnaires and an evaluation of the Pittsburgh Sleep Quality Index (PSQI), [11][12][13] which is widely used to assess sleep quality. However, these questionnaire-based procedures are incredibly subjective, based on memory, unpredictable, and prone to human error, as the subject is asleep when the disturbances occur.…”

Section: Introductionmentioning

confidence: 99%

“…They contain questions about the psychometric properties of sleep. Sleep diaries are maintained for a period of at least a week, whereas questionnaires are filled by staying at a sleep clinic for 3–4 h. The doctor identifies sleep disorders on the basis of the patient's responses to questionnaires and an evaluation of the Pittsburgh Sleep Quality Index (PSQI), 11–13 which is widely used to assess sleep quality. However, these questionnaire‐based procedures are incredibly subjective, based on memory, unpredictable, and prone to human error, as the subject is asleep when the disturbances occur.…”

Section: Introductionmentioning

confidence: 99%

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 Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals

Cited by 32 publications

References 50 publications

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with Ballistocardiography Signals

Smart Homes and Families to Enable Sustainable Societies: A Data-Driven Approach for Multi-Perspective Parameter Discovery using BERT Modelling

Sleep disorder identification using wavelet scattering on ECG signals

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