Insomnia and mortality among older people in five Latin American countries: a population based cohort study

Mazzotti, Diego R.; Garbuio, S.; Ferri, Matteo; Tufik, Sérgio; Ferri, Cleusa P.

doi:10.1016/j.sleep.2013.11.484

Sleep Medicine

2013

DOI: 10.1016/j.sleep.2013.11.484

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Insomnia and mortality among older people in five Latin American countries: a population based cohort study

Diego R. Mazzotti

S. Garbuio

Matteo Ferri

et al.

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2024

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“…This disorder may cause conditions such as stroke, asthma attacks, impaired immune system, seizures, depression, diabetes mellitus, cardiovascular disease, anxiety, obesity, and hypertension. Mazzotti et al [7] also showed a crucial relation between the mortality rates and insomnia, in five Latin American countries.…”

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Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals

Kumar,

Patel

et al. 2024

IEEE Latin Am. Trans.

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It is essential to have enough sleep for a healthy life; otherwise, it may lead to sleep disorders such as apnea, narcolepsy, insomnia, and periodic leg movements. A polysomnogram (PSG) is typically used to analyze sleep and identify different sleep disorders. This work proposes a novel convolutional neural network (CNN)-based technique for insomnia detection using single-channel electroencephalogram (EEG) signals instead of complex PSG. Morlet wavelet-based continuous wavelet transforms and smoothed pseudo-Wigner-Ville distribution (SPWVD) are explored in the proposed method to obtain scalograms of EEG signals of duration 1salong with convolutional layers for features extraction and image classification. The Morlet transform is found to be a better time-frequency distribution. We have developed Morlet wavelet-based CNN (MWTCNNet) for the classification of healthy and insomniac patients using cyclic alternating pattern (CAP) and sleep disorder research centre (SDRC) databases with C4-A1 single-channel EEG derivation. We have used multiple cohorts/settings of the CAP and SDRC databases to analyse the performance of proposed model. The proposed MWTCNNet achieved an accuracy, sensitivity, and specificity of 98.9%, 99.03%, and 98.66%, respectively, using the CAP database, and 99.03%, 99.20%, and 98.87%, respectively, with the SDRC database. Our proposed model performs better than existing state-of-the-art models and can be tested on a vast, diverse database before being installed for clinical application.

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confidence: 92%

Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals

Kumar,

Patel

et al. 2024

IEEE Latin Am. Trans.

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Insomnia and mortality among older people in five Latin American countries: a population based cohort study

Cited by 1 publication

References 0 publications

Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals

Time frequency distribution and deep neural network for automated identification of insomnia using single channel EEG-signals

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