☆ 4.4 Article

INSOMNet: Automated insomnia detection using scalogram and deep neural networks with ECG signals

MEDICAL ENGINEERING & PHYSICS (2023)

Related references

Note: Only part of the references are listed.

Article Engineering, Biomedical

An automatic sleep-scoring system in elderly women with osteoporosis fractures using frequency localized finite orthogonal quadrature Fejer Korovkin kernels

Bharti Jogi Dakhale et al.

Summary: Healthy sleep is important for physical and mental well-being, but factors like work schedules and medical complications can lead to sleep disorders. This study proposes a new method for automated sleep stage classification using machine learning and EEG signals. By analyzing data from 453 subjects, the developed model achieved a classification accuracy of 81.3%.

MEDICAL ENGINEERING & PHYSICS (2023)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Uncertainty quantification in DenseNet model using myocardial infarction ECG signals

V. Jahmunah et al.

Summary: A Dirichlet DenseNet model was developed to analyze out-of-distribution data and detect misclassification of myocardial infarction (MI) and normal electrocardiogram (ECG) signals. The model was trained with pre-processed MI ECG signals from the PTB database and showed increased confidence in classifying signals with lower levels of noise. The model proved reliable in diagnosing MI.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2023)

Add to Collection

Article Engineering, Electrical & Electronic

Integrated S-Transform-Based Learning System for Detection of Arrhythmic Fetus

Kapil Gupta et al.

Summary: Measurement of abnormal heartbeat rhythm of a fetus to detect arrhythmia is important for assessing the health status of the fetus. However, recording clean fetal-electrocardiogram (f-ECG) signals under free-living conditions presents operational difficulties. This study proposes a new learning system based on the S-transform to denoise the f-ECG signals and detect arrhythmic fetuses.

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT (2023)

Add to Collection

Article Computer Science, Information Systems

Automated Explainable Detection of Cyclic Alternating Pattern (CAP) Phases and Sub-Phases Using Wavelet-Based Single-Channel EEG Signals

Manish Sharma et al.

Summary: Sleep is important for health and well-being, covering one-third of life. The traditional evaluation of sleep quality does not consider important transient phenomena, such as K-complexes and transient fluctuations, which are crucial for diagnosing sleep disorders. This study proposes an automated, computerized approach for classifying sleep phases using a machine learning model with explainable artificial intelligence capabilities based on wavelet-based Hjorth parameters.

IEEE ACCESS (2023)

Add to Collection

Article Biology

Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals

Joel E. W. Koh et al.

Summary: ADHD and CD are the most prevalent neuropsychiatric disorders among children, and the lack of definitive diagnostic tests makes diagnosis challenging. Distinguishing between these three disorders is crucial for treatment and prognosis. Our model achieved good classification results by analyzing ECG signals.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Add to Collection

Article Engineering, Biomedical

Hyp-Net: Automated detection of hypertension using deep convolutional neural network and Gabor transform techniques with ballistocardiogram signals

Kapil Gupta et al.

Summary: This study proposes a fully automated hypertension detection system using time-frequency spectral images and convolutional neural networks for accurate detection of hypertension. The experimental results demonstrate the effectiveness of the system in hypertension detection.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2022)

Add to Collection

Review Computer Science, Interdisciplinary Applications

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022)

Hui Wen Loh et al.

Summary: This study focuses on the application areas of XAI technology in healthcare, emphasizing the need for more attention from the XAI research community in detecting abnormalities in 1D biosignals and identifying key text in clinical notes.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

Add to Collection

Review Biology

A review of automated sleep disorder detection

Shuting Xu et al.

Summary: Automated sleep disorder detection is challenging due to wide variations in physiological symptoms. Research shows that digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders, with the emergence of deep learning algorithms demanding more labelled data for effective detection.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Add to Collection

Article Medicine, General & Internal

Deep Learning for Automatic Detection of Periodic Limb Movement Disorder Based on Electrocardiogram Signals

Erdenebayar Urtnasan et al.

Summary: In this study, a deep learning model called deepPLM was used to automatically detect PLMS based on ECG signals. The model achieved high performance with F1-score, precision score, and recall score of over 90%. The results demonstrate the potential of using the deepPLM model as an alternative method for PLMS screening and a useful tool for home healthcare services for the elderly population.

DIAGNOSTICS (2022)

Add to Collection

Article Computer Science, Information Systems

Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal

Pragati Tripathi et al.

Summary: This paper proposes a hybrid artificial intelligence approach based on power spectral density of heart rate variability to automatically detect insomnia disorders. The approach achieves accurate diagnosis of insomnia through three different classification scenarios, enabling timely detection and treatment.

IEEE ACCESS (2022)

Add to Collection

Article Biology

Automatic identification of insomnia using optimal antisymmetric biorthogonal wavelet filter bank with ECG signals

Manish Sharma et al.

Summary: Sleep is crucial for human body functioning, and poor sleep quality can lead to sleep disorders, with insomnia being a common one. In the automated detection of insomnia, using electrocardiogram signals is an innovative approach.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Add to Collection

Article Medicine, General & Internal

AI-Enabled Algorithm for Automatic Classification of Sleep Disorders Based on Single-Lead Electrocardiogram

Erdenebayar Urtnasan et al.

Summary: The study introduced an AI-enabled algorithm for automatic classification of major sleep disorders based on single-lead ECG, achieving high performance. The constructed SDN utilizing deep convolutional neural networks demonstrated great potential as a convenient and accurate tool for screening and identifying sleep disorders.

DIAGNOSTICS (2021)

Add to Collection

Article Computer Science, Information Systems

Automated Method for Detecting Acute Insomnia Using Multi-Night Actigraphy Data

Maia Angelova et al.

IEEE ACCESS (2020)

Add to Collection

Review Multidisciplinary Sciences

Measures of cardiovascular autonomic activity in insomnia disorder: A systematic review

Marina-Marinela Nano et al.

PLOS ONE (2017)

Add to Collection

Review Clinical Neurology

Heart rate variability in insomnia patients: A critical review of the literature

Kirsty L. Dodds et al.

SLEEP MEDICINE REVIEWS (2017)

Add to Collection

Article Computer Science, Information Systems

Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis

Mostafa Shahin et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2017)

Add to Collection

Article Multidisciplinary Sciences

EEG/EOG/EMG data from a cross sectional study on psychophysiological insomnia and normal sleep subjects

Mohammad Rezaei et al.

DATA IN BRIEF (2017)

Add to Collection

Review Public, Environmental & Occupational Health

An Overview of Heart Rate variability Metrics and Norms

Fred Shaffer et al.

FRONTIERS IN PUBLIC HEALTH (2017)

Add to Collection

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Add to Collection

Article Medicine, General & Internal

Insomnia disorder

Charles M. Morin et al.

NATURE REVIEWS DISEASE PRIMERS (2015)

Add to Collection

Article Clinical Neurology

Heart Rate and Heart Rate Variability Modification in Chronic Insomnia Patients

Benedetto Farina et al.

BEHAVIORAL SLEEP MEDICINE (2014)

Add to Collection

Article Clinical Neurology

Performance evaluation of an automated single-channel sleep-wake detection algorithm

Richard F. Kaplan et al.

NATURE AND SCIENCE OF SLEEP (2014)

Add to Collection

Article Clinical Neurology

Nighttime cardiac sympathetic hyper-activation in young primary insomniacs

M. de Zambotti et al.

CLINICAL AUTONOMIC RESEARCH (2013)

Add to Collection

Article Psychology, Biological

REM and NREM power spectral analysis on two consecutive nights in psychophysiological and paradoxical insomnia sufferers

Genevieve St-Jean et al.

INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY (2013)

Add to Collection

Article Health Care Sciences & Services

Singular Spectrum Analysis of Sleep EEG in Insomnia

Serap Aydin et al.

JOURNAL OF MEDICAL SYSTEMS (2011)

Add to Collection

Article Clinical Neurology

The Insomnia Severity Index: Psychometric Indicators to Detect Insomnia Cases and Evaluate Treatment Response

Charles M. Morin et al.

SLEEP (2011)

Add to Collection

Article Psychology, Biological

Age-related differences in prefrontal control of heart rate in humans: A pharmacological blockade study

Julian F. Thayer et al.

INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY (2009)

Add to Collection

Article Clinical Neurology

Proposed diagnostic criteria of Internet addiction for adolescents

CH Ko et al.

JOURNAL OF NERVOUS AND MENTAL DISEASE (2005)

Add to Collection

Article Multidisciplinary Sciences

Sleep inspires insight

U Wagner et al.

NATURE (2004)

Add to Collection

Article Clinical Neurology

Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep

Mario Giovanni Terzano et al.

SLEEP MEDICINE (2002)

Add to Collection

© Peeref 2019-2024. All rights reserved.