A novel insomnia identification method based on Hjorth parameters

Hamida, Sana Tmar-Ben; Ahmed, Beena; Penzel, Thomas

doi:10.1109/isspit.2015.7394397

Cited by 13 publications

(7 citation statements)

References 27 publications

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“…In paper [21], a support vector machine (SVM) classifier was employed to distinguish the control group and insomnia patients. A k-means classifier was presented using Hjorth parameters extracted from the central electroencephalogram (EEG) signals to accurately detect insomnia [22].…”

Section: Insomnia Detectionmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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Sleep recognition refers to detection or identification of sleep posture, state or stage, which can provide critical information for the diagnosis of sleep diseases. Most of sleep recognition methods are limited to single-task recognition, which only involves single-modal sleep data, and there is no generalized model for multi-task recognition on multi-sensor sleep data. Moreover, the shortage and imbalance of sleep samples also limits the expansion of the existing machine learning methods like support vector machine, decision tree and convolutional neural network, which lead to the decline of the learning ability and overfitting. Self-supervised learning technologies have shown their capabilities to learn significant feature representations. In this paper, a novel self-supervised learning model is proposed for sleep recognition, which is composed of an upstream self-supervised pre-training task and a downstream recognition task. The upstream task is conducted to increase the data capacity, and the information of frequency domain and the rotation view are used to learn the multi-dimensional sleep feature representations. The downstream task is undertaken to fuse bidirectional long-short term memory and conditional random field as the sequential data recognizer to produce the sleep labels. Our experiments shows that our proposed algorithm provide promising results in sleep identification and can further be applied in clinical and smart home environments as a diagnostic tool. The source code is provided at: ''https://github.com/zhaoaite/SSRM ''.

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Section: Insomnia Detectionmentioning

confidence: 99%

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

2020

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“…We use three Hjorth parameters, Activity, M obility, and Complexity. Activity represents a signal strength, scatter of a time function [23]:…”

Section: Preprocessingmentioning

confidence: 99%

“…where (y(t)) represents the signal. M obility represents the mean frequency -the share of the standard deviation of the power spectrum [23]:…”

Section: Preprocessingmentioning

confidence: 99%

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

2019

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Electroencephalograph (EEG) is a commonly used method in neurological practice. Automatic classifiers (algorithms) highlight signal sections with interesting activity and assist an expert with record scoring. Algorithm K-means is one of the most commonly used methods for EEG inspection. In this paper, we propose/apply a method based on density-oriented algorithms DBSCAN and DENCLUE. DBSCAN and DENCLUE separate the nested clusters against K-means. All three algorithms were validated on a testing dataset and after that adapted for a real EEG records classification. 24 dimensions EEG feature space were classified into 5 classes (physiological, epileptic, EOG, electrode, and EMG artefact). Modified DBSCAN and DENCLUE create more than two homogeneous classes of the epileptic EEG data. The results offer an opportunity for the EEG scoring in clinical practice. The big advantage of the proposed algorithms is the high homogeneity of the epileptic class.

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“…For example, Pezard et al (16) reported that depressive subjects tend to display lower complexity than control subjects. Moreover, it has been reported that decreased complexity and mobility are associated with insomniac subjects (17). Studies concerning post-traumatic stress disorder and panic disorder have reported lower Hjorth complexity in patients in pathological states when compared to healthy subjects (18).…”

Section: Introductionmentioning

confidence: 99%

Lack of a Sense of Threat and Higher Emotional Lability in Patients With Chronic Microvascular Ischemia as Measured by Non-linear EEG Parameters

Portnova

2020

Front. Neurol.

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We aimed to investigate the non-linear features of the electroencephalogram (EEG) findings in patients with chronic microvascular ischemia (CMI) in order to determine the brain correlates of emotional impairment that could impact the risk of developing acute ischemia. We compared the EEG responses of patients with CMI and age-matched healthy volunteers to non-verbal emotionally charged sounds. We analyzed the EEG data, the psychological assessment of the stimuli, and the results of neuropsychological and behavioral testing. We assessed the (in)stability of the envelope's amplitude by calculating its average frequency and the ratio of its standard deviation to its mean. The non-linear features were lower in the patient group in the resting state. The emotional stimulation induced a decrease in the frequency of the envelope's amplitude in all subjects. Changes in the fractal dimension during stimulation were only seen in the patient group, and they correlated with symptoms of emotional lability. The lower ratio of the alpha-rhythm envelope's standard deviation to its mean in the right hemisphere correlated with a higher sense of threat. The EEG and behavioral correlates of emotional impairment in patients with CMI were found.

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A novel insomnia identification method based on Hjorth parameters

Cited by 13 publications

References 27 publications

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Self-Supervised Learning From Multi-Sensor Data for Sleep Recognition

Automatic Eeg Classification Using Density Based Algorithms Dbscan and Denclue

Lack of a Sense of Threat and Higher Emotional Lability in Patients With Chronic Microvascular Ischemia as Measured by Non-linear EEG Parameters

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