A New Era in Sleep Monitoring: The Application of Mobile Technologies in Insomnia Diagnosis

Hamida, Sana Tmar-Ben; Ahmed, Beena; Cvetković, Dean; Jovanov, Emil; Kennedy, Gerard; Penzel, Thomas

doi:10.1007/978-3-319-12817-7_5

Cited by 11 publications

(5 citation statements)

References 41 publications

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“…Kaplan et al [29] studied that A1-A2 channel are used in the automatic detection of sleep-wake. Penzel's assembly stated that Insomnia could be identified through Hjorth parameters and classifies the system using the deep learning classifiers [30], [31]. The ECG signal is a noninvasive and low-cost method; it can be easily applied in screening of insomnia.…”

Section: Literature Surveymentioning

confidence: 99%

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

et al. 2022

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Insomnia is a common sleep disorder in which patients cannot sleep properly. Accurate detection of insomnia disorder is a crucial step for disease analysis in the early stages. The disruption in getting quality sleep is one of the big sources of cardiovascular syndromes such as blood pressure and stroke. The traditional insomnia detection methods are timeconsuming, cumbersome, and more expensive because they demand a long time from a trained neurophysiologist, and they are prone to human error, hence, the accuracy of diagnosis gets compromised. Therefore, the automatic insomnia diagnosis from the electrocardiogram (ECG) records is vital for timely detection and cure. In this paper, a novel hybrid approach based on the power spectral density (PSD) of the heart rate variability (HRV) is proposed to detect insomnia in three classification scenarios:(1) subject-based classification scenario (normal Vs. insomnia), (2) sleep stage-based classification (REM Vs. W. stage), and (3) the combined classification scenario using both subject-based and sleep stage-based features. The ensemble learning of random forest (RF) and decision tree (DT) classifiers are used to perform the first and second classification scenarios, while the linear discriminant analysis (LDA) classifier is used to perform the third combined scenario. The proposed framework includes data collection, investigation of the ECG signals, extraction of the signal HRV, estimation of the PSD, and AI-based classification via hybrid machine learning classifiers. The proposed framework is fine-tuned and evaluated using the free public Physio Net dataset over fivefold trails cross-validation. For the subject-based classification scenario, the detection performance in terms of sensitivity, specificity, and accuracy is recorded to be 96.0%, 94.0%, and 96.0%, respectively. For the sleep stage-based classification scenario, the detection evaluation results are recorded equally with 96.0% for ceiling level accuracy, sensitivity, and specificity. For the combined classification scenario, the LDA classifier have achieved the best insomnia detection accuracy of 99.0% of the three cases as discussed. In future, the proposed approach could be applicable for mobile observation schemes to automatically detect insomnia disorder.

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Section: Literature Surveymentioning

confidence: 99%

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

et al. 2022

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“…eHealth can also include digital health applications on mobile devices, such as mobile phones, tablets, personal digital assistants, and the wireless infrastructure. The mode is also referred to as mobile health (mHealth) (Hamida et al, 2015). Online health consultation is a common eHealth service and can allow patients to use mobile devices to access to medical consultations with online doctors.…”

Section: Literature Review Ehealthmentioning

confidence: 99%

An Investigation of Patient Decisions to Use eHealth

Zheng

Chang

Chen

et al. 2021

Journal of Organizational and End User Computing

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eHealth service has received increasing attention. Patients can consult online doctors via the Internet, and then physically visit the doctors for further diagnosis and treatments. Although extant research has focused on the adoption of eHealth services, the decision-making process from online to offline health services still remains unclear. This study aims to examine patients’ decisions to use online and offline health services by integrating the extended valence framework and the halo effect. By analyzing 221 samples with online consultation experiences, the results show that trust significantly influences perceived benefits and perceived risks, while trust, perceived benefits, and perceived risks significantly influence the intention to consult. The intention to consult positively influences the intention to visit. Considering the moderating effects of payment types, the influence of perceived risks on the intention to consult is larger for the free group than for the paid group. The findings are useful to better understand patients’ decisions to use eHealth.

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“…In practice, the unobtrusive, low-cost, and ease-of-use of wearable sensor technologies, such as wrist-worn actigraphy, enable sleep monitoring at home [9], [10]. In the past decade, a relatively high performance of sleep/wake detection using actigraphy has been achieved for healthy subjects without sleep disturbances [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%