Heart rate variability for medical decision support systems: A review

Faust, Oliver; Hong, Wanrong; Loh, Hui Wen; Xu, Shuting; Tan, Ru‐San; Chakraborty, Subrata; Barua, Prabal Datta; Molinari, Filippo; Acharya, U. Rajendra

doi:10.1016/j.compbiomed.2022.105407

Cited by 40 publications

(29 citation statements)

References 136 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HRV is also used extensively in clinical setting with many different types of patients ( 61 , 62 ). HRV is then seen mainly as a valuable diagnostic metrics of health.…”

Section: Monitoring Of Fatigue and Adaptationsmentioning

confidence: 99%

Look to the stars—Is there anything that public health and rehabilitation can learn from elite sports?

Millet

Chamari

2023

Front. Sports Act. Living

View full text Add to dashboard Cite

show abstract

“…HRV is also used extensively in clinical setting with many different types of patients ( 61 , 62 ). HRV is then seen mainly as a valuable diagnostic metrics of health.…”

Section: Monitoring Of Fatigue and Adaptationsmentioning

confidence: 99%

Look to the stars—Is there anything that public health and rehabilitation can learn from elite sports?

Millet

Chamari

2023

Front. Sports Act. Living

View full text Add to dashboard Cite

show abstract

“…While our results are promising, there is still much work to be done in this field. One avenue for future research is to explore the use of Shapley analysis using explainable artificial intelligence (XAI) to better understand the contributions of individual features to the overall prediction 50 . This will allow us to identify which biomarkers are most important for the accurate diagnosis of sleep disorders and potentially develop more targeted treatments.…”

Section: Discussionmentioning

confidence: 99%

Sleep disorder identification using wavelet scattering on ECG signals

Sharma,

Lodhi,

Yadav

et al. 2023

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The potential workflow for such a device is depicted in figure 10, and it is believed that using this configuration would lead to higher-quality recordings. Also, future research can be carried out by focusing on multi-night sleep monitoring and observing sleep patterns in a more natural environment to improve the accuracy of automated detection techniques for insomnia (Faust et al 2022). The use of EOG signals, which are used to research eye movements and create assistive devices, could be a good alternative for monitoring sleep in a more comfortable environment for the study subject to reduce false detection (Kuo and Chen 2020).…”

Section: Future Directionmentioning

confidence: 99%

A systematic review on automatic identification of insomnia

Ingle,

Sharma,

Kumar

et al. 2024

Physiol. Meas.

View full text Add to dashboard Cite

Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection.Methods: We followed the recommendations given in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a se- lection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various meth- ods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intel- ligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this de- tection task.Results: Result: The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential com- ponents under investigation in the automated technique include the data input source, objective, machine learning (ML) and deep learning (DL) network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types.Conclusion: Based on our review of the studies featured in this paper, we have identi- fied a notable research gap in the current methods for identifying insomnia and oppor- tunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.

show abstract

Heart rate variability for medical decision support systems: A review

Cited by 40 publications

References 136 publications

Look to the stars—Is there anything that public health and rehabilitation can learn from elite sports?

Look to the stars—Is there anything that public health and rehabilitation can learn from elite sports?

Sleep disorder identification using wavelet scattering on ECG signals

A systematic review on automatic identification of insomnia

Contact Info

Product

Resources

About