Human Sleep Scoring Based on K-Nearest Neighbors

Qureshi, Shahnawaz; Karrila, Seppo; Vanichayobon, Sirirut

doi:10.3906/elk-1805-12

Cited by 8 publications

(7 citation statements)

References 23 publications

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“…Three classifiers were compared in terms of the classification performance parameters to select a suitable classifier for a provided EEG data set. The KNN classifier, also called the lazy learner algorithm, assumes similarity between available data and new data and assigns the most similar class [ 55 ]. The KNN algorithm calculates the distance by utilizing the distance measure, e.g., Euclidean and Manhattan distance measures.…”

Section: Methodsmentioning

confidence: 99%

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

2022

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Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

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Section: Methodsmentioning

confidence: 99%

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

2022

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“…The Rechtschaffen and Kales (R&K) criteria, first published in 1968, describe five phases of sleep in healthy individuals but were superseded in 2007 by the American Academy of Sleep Medicine’s (AASM) sleep scoring rules[ 16 ]. The AASM and R&K scoring rules share many similarities (Table 3 ) but show relatively low concordance when scoring NREM phases[ 15 - 17 , 122 , 123 ]. Moreover, both lack accuracy in quantifying the atypical sleep seen in the critically ill[ 124 ].…”

Section: Objective Measurement Of Sleep In the Critically Illmentioning

confidence: 99%

Sleep during and following critical illness: A narrative review

Showler¹,

Abdelhamid²,

Goldin³

et al. 2023

World J Crit Care Med

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Sleep is a complex process influenced by biological and environmental factors. Disturbances of sleep quantity and quality occur frequently in the critically ill and remain prevalent in survivors for at least 12 mo. Sleep disturbances are associated with adverse outcomes across multiple organ systems but are most strongly linked to delirium and cognitive impairment. This review will outline the predisposing and precipitating factors for sleep disturbance, categorised into patient, environmental and treatment-related factors. The objective and subjective methodologies used to quantify sleep during critical illness will be reviewed. While polysomnography remains the gold-standard, its use in the critical care setting still presents many barriers. Other methodologies are needed to better understand the pathophysiology, epidemiology and treatment of sleep disturbance in this population. Subjective outcome measures, including the Richards-Campbell Sleep Questionnaire, are still required for trials involving a greater number of patients and provide valuable insight into patients’ experiences of disturbed sleep. Finally, sleep optimisation strategies are reviewed, including intervention bundles, ambient noise and light reduction, quiet time, and the use of ear plugs and eye masks. While drugs to improve sleep are frequently prescribed to patients in the ICU, evidence supporting their effectiveness is lacking.

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“…K-nearest Neighbors: It's a nonlinear lazy learning technique that can be used to solve regression and classification problems. It makes predictions based on known labels of the K closest neighbours [102], [103] according to some distance function, often the Euclidean distance.…”

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

A Systematic Review of Literature on Automated Sleep Scoring

Alsolai

Qureshi

Iqbal³

et al. 2022

IEEE Access

Self Cite

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Sleep is a period of rest that is essential for functional learning ability, mental health, and even the performance of normal activities. Insomnia, sleep apnea, and restless legs are all examples of sleep-related issues that are growing more widespread. When appropriately analyzed, the recording of bio-electric signals, such as the Electroencephalogram, can tell how well we sleep. Improved analyses are possible due to recent improvements in machine learning and feature extraction, and they are commonly referred to as automatic sleep analysis to distinguish them from sleep data analysis by a human sleep expert. This study outlines a Systematic Literature Review and the results it provided to assess the present state-of-the-art in automatic analysis of sleep data. A search string was organized according to the PICO (Population, Intervention, Comparison, and Outcome) strategy in order to determine what machine learning and feature extraction approaches are used to generate an Automatic Sleep Scoring System. The American Academy of Sleep Medicine and Rechtschaffen & Kales are the two main scoring standards used in contemporary research, according to the report. Other types of sensors, such as Electrooculography, are employed in addition to Electroencephalography to automatically score sleep. Furthermore, the existing research on parameter tuning for machine learning models that was examined proved to be incomplete. Based on our findings, different sleep scoring standards, as well as numerous feature extraction and machine learning algorithms with parameter tuning, have a high potential for developing a reliable and robust automatic sleep scoring system for supporting physicians. In the context of the sleep scoring problem, there are evident gaps that need to be investigated in terms of automatic feature engineering techniques and parameter tuning in machine learning algorithms.

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Human Sleep Scoring Based on K-Nearest Neighbors

Cited by 8 publications

References 23 publications

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Sleep during and following critical illness: A narrative review

A Systematic Review of Literature on Automated Sleep Scoring

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