HRV and EEG Signal Features for Computer-Aided Detection of Sleep Apnea

Estrada, Edson; Nazeran, Homer; Ochoa, Humberto

doi:10.1007/978-3-642-01697-4_93

Cited by 1 publication

(7 citation statements)

References 7 publications

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“…• Match Pursuits: is used with dictionaries of Gabor functions in time-frequency analysis of signals and has following advantages (i) explicit parametrization of transients, (ii) robust time frequency estimate [41].…”

Section: ) Time and Frequency Domain Featuresmentioning

confidence: 99%

“…Multilayer Perceptron: This type of neural network comprises three categories of layers: input, (multiple) hidden, and output layers. Any continuous function can be arbitrarily well approximated, provided the hidden layer is made large enough [41]. It may be flexible for classification but too sensitive to over-fitting [75].…”

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

“…Self-Organizing Maps: This allows visualization of multidimensional data [41] and clusters data into several classes. It is suited for applications with a small amount of input data and no output available [41]. In the cooperative learning the neurons not only adjust themselves to the data but also to the neighbouring neurons as well.…”

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

“…Support Vector Machine: This technique is useful both for classification as well as for regression problems. It is based on the design of an optimal hyperplane which classifies all training vectors into two classes and this optimal hyperplane leaves the maximum margins to the two classes [41]. It can be used to classify data in both linear and non-liner classifications.…”

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%

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A Systematic Review of Literature on Automated Sleep Scoring

Alsolai

Qureshi

Iqbal³

et al. 2022

IEEE Access

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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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Section: ) Time and Frequency Domain Featuresmentioning

confidence: 99%

Section: Types Of Machine Learning Techniquesmentioning

confidence: 99%