Sleep stage classification using the combination of SVM and PSO

Surantha, Nico; Isa, Sani Muhamad; Lesmana, Tri Fennia; Setiawan, I Made Agus

doi:10.1109/icicos.2017.8276358

Cited by 6 publications

(7 citation statements)

References 14 publications

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“…Studies on sleep stage classification use ECG signal, rather than multi signal to reduce the complexity and increase comfort because the use of ECG signal could produce high accuracy [19]. Several methods of machine learning have been used to analyze the data from ECG to identify sleep stages (e.g., sleep, NREM, REM, awake) [20][21][22][23][24][25] and sleep disorders [26][27][28]. The sleep disorders are not covered in this work since the study is focused on sleep stage classification.…”

Section: Related Workmentioning

confidence: 99%

“…The study was performed based on HRV data and achieved 77.00 ± 8.90% for four classes of sleep stages. Our previous study combined SVM with PSO in which SVM was deployed for classification while PSO was utilized for feature selection [24]. By using HRV as a method to classify 2 sleep stages, PSO was able to increase accuracy from ≈ 72% to 78.41%.…”

Section: Related Workmentioning

confidence: 99%

“…Two types of kernel functions can be used in SVM which is linear kernel function and RBF kernel. RBF was chosen in this work, which provided better accuracy in our previous work [24].…”

Section: Second Algorithm: Support Vector Machinementioning

confidence: 99%

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Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021

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Recent developments of portable sensor devices, cloud computing, and machine learning algorithms have led to the emergence of big data analytics in healthcare. The condition of the human body, e.g. the ECG signal, can be monitored regularly by means of a portable sensor device. The use of the machine learning algorithm would then provide an overview of a patient’s current health on a regular basis compared to a medical doctor’s diagnosis that can only be made during a hospital visit. This work aimed to develop an accurate model for classifying sleep stages by features of Heart Rate Variability (HRV) extracted from Electrocardiogram (ECG). The sleep stages classification can be utilized to predict the sleep stages proportion. Where sleep stages proportion information can provide an insight of human sleep quality. The integration of Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) was utilized for selecting features and determining the number of hidden nodes. The results were compared to Support Vector Machine (SVM) and ELM methods which are lower than the integration of ELM with PSO. The results of accuracy tests for the combined ELM and PSO were 62.66%, 71.52%, 76.77%, and 82.1% respectively for 6, 4, 3, and 2 classes. To sum up, the classification accuracy can be improved by deploying PSO algorithm for feature selection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

2021

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“…Most of the studies have classified sleep into 2 (sleep and wake or light and deep sleep) [23][24][25][26] or 3 stages (wake, REM and NREM) only [8,[27][28][29][30]. Very few studies have classified sleep into 4 stages [18,27], 5 stages [31] and 6 stages [27] using various classification models.…”

Section: Introductionmentioning

confidence: 99%

“…These sleep epochs are not independent entities and bear a temporal relation to each other. Most of the studies have used non-temporal models, like Support Vector Machine [23][24][25], Random Forest Classifier [30], Extreme Learning Machine [24,26], Back Propagation Neural Network [24], Linear Discriminant Classifier [8] to classify sleep stages using HRV. This may result in loss of temporal information from the data leading to decreased classification performance.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach for automatic sleep staging using heart rate variability

Chakraborty

Goyal

et al. 2022

Preprint

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Sleep stage classification from heart-rate variability using long short-term memory neural networks

Radha

Fonseca

Moreau³

et al. 2019

Sci Rep

131

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Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

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Sleep stage classification using the combination of SVM and PSO

Cited by 6 publications

References 14 publications

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

Sleep stage classification using extreme learning machine and particle swarm optimization for healthcare big data

A machine learning approach for automatic sleep staging using heart rate variability

Sleep stage classification from heart-rate variability using long short-term memory neural networks

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