Sleep Stage Identification Using the Combination of ELM and PSO Based on ECG Signal and HRV

Lesmana, Tri Fennia; Isa, Sani Muhamad; Surantha, Nico

doi:10.1109/ccoms.2018.8463307

Cited by 16 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the increasing number of comparisons and the t level of difficulty in class determination. These results correspond to T. F. Lesmana, S. M. Isa, and N. Surantha [31], and O. K. Utomo, N. Surantha, S. M. Isa, and B. Soewito [32] showed that the accuracy of research on the testing data decreases in parallel with increase in the number of classes. The lowest accuracy in trials with 4 classes compared to 2 and 3 classes is also due to prediction errors for the cancer class.…”

Section: Average Accuracy Of Pso-elmsupporting

confidence: 82%

PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

Ratnawati¹,

Marjono²,

Widodo³

et al. 2020

IJIES

View full text Add to dashboard Cite

Classification of active compounds is necessary to trivialize their properties and activities and can be carried out by identifying functional groups and their structures based on the simplified molecular input line entry system (SMILES) code. This study classified active compounds based on the SMILES structure using a combination of the machine learning techniques, extreme learning machine (ELM), and particle swarm optimization (PSO). ELM facilitates learning very quickly and at with high predictive accuracy. The PSO covers the limitations of the ELM, i.e., randomly determining the weight and bias, trial, and error in determining the number of hidden neurons. PSO optimizes the weight, bias, and number of hidden neurons. This study’s results indicate that the classification of active compounds’ function based on the SMILES code can be done by the ELM and PSO-ELM with high accuracy. The PSO-ELM can improve the accuracy performance of the ELM classification by optimizing the weight, bias, and number of hidden neurons automatically. Moreover, the proposed PSO-ELM is better than the PSO-ELM for average accuracy, computation time, and standard deviation. The proposed algorithm has the highest average accuracy compared to the ELM by increasing the accuracy to 2.54%, 6.43%, and 3.85% for 2, 3, and 4 classes, respectively. A comparison with other machine algorithms shows that the proposed is superior.

show abstract

Section: Average Accuracy Of Pso-elmsupporting

confidence: 82%

PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

Ratnawati¹,

Marjono²,

Widodo³

et al. 2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Dream behaviour also involves movements in sleep called as REM parasomnia and treated as REM sleep behaviour disorder (RBD). Sleep is divided in a cyclic pattern in between N-REM and REM sleep stages [1][2][3][4][5][6].…”

Section: Methodsmentioning

confidence: 99%

“…OSA is found in abundance in middle age or elders and causes obesity [3] in most situation. It is recorded that 70 billion dollars loss, 11.1 billion in damages and 980 deaths in each year [5]. Most cases go undiagnosed because of the inconvenience, expenses and unavailability of testing.…”

Section: Introductionmentioning

confidence: 99%

Sleep Disorder Diagnosis through Frequency based EEG Energy Level Patterns

Tiwari¹

2020

IJATCSE

View full text Add to dashboard Cite

EEG data has proved to reflect the activities of brain over all the section with respect to human activities. They are useful in both cases that is in awakening states and in sleep stage. Most of the brain disease is due to the deterioration of brain cells. In this research work, EEG data is used to generate some frequency based energy level features for RBD patients and normal persons. After experimentation, It is found that percentage of energy levels may be considered as parameter to distinguish healthy and defected EEG data due to sleep disorder. During this study, it is also found that S0 is proved to be helpful in diagnosis of RBD sleep disorder because in this stage the percentage energy level is found to be low for alpha waves for patients having sleep disorder. The results shows that the average percentage energy level in RBD patient is 0.1139 while in normal cases it is 0.1530 during alpha activity.

show abstract

“…In 2016, Lesmana et al [15] classified sleep stages using a combination of ELM and PSO algorithms. A total of 18 features from the MIT-BIH polysomnographic database were utilized and achieved accuracy rates of 62.66%, 71.52%, 76.77%, and 82.1% for six, four, three, and two classes, respectively.…”

Section: The Classification Of Sleep Stagementioning

confidence: 99%

Intelligent Sleep Monitoring System Based on Microservices and Event-Driven Architecture

et al. 2022

Self Cite

View full text Add to dashboard Cite

Sleep monitoring using polysomnography (PSG) in hospitals can be considered expensive, so the preferable way is to use contactless and wearable sensors to monitor sleep daily by patients at home. In this work, the Internet of things (IoT) platform was utilized for sleep monitoring with contactless or wearable sensors as an integrated system developed based on an event-driven and microservice architecture. Multiple services that respond to events are provided within the system. Electrocardiogram (ECG) data were used as the input in the sleep monitoring system. The combination of the weighted extreme learning machine (WELM) algorithm with particle swarm optimization (PSO) was used to process the ECG data, followed by fuzzy logic to measure sleep quality, then display the data on the dashboard. Based on the experimental results, the proposed architecture increased throughput by 34.76%, decreased response time by 55.85%, and reduced memory consumption by 37.26% per instance replication compared to the non-event-driven architecture. The accuracies of the sleep stage classification were 78.78% and 73.09% for the three and four classes, respectively, and the area under the ROC curve (AUC) reached 0.89 for both the three and four class classification.

show abstract

Sleep Stage Identification Using the Combination of ELM and PSO Based on ECG Signal and HRV

Cited by 16 publications

References 13 publications

PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

PSO-ELM with Time-varying Inertia Weight for Classification of SMILES Codes

Sleep Disorder Diagnosis through Frequency based EEG Energy Level Patterns

Intelligent Sleep Monitoring System Based on Microservices and Event-Driven Architecture

Contact Info

Product

Resources

About