Multi-Layer Perceptron for Sleep Stage Classification

Yulita, Intan Nurma; Rosadi, Rudi; Purwani, Sri; Suryani, Mira

doi:10.1088/1742-6596/1028/1/012212

Cited by 13 publications

(5 citation statements)

References 10 publications

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“…The learning process is carried out by ANN by varying the weights of neural connections. The backpropagation technique is the most often used learning algorithm for training MLP models [17]. It makes use of an output error to change the value of its weights in reverse.…”

Section: Multi-layer Perceptronmentioning

confidence: 99%

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Yulita,

Afrida Helen,

Mira Suryani

2023

j. nas. pendidik. teknik. inform.

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In 2019, the COVID-19 pandemic appeared. There have been several efforts to curb the spread of this virus. West Java, Indonesia, employs social restrictions to prevent the spread of this disease. However, this method destroyed the economy of the people. If no instances were detected in the region, the World Health Organization (WHO) authorized the social restrictions to be relaxed. If the government lifts the social limitation, the decision must also consider the potential of future confirmed instances. By utilizing machine learning, it is possible to forecast future data. This work utilized the following algorithms: linear regression (LR), locally weighted learning (LWL), multi-layer perceptron (MLP), radial basis function regression (RBF), and support vector machine (SVM). The study investigated daily new instances of COVID-19 in West Java, Indonesia, from March 2, 2020, to October 15, 2020. The RBF algorithm was the best in this investigation. Mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) were 48.85, 89.73, 88.67, 62.99, and 60.88, respectively. The RBF prediction model may be proposed to the government of West Java for assessing data on COVID-19 instances, particularly in social restriction management. It is anticipated that West Java would have a minimum of 275 new cases every day for the following 30 days beginning on October 16, 2020. Consequently, the easing of societal limitations requires careful consideration.

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Section: Multi-layer Perceptronmentioning

confidence: 99%

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Yulita,

Afrida Helen,

Mira Suryani

2023

j. nas. pendidik. teknik. inform.

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“…An HCI system, also widely known as a brain-computer interface (BCI) system, converts the mental state (brain waves) of humans into computer commands which can be used by the disabled people to recover their environmental control capabilities (Wang et al, 2016). It can also be used to detect pre-seizure activities (Zhou et al, 2018) and sleep stages (Yulita et al, 2018). The human brain waves are usually captured using electroencephalography (EEG) sensors, with non-invasive sensors preferred over invasive sensors due to the fact that surgery is required for invasive sensors and those non-invasive sensors can be integrated into wearable devices (Mullen et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals

Kumar

Sharma

2021

PeerJ Computer Science

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A human–computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In this work, we address the problem of selecting the appropriate filtering frequency band while also achieving a good system performance by proposing a frequency-based approach using long short-term memory network (LSTM) for recognizing different brain wave signals. Adaptive filtering using genetic algorithm is incorporated for a hybrid system utilizing common spatial pattern and LSTM network. The proposed method (OPTICAL+) achieved an overall average classification error rate of 30.41% and a kappa coefficient value of 0.398, outperforming the state-of-the-art methods. The proposed OPTICAL+ predictor can be used to develop improved HCI systems that will aid in neurorehabilitation and may also be beneficial for sleep stage classification and seizure detection.

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“…12, NO. [11], we used a Convolutional Network and deep learning network that is more suitable for working with multidimensional data. Last, instead of just tying EDS features to alpha wave only, this research tried to step up the game by binding the feature with multiple brainwaves to improve the classifier performance [12].…”

Section: Introductionmentioning

confidence: 99%

Detecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG

Udayana¹,

Sudarma

Ariyani

2021

LKJITI

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Epworth sleepiness scale is a self-assessment method in sleep medicine that has been proven to be a good predictor of obstructive sleep apnea. However, the over-reliance of the method making the process not socially distancing friendly enough in response to a global covid-19 pandemic. A study states that the Epworth sleepiness scale is correlated with the brainwave signal that commercial-grade EEG can capture. This study tried to train a classifier powered by CNN and deep learning that could perform as well as the Epworth with the objectiveness of brainwave signal. We test the classifier using the 20 university student using the Epworth sleepiness test beforehand. Then, we put the participant in 10 minutes EEG session, downsampling the data for normalization purposes and trying to predict the outcome of the ESS in respect of their brainwave state. The AI predict the reaching 65% of accuracy and 81% of sensitivity with just under 100.000 dataset which is excellent considering small dataset although this still have plenty room for improvement.

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Multi-Layer Perceptron for Sleep Stage Classification

Cited by 13 publications

References 10 publications

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals

Detecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG

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