Sleep stage classification via ensemble and conventional machine learning methods using single channel EEG signals

Abstract

Sleep-stages play important roles in the diagnosis of the sleep disorders and the sleep-related illnesses. In this sense, accurate identification of the sleep-stages is a necessity for more robust and efficient diagnosis systems. Several traditional machine-learning and pattern recognition algorithms are deployed on the modern computer aided diagnosis systems. However, current results are not as satisfactory as expected. In the last two decade, a new concept has emerged with 'ensemble learning' title. It has attracted the attention of many researchers from various disciplines. In this study, several ensemble-learning methods are utilized and inspected on EEG signals for sleep-stage classification. Conventional machine-learning methods are also performed in same testing phase to report comparative results. Additionally, methods are evaluated in two different scenarios; subject specific and independent. Study proves that combination of DTs and SVMs in bagging theorem surpasses all of the conventional methods used in the experiments. Moreover, test trials reveal that both conventional and ensemble models need to be improved for subject independent scenario which is more essential case in the development of patient independent computer based diagnosis systems.

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

“…(2) Decision Tree Classifier (DT): It is one of the structured and comprehends classification techniques incomparable to another classification algorithm. Majorly DT used by different type's classification tasks [64] and the major cause behind this is its simplicity and ease of understanding the rules regarding tree structures. A decision tree is constructed from a considered training dataset and each sample of the dataset is contained feature values and its class labels.…”

Applying Machine Learning Algorithms for the Classification of Sleep Disorders

“…Deep learning algorithms are a likely replacement for traditional machine intelligence. Deep learning refers to any algorithm that employs layers for data processing, and the process of feature engineering is automatic [6], [7]. Deep learning models are particularly suitable for classification tasks that involve considerable data or complex features.…”

Feature Extraction From Single-Channel EEG Using Tsfresh and Stacked Ensemble Approach for Sleep Stage Classification

“…From every subband, statistical features are extracted, and using an RF classifier reached 91.5% accuracy in 5-class classification. In (Ilhan, 2017), the decomposed the Fpz-Cz EEG channel signals into sub-bands such as delta (δ), alpha (α), beta (β), theta (θ), saw-tooth, spindle, and K-complex. Energy features are extracted and provided to the ensemble classifier from these frequency bands.…”