Drowsiness detection for single channel EEG by DWT best m-term approximation

Abstract

Introduction: In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG) signal preprocessed through a discrete wavelet transform. Methods: In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results: By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR) of 84.98% and 98.65% of precision (PPV). Conclusion: The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.

Sleep Stages Classification Using Spectral Based Statistical Moments as Features

“…Therefore it is well accepted by the scientific community that one EEG channel shall sufficiently provide information to classify the sleep stages as stated in [11], and also explored in [5] and [32]. The recordings analyzed here are formatted in the EDF standard and contain two EEG channels, Pz-Oz and Fpz-Cz, sampled at 100Hz.…”

“…Among non-invasive techniques, EEG is considered one of the most reliable source for sleep staging [3], supporting an increasing number of works [4,5,6,7]. Their main attempts are both to enhance classification performance and reducing PSG acquisition channels, seeking highly accurate and efficient portable sleep classification systems.…”

EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach

“…Sleeping state begins with the activation of neurons and brain inhibition. The transformation of awaking or alertness state to unconscious or drowsiness state is described by certain rhythmic changes [8][9][10]: (i) decreased the beta rhythmic (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) activity, (ii) increase in alpha rhythm activity (8)(9)(10)(11)(12)(13) but best observable while resting by eyes closed; and (iii) increased theta rhythm activity (4-8 Hz) if consequently alpha rhythm decreased.…”

“…In addition to that, these works did not mention any statistical threshold level for the mental alertness states by which method highly alertness or non-alertness can be monitored practically while performing risky works. On the other hand, authors in [9,[25][26] proposed different feature selection methods to enhance the classification accuracy or overall detection performance. Authors in [9] describe a drowsiness detection system where the feature is selected based on the most responsible m terms approximation of the DWT expansion.…”

“…On the other hand, authors in [9,[25][26] proposed different feature selection methods to enhance the classification accuracy or overall detection performance. Authors in [9] describe a drowsiness detection system where the feature is selected based on the most responsible m terms approximation of the DWT expansion. Authors claimed that this method alleviates the use of complex techniques.…”