Detecting Slow Wave Sleep Using a Single EEG Signal Channel

Background: In addition to the cost and complexity of processing multiple signal channels, manual sleep staging is also tedious, time consuming, and error-prone. The aim of this paper is to propose an automatic slow wave sleep (SWS) detection method that uses only one channel of the electroencephalography (EEG) signal. New Method: The proposed approach distinguishes itself from previous automatic sleep staging methods by using three specially designed feature groups. The first feature group characterizes the waveform pattern of the EEG signal. The remaining two feature groups are developed to resolve the difficulties caused by interpersonal EEG signal differences. Results and comparison with existing methods: The proposed approach was tested with 1,003 subjects, and the SWS detection results show kappa coefficient at 0.66, an accuracy level of 0.973, a sensitivity score of 0.644 and a positive predictive value of 0.709. By excluding sleep apnea patients and persons whose age is older than 55, the SWS detection results improved to kappa coefficient, 0.76; accuracy, 0.963; sensitivity, 0.758; and positive predictive value, 0.812. Conclusions: With newly developed signal features, this study proposed and tested a single-channel EEG-based SWS detection method. The effectiveness of the proposed approach was demonstrated by applying it to detect the SWS of 1003 subjects. Our test results show that a low SWS ratio and sleep apnea can degrade the performance of SWS detection. The results also show that a large and accurately staged sleep dataset is of great importance when developing automatic sleep staging methods

Developing a Low-Cost Force Treadmill via Dynamic Modeling

By incorporating force transducers into treadmills, force platform-instrumented treadmills (commonly called force treadmills) can collect large amounts of gait data and enable the ground reaction force (GRF) to be calculated. However, the high cost of force treadmills has limited their adoption. This paper proposes a low-cost force treadmill system with force sensors installed underneath a standard exercise treadmill. It identifies and compensates for the force transmission dynamics from the actual GRF applied on the treadmill track surface to the force transmitted to the force sensors underneath the treadmill body. This study also proposes a testing procedure to assess the GRF measurement accuracy of force treadmills. Using this procedure in estimating the GRF of “walk-on-the-spot motion,” it was found that the total harmonic distortion of the tested force treadmill system was about 1.69%, demonstrating the effectiveness of the approach

Assessing Postural Stability Via the Correlation Patterns of Vertical Ground Reaction Force Components

Background Many methods have been proposed to assess the stability of human postural balance by using a force plate. While most of these approaches characterize postural stability by extracting features from the trajectory of the center of pressure (COP), this work develops stability measures derived from components of the ground reaction force (GRF). Methods In comparison with previous GRF-based approaches that extract stability features from the GRF resultant force, this study proposes three feature sets derived from the correlation patterns among the vertical GRF (VGRF) components. The first and second feature sets quantitatively assess the strength and changing speed of the correlation patterns, respectively. The third feature set is used to quantify the stabilizing effect of the GRF coordination patterns on the COP. Results In addition to experimentally demonstrating the reliability of the proposed features, the efficacy of the proposed features has also been tested by using them to classify two age groups (18–24 and 65–73 years) in quiet standing. The experimental results show that the proposed features are considerably more sensitive to aging than one of the most effective conventional COP features and two recently proposed COM features. Conclusions By extracting information from the correlation patterns of the VGRF components, this study proposes three sets of features to assess human postural stability during quiet standing. As demonstrated by the experimental results, the proposed features are not only robust to inter-trial variability but also more accurate than the tested COP and COM features in classifying the older and younger age groups. An additional advantage of the proposed approach is that it reduces the force sensing requirement from 3D to 1D, substantially reducing the cost of the force plate measurement system