The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert–Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time–frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.
Purpose Sleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID‑19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper. Methods We developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing. Results The averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements. Conclusion These experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.
Parameters derived from the goniometer measures in the Pendulum test are insufficient in describing the function of abnormal muscle activity in the spasticity. To explore a quantitative evaluation of muscle activation-movement interaction, we propose a novel index based on phase amplitude coupling (PAC) analysis with the consideration of the relations between movement and surface electromyography (SEMG) activity among 22 hemiplegic stroke patients. To take off trend and noise, we use the empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) of the angular velocity due to its superior decomposing ability in nonlinear oscillations. Shannon entropy based on angular velocity (phase)-envelope of EMG (amplitude) distribution was calculated to demonstrate characteristics of the coupling between SEMG activity and joint movement. We also compare our results with those from traditional methods such as the normalized relaxation index derived from the Pendulum test and the mean root mean square (RMS) of the SEMG signals in the study. Our results show effective discrimination ability between spastic and nonaffected limbs using our method . This study indicates the feasibility of using the novel indices based on the PAC in evaluation the spasticity among the hemiplegic stroke patients with less than three swinging cycles.
Background The pendulum test is a standard clinical test for quantifying the severity of spasticity. In the test, an electrogoniometer is typically used to measure the knee angular motion. The device is costly and difficult to set up such that the pendulum test is normally time consuming. Objective The goal of this study is to determine whether a Nintendo Wii remote can replace the electrogroniometer for reliable assessment of the angular motion of the knee in the pendulum test. Methods The pendulum test was performed in three control participants and 13 hemiplegic stroke patients using both a Wii remote and an electrogoniometer. The correlation coefficient and the Bland–Altman difference plot were used to compare the results obtained from the two devices. The Wilcoxon signed-rank test was used to compare the difference between hemiplegia-affected and nonaffected sides in the hemiplegic stroke patients. Results There was a fair to strong correlation between measurements from the Wii remote and the electrogoniometer (0.513 < R2 < 0.800). Small but consistent differences between the Wii remote and electrogoniometer were identified from the Bland–Altman difference plot. Within the hemiplegic stroke patients, both devices successfully distinguished the hemiplegia-affected (spastic) side from the nonaffected (nonspastic) side (both with p < .0001*). In addition, the intraclass correlation coefficient, standard error of measurement, and minimum detectable differences were highly consistent for both devices. Conclusion Our findings suggest that the Wii remote may serve as a convenient and cost-efficient tool for the assessment of spasticity.
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