目的：本研究旨在比較一種新的腦電參數-腦電非線性指數（ENI）與BIS在丙泊酚靶控輸注時預測鎮靜深度的能力。方法:選擇30例18～60歲，ASA Ⅰ～Ⅱ級，擬行擇期普外科手術患者。每一患者同時監測ENI和BIS。麻醉誘導給予丙泊酚靶控輸注，直至患者意識消失后給予芬太尼和羅庫溴銨行氣管插管。麻醉誘導過程中每30秒進行一次鎮靜評分（采用改良OAA/S評分），并記錄ENI和BIS值以及平均動脈壓（MAP）和心率（HR）。結果:ENI和BIS與鎮靜評分的相關性比MAP和HR高（r=0.90、0.93 vs r=0.77、0.27）。鎮靜過程（改良OAA/S評分）中ENI和BIS有很好的相關性(R2=0.828)。ENI和BIS預測鎮靜深度的能力優于MAP和HR。結論：ENI可提供與BIS相似的反映鎮靜深度的信息，能準確預測不同的鎮靜深度。
Finger tapping test is a common testing item for patients with Parkinson's disease (PD) in clinical neurology. It mainly evaluates the fine motor function of patient's hand in three aspects:amplitude, speed and regularity of the movement. This paper focused on the quantitative assessment of regularity of finger tapping movement for PD patients. The movement signals of thumb and index finger were recorded by using inertial sensor unit in the process of tapping test. Two nonlinear dynamic indexes, approximate entropy (ApEn) and sample entropy (SampEn), were calculated, and then the values were statistically analyzed. The experimental results indicated that both indexes had significant differences between patient group and control group. Moreover, the indexes had relatively high correlation with the scores of corresponding unified Parkinson's disease rating scale (UPDRS) item rated by clinical clinician, which illustrated that these two indexes could reflect the injury level of the repetitive finger movement. So, as a reliable method, it can be provided to the clinical evaluation of hand movement function for PD patients.
The result of the emotional state induced by music may provide theoretical support and help for assisted music therapy. The key to assessing the state of emotion is feature extraction of the emotional electroencephalogram (EEG). In this paper, we study the performance optimization of the feature extraction algorithm. A public multimodal database for emotion analysis using physiological signals (DEAP) proposed by Koelstra et al. was applied. Eight kinds of positive and negative emotions were extracted from the dataset, representing the data of fourteen channels from the different regions of brain. Based on wavelet transform, δ, θ, α and β rhythms were extracted. This paper analyzed and compared the performances of three kinds of EEG features for emotion classification, namely wavelet features (wavelet coefficients energy and wavelet entropy), approximate entropy and Hurst exponent. On this basis, an EEG feature fusion algorithm based on principal component analysis (PCA) was proposed. The principal component with a cumulative contribution rate more than 85% was retained, and the parameters which greatly varied in characteristic root were selected. The support vector machine was used to assess the state of emotion. The results showed that the average accuracy rates of emotional classification with wavelet features, approximate entropy and Hurst exponent were respectively 73.15%, 50.00% and 45.54%. By combining these three methods, the features fused with PCA possessed an accuracy of about 85%. The obtained classification accuracy by using the proposed fusion algorithm based on PCA was improved at least 12% than that by using single feature, providing assistance for emotional EEG feature extraction and music therapy.
The purpose of this study is to compare the differences among neck muscle fatigue evaluation algorithms and to find a more effective algorithm which can provide a human factor quantitative evaluation method for neck muscle fatigue during bending over the desk. We collected surface electromyography signal of sternocleidomastoid muscle of 15 subjects using wireless physiotherapy Bio-Radio when they bent over the desk using memory pillows for 12 minutes. Five algorithms including mean power frequency, spectral moments ratio, discrete wavelet transform, fuzzy approximation entropy and the complexity algorithms were used to calculate the corresponding muscle fatigue index. The least squares method was used to calculate the corresponding coefficient of determination R2 and slope k of the linear regression of the muscle fatigue metric. The coefficient of determination R2 evaluates anti-interference ability of algorithms. The maximum vertical distance Lmax which is obtained by the Kolmogorov-Smirnov test for the slopes k evaluates the ability to distinguish fatigue of algorithms. The results indicate that in the aspect of anti-interference ability, the fuzzy approximation entropy has the largest R2 when using memory pillows with different heights. When the fuzzy approximate entropy is compared with average power frequency or the discrete wavelet transform, the differences are significant (P < 0.05). In terms of distinguishing the degree of fatigue, the approximate entropy is still the largest, with a maximum of 0.496 7. Fuzzy approximation entropy is superior to other algorithms in ability of anti-interference and distinguishing fatigue. Therefore, fuzzy approximation entropy can be used as a better evaluation algorithm in the evaluation of cervical muscle fatigue.