Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.
【摘要】 目的 观察70岁以上老年心肌梗死急性期血清胱抑素C（cystatin C，CysC）水平，探讨急性心肌梗死后CysC水平变化的意义。 方法 顺序入选2010年7月-2011年7月期间70岁以上急性心肌梗死患者58例及正常对照58例。入选对象均经冠状动脉造影检查确诊或排除诊断，记录急性心肌梗死患者梗死部位和梗死相关血管，并计算Gensini积分。所有入选对象采血，使用乳胶增强免疫透射比浊法测定急性期血清CysC水平。 结果 心肌梗死急性期，血清CysC水平低于正常对照组（Plt;0.05）；不同冠状动脉病变评分与血清CysC水平呈负相关，Gensini积分越高，血清CysC水平越低。 结论 血清CysC与冠心病关系密切。检测CysC，为冠心病的风险预测、老年患者危险分层和治疗提供一条新的线索和途径。
Objective To evaluate the efficacy of normalization management on prognosis in elderly patients with coronary artery disease, in aspects of drug compliance, readmission rate and quality of life. Methods A total of 110 patients above 65 years old with coronary artery disease visiting West China Hospital from August 2010 to February 2011 were investigated. The patients were divided into two groups: the intensive management group (n=55) and the general management group (n=55). The measures such as regular follow-up, regular examination and medical education were conducted in the intensive management group, and the two groups were observed in aspects of drug compliance, readmission rate and quality of life. Results After 1-year follow-up, the percentages of patients taking aspirin/clopidogrel (98.18% vs. 67.27%, Plt;0.05), nitrate (85.45% vs. 40.00%, Plt;0.05), ACEI/ARB (56.36% vs. 18.18%, Plt;0.05), β receptor blocker (58.18% vs. 29.09%, Plt;0.05) and statin (94.55% vs. 32.73%, Plt;0.05) were higher in the intensive management group than those in the general management group. Also, the readmission rate was lower (12.73% vs. 41.42%, Plt;0.05) and the score of quality of life was higher in the intensive management group than that in the general management group. Conclusion The normalization management guided by evidence-based medicine for the elderly patients with coronary artery disease is helpful to improve the drug compliance, reduce the readmission rate, and improve the quality of life.
Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.
Tumor-treating fields (TTFields) is a novel treatment modality for malignant solid tumors, often employing electric field simulations to analyze the distribution of electric fields on the tumor under different parameters of TTFields. Due to the present difficulties and high costs associated with reproducing or implementing the simulation model construction techniques, this study used readily available open-source software tools to construct a highly accurate, easily implementable finite element simulation model for TTFields. The accuracy of the model is at a level of 1 mm3. Using this simulation model, the study carried out analyses of different factors, such as tissue electrical parameters and electrode configurations. The results show that factors influncing the distribution of the internal electric field of the tumor include changes in scalp and skull conductivity (with a maximum variation of 21.0% in the treatment field of the tumor), changes in tumor conductivity (with a maximum variation of 157.8% in the treatment field of the tumor), and different electrode positions and combinations (with a maximum variation of 74.2% in the treatment field of the tumor). In summary, the results of this study validate the feasibility and effectiveness of the proposed modeling method, which can provide an important reference for future simulation analyses of TTFields and clinical applications.