The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F1 index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it’s suitable for real-time warning of wearable ECG monitoring equipment.
目的 建立測定胎盤灌流液中格列苯脲濃度的高效液相色譜（HPLC）檢測方法。方法 采用的色譜柱為Symmetry Shield RP C18（150 mm×4.6 mm，5 μm），柱溫40℃，流動相為NaH2PO4緩沖鹽（25 mmol/L，pH值5.2）︰乙腈=1︰1；內標為格列齊特，流速1.0 mL/min，檢測波長228 nm，采用液-液萃取預處理方法測定胎盤灌流液中格列苯脲的濃度。 結果 格列苯脲濃度線性范圍為2.0～25.0 μg/mL，線性方程為：y=0.226x+0.002，r=0.999 9 （n=6），日內相對標準偏差（RSD）＜3.1%，日間RSD＜9.5%，方法學回收率為95.32%～103.35%。 結論 HPLC檢測方法靈敏、簡便，可用于胎盤灌流液中格列苯脲濃度的檢測。
目的 建立柱前衍生化反相高效液相色譜-熒光檢測法測定血漿中同型半胱氨酸（Hcy）濃度的方法。 方法 以tris-(2-carboxylethyl)-phosphine hydrochloride （TCEP）為還原劑，以7-fluorbenzo-2-oxa-1,3-diazole-4-sulfonate （SBD-F）為衍生劑，色譜柱為Xterra RP18柱，柱溫35℃，流動相為甲醇︰磷酸二氫鈉緩沖液（pH值3.0）=3︰97，激發波長380 nm，發射波長510 nm，外標法定量。 結果 Hcy濃度在1.95～125 ?mol/L范圍內線性關系良好（r=0.999 8）。日內和日間相對標準偏差均＜7%，方法回收率為103.2%～111.9%。 結論 此方法準確、靈敏、快速，是一種適合于實驗室研究和臨床檢測血漿中Hcy濃度的方法。
Cochlear implant (CI) is the only method for efficacious treatment of congenital severe deafness at present. However, for children with congenital severe deafness after CI, the mechanism of the structural and functional changes of their cerebral cortex is not clear. This study was based on the cross-modal reorganization of deaf patients. Event related potential (ERP) and source localization technique were used to visualize the change of cortical activity in children with congenital severe deafness during 1-year period (0, 1, 3, 6, 9 and 12 months after CI). We aimed to investigate the association between hearing restoration and cross-modal reorganization in children with congenital severe deafness after CI. The results showed that the cross-modal reorganization exists in children with congenital severe deafness. During hearing restoration, the function of the cross-modal reorganization reversed to the normal state. The method and conclusions of this study may be of significance in guiding the training and evaluation of hearing rehabilitation after CI in patients.
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.
ObjectivesTo systematically review the efficacy and safety of escitalopram in the prevention of post-stroke depression (PSD).MethodsPubMed, The Cochrane Library, CBM, WanFang Data, VIP and CNKI databases were electronically searched to collect randomized controlled trials (RCTs) on escitalopram in preventing PSD from inception to March 2019. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 6 RCTs involving 891 patients were included. The results of meta-analysis showed that: compared with the control group, the escitalopram group could reduce the incidence of PSD (RR=0.55, 95%CI 0.31 to 0.98, P=0.04). In addition, there was no statistical difference between escitalopram group and control group in rate of adverse events (P≥0.05).ConclusionsCurrent evidence shows that escitalopram can reduce the incidence of PSD without increasing the incidence of adverse reactions. Due to limited quality and quantity of the included studies, more high quality studies are required to verify above conclusions.