The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.
Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constructed a one-dimensional convolutional neural network (CNN) model, which consists in four convolution layers, four pooling layers, two full connection layers and one classification layer. The automatic feature extraction and classification were realized by the structure of the proposed CNN model. The model was verified by the whole night single-channel sleep electrocardiogram (ECG) signals of 70 subjects from the Apnea-ECG dataset. Our results showed that the accuracy of per-segment SA detection was ranged from 80.1% to 88.0%, using the input signals of single-channel ECG signal, RR interval (RRI) sequence, R peak sequence and RRI sequence + R peak sequence respectively. These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence. When the input signals were RRI sequence + R peak sequence, the CNN model achieved the best performance. The accuracy, sensitivity and specificity of per-segment SA detection were 88.0%, 85.1% and 89.9%, respectively. And the accuracy of per-recording SA diagnosis was 100%. These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years. The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.
目的 探讨成都地区中老年人群的腰高比值（WHtR）与血压、血脂、血糖的关系。 方法 2007年5月间在成都市地中老年人群（688人）中用统一编制的调查表记录被调查者的血压、血脂、血糖、WHtR等指标，并用相关统计学数据进行分析。 结果 ① 成都地区中老年人群WHtR≥0.5的高血压、高血脂、高血糖的发病率明显高于对照组（P＜0.05），且有统计学意义。② 年龄、收缩压、舒张压、甘油三酯和血糖水平，WHtR≥0.5组明显高于WHtR＜0.5组，而WHtR≥0.5组高密度脂蛋白水平低于WHtR＜0.5组，且组间差异均有统计学意义。③ logistic 回归分析表明WHtR与年龄、收缩压、舒张压、甘油三酯、空腹血糖水平呈正相关，与高密度脂蛋白水平呈负相关。 结论 成都地区中老年人群WHtR与血压、血脂及血糖关系密切，可能可以通过改善血压、血脂、空腹血糖等指标来减少WHtR，中老年人群要加强对 WHtR 的自我管理, 尽可能将心血管疾病危险因素降到最低。
This paper is aimed to analyze the topological properties of structural brain networks in depressive patients with and without anxiety and to explore the neuropath logical mechanisms of depression comorbid with anxiety. Diffusion tensor imaging and deterministic tractography were applied to map the white matter structural networks. We collected 20 depressive patients with anxiety (DPA), 18 depressive patients without anxiety (DP), and 28 normal controls (NC) as comparative groups. The global and nodal properties of the structural brain networks in the three groups were analyzed with graph theoretical methods.The result showed that ① the structural brain networks in three groups showed small-world properties and highly connected global hubs predominately from association cortices; ② DP group showed lower local efficiency and global efficiency compared to NC group, whereas DPA group showed higher local efficiency and global efficiency compared to NC group; ③ significant differences of network properties (clustering coefficient, characteristic path lengths, local efficiency, global efficiency) were found between DPA and DP groups; ④ DP group showed significant changes of nodal efficiency in the brain areas primarily in the temporal lobe and bilateral frontal gyrus, compared to DPA and NC groups. The analysis indicated that the DP and DPA groups showed nodal properties of the structural brain networks, compared to NC group. Moreover, the two diseased groups indicated an opposite trend in the network properties. The results of this study may provide a new imaging index for clinical diagnosis for depression comorbid with anxiety.
【摘要】 目的 探讨成都市成华区中老年人群血脂水平、分布特点及其与胰岛素抵抗指数（HOMA-IR）的关系。 方法 2007年5月在此区中老年（50~79岁）人群中随机抽取672人进行心血管危险因素研究调查，对其血脂水平及HOMA-IR进行统计分析。 结果 人群当中①女性各血脂项目的水平均比男性高，其中总胆固醇（TC）、高密度脂蛋白胆固醇（HDL-C）的差异有统计学意义（Plt;0.05）；②三酰甘油（TG）升高的比例较高，其中男性为30.0%，女性为27.6%；大部分人群HDL-C、低密度脂蛋白胆固醇（LDL-C）水平处于合适范围，HDL-C降低的比例为6.0%，LDL-C升高的比例为7.3%；③随着TG水平的升高、HDL-C 水平的降低，HOMA-IR呈升高趋势；LDL-C水平的升高，HOMA-IR呈升高趋势，仅在女性人群中差异有统计学意义（Plt;0.05），在男性人群中差异无统计学意义；④TG与HOMA-IR呈正相关，相关系数为0.185（P=0.000）；HDL-C与HOMA-IR呈负相关，相关系数为-0.145（P=0.000）。LDL-C与HOMA-IR呈正相关，相关系数为0.099（P=0.010）。 结论 TG增高是成都市成华区中老年人群的显著特点，女性HDL-C比男性高；血脂紊乱与胰岛素抵抗相关。【Abstract】 Objective To investigate the relationship between blood lipids level and homeostasis model assessment-insulin resistance (HOMA-IR) in elder people in Chengdu. Methods In May 2007, 672 people aged from 50 to 79 years in Chengdu were recruited by random sampling methods for the survey of cardiovascular risk factors. The blood lipids level and HOMA-IR were statistically analyzed. Results ① The serum total cholesterol (TC) and high density lipoprotein chole sterol (HDL-C) were obviously higher in women than those in men (Plt;0.05). ② Triacylglycerol (TG) increased in 30.0% of men and 27.6% of women; HDL-C and low density lipoprotein cholesterin (LDL-C) in most of the involved people were appropriate. ③ HOMA-IR increased as the TG level increased and HDL-C decreased; HOMA-IR increased as the LDL-C level increased, which was significant in the females (Plt;0.05). ④ HDL-C was positively correlated with HOMA-IR (r=-0.145, P=0.000); LDL-C was positively correlated with HOMA-IR (r=0.099, P=0.010). Conclusion The increase of hypertriglyceridemia was the most frequent type of the dislipidemia in the elder people in Chengdu; HDL-C level is higher in women than in men. Dyslipidemia is correlated with insulin resistance.
ObjectiveTo evaluate the occupational exposures and protective condition of medical staff in operating room for interventional therapy. MethodsFrom September to November in 2014, 46 medical staff in the operating room for interventional therapy were adopted to complete the occupational exposures and protection situation survey. After that, we analyzed and scored all the questionnaires. ResultsThe awareness of the medical staff about occupational exposures and occupational protection in the operating room was low; the awareness rates of doctors, nurses, and radiology technicians were 74.2%, 63.0% and 66.7%, respectively. The general condition of implement of occupational protection measures of the medical staff was bad, and the average rate of the doctors, nurses and radiology technicians was 51.3%, 43.9%, and 15.2%. ConclusionThe implement rates of occupational protection measures of the medical staff in Operating Room for interventional therapy differ much from the awareness rates. The rates in doctors and nurses are higher than that in radiology technicians. We should strengthen the education of radiation damage and protection measures in doctors, and enhance the training of iatrogenic injuries related knowledge in clinical radiology technicians to reduce occupational hazards.
【摘要】 目的 分析成都地区中老年居民脉压（pulse pressure， PP）及脉压指数（pulse presure index，PPI）与高尿酸血症（hyperuricemia，HUA）的关系。 方法 利用2007年5月代谢综合征研究调查资料（共1 061人），依据PP[≤60 mm Hg（1 mm Hg=0.133 kPa）、gt;60 mm Hg]和PPI（≤0.450、gt;0.450）将人群分为正常组及增高组，分析两组人群尿酸水平及HUA患病率，采用单因素回归及logistic回归分析PP及PPI与HUA关系。 结果 ①PP/PPI增高组血浆尿酸水平明显高于PP/PPI正常组，差异有统计学意义（P=0.000）。②PP/PPI增高组HUA患病率明显高于PP/PPI正常组，差异有统计学意义（P=0.026、0.027）。③单因素回归和logistic回归分析皆提示PP及PPI与HUA呈正相关。 结论 成都地区PP及PPI与血浆尿酸水平关系密切，PP/PPI增高可能是HUA的危险因素。【Abstract】 Objective To evaluate the relationship between pulse pressure (PP), pulse pressure index (PPI) and hyperuricemia (HUA) among middle-aged and aged residents in Chengdu. Methods Based on the level of PP [≤60 mm Hg (1 mm Hg=0.133 kPa),gt;60 mm Hg] and PPI (≤0.450,gt;0.450), We divided the 1 061 middle-aged or aged people into normal PP/PPI group and augmented PP/PPI group. All patients came from the survey for metabolic syndrome study in May 2007. We analyzed the distribution of serum uric acid (UA) and HUA, and analyzed the relationship between PP, PPI and HUA by using single-factor and logistic regression analysis. Results The index of UA in the augmented PP/PPI groups was higher than that in the normal groups with a significant difference (P=0.000). The prevalence of HUA in the augmented PP/PPI groups was statistically higher than that in the normal groups (P=0.026, 0.027). Single-factor and logistic regression analysis showed that PP and PPI were both positively correlated to HUA. Conclusion The abnormalities of PP and PPI are closely related to metabolism disorder in Chengdu, and high level of PP or PPI is probably risk factors for HUA.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.