Sub-threshold depression refers to a psychological sub-health state that fails to meet the diagnostic criteria for depression. Appropriate intervention can improve the state and reduce the risks of disease development. In this paper, we focus on music neurofeedback stimulation improving emotional state of sub-threshold depression college students.Twenty-four college students with sub-threshold depression participated in the experiment, 16 of whom were members of the experimental group. Decompression music based on spectrum classification was applied to 16 experimental group participants for 10 min/d music neural feedback stimulation with a period of 14 days, and no stimulation was applied to 8 control group participants. Three feature parameters of electroencephalogram (EEG) relative power, sample entropy and complexity were extracted for analysis. The results showed that the relative power of α、β and θ rhythm increased, while δ rhythm decreased after the stimulation of musical nerofeedback in the experimental group. The sample entropy and complexity were significantly increased after the stimulation, and the differences of these parameters pre and post stimulation were statistically significant (P < 0.05), while the differences of all feature parameters in the control group were not statistically significant. In the experimental group, the scores of self-rating depression scale(SDS) decreased after the stimulation of musical nerofeedback, indicating that the depression was improved. The result of this study showed that music neurofeedback stimulation can improve sub-threshold depression and may provides an effective new way for college students to self-regulation of emotion.
Objective To evaluate a score system to allow stratification of complexity in degenerative mitral valve repair. Methods We retrospectively reviewed the clinical data of 312 consecutive patients who underwent surgery for mitral valve repair and whose preoperative echocardiography was referable in our hospital from January 2012 to December 2013. A scoring system for surgical complexity was used based mainly on the preoperative echocardiography findings. Complexity of mitral valve repair was scored as 1 to 9, and patients were categorized into 3 groups based on the score for surgical complexity: a simple group (1 point), an intermediate group (2-4 points) and a complex group (≥5 points). There were 86 males and 35 females in the simple group (n=121) with an average age of 51.6±12.6 years, 105 males and 53 females in the intermediate group (n=158) with an average age of 51.1±12.8 years and 25 males and 8 females in the complex group (n=33) with an average age of 49.3±13.0 years. Results There was significant difference in surgical complexity in different groups. In the simple, intermediate and complex groups, the mean cardiopulmonary bypass time was 111.7±45.5 min, 117.7±40.4 min and 153.4±74.2 min (P<0.001), the mean cross-clamping time was 77.5±33.8 min, 83.2±29.9 min and 108.8±56.2 min (P<0.001), and the mean number of repair techniques utilized was 2.1±0.4, 2.4±0.6 and 2.8±0.8 (P<0.001). However, there was no significant difference in the early and late outcomes in different groups. Conclusion It is feasible to use echocardiography to quantitatively evaluate the difficulty of mitral valvuloplasty.
All the collected original electroencephalograph (EEG) signals were the subjects to low-frequency and spike noise. According to this fact, we in this study performed denoising based on the combination of wavelet transform and independent component analysis (ICA). Then we used three characteristic parameters, complexity, approximate entropy and wavelet entropy values, to calculate the preprocessed EEG data. We then made a distinguishing judge on the EEG state by the state change rate of the characteristic parameters. Through the anesthesia and non-anesthesia EEG data processing results showed that each of the three state change rates could reach about 50.5%, 21.6%, 19.5%, respectively, in which the performance of wavelet entropy was the highest. All of them could be used as a foundation in the quantified research of depth of anesthesia based on EEG analysis.
When people are walking, they will produce gait signals and different people will produce different gait signals. The research of the gait signal complexity is really of great significance for medicine. By calculating people's gait signal complexity, we can assess a person's health status and thus timely detect and diagnose diseases. In this study, the Jensen-Shannon divergence (JSD), the method of complexity analysis, was used to calculate the complexity of gait signal in the healthy elderly, healthy young people and patients with Parkinson's disease. Then we detected the experimental data by variance detection. The results showed that the difference among the complexity of the three gait signals was great. Through this research, we have got gait signal complexity range of patients with Parkinson's disease, the healthy elderly and healthy young people, respectively, which would provide an important basis for clinical diagnosis.
This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis—dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.
The study on complexity of glucose fluctuation not only helps us understand the regulation of the glucose homeostasis system but also brings us a new insight of the research methodology on glucose regulation. In the experiments, we analyzed the complexity of the temporal structure of the 72 hours continuous glucose time series from a group of 93 subjects with type Ⅱ diabetes mellitus using the multi-scale entropy method. We adapted the most recently improved refined composite multi-scale entropy (RCMSE) algorithm which could overcome the shortcomings on the 72 hours short time series analysis. We then quantified and compared the complexity of continuous glucose time series between groups with type Ⅱ diabetes mellitus with different mean absolute glycemic excursion (MAGE) and glycated hemoglobin (HbA1c). The results implied that the complexity of glucose time series decreased on lower MAGE group compared to high MAGE group, and the entropy on scale 1 to 6 which corresponded to 5 to 30 min had significant differences between these two groups; the complexity of glucose time series decreased with the increasing HbA1c level but the entropy had no statistical difference among groups at different scales. Therefore, RCMSE provided us with a new prospect to analyze the glucose time series and it was proved that less complexity of glucose dynamics could indicate the impaired gluco-regulation function from the MAGE point of view or HbA1c for patients, and the glucose complexity had the potential to become a new biomarker to reflect the fluctuation of the glucose time series.
To distinguish the randomness and chaos characteristics of physiological signals and to keep its performance independent of the signal length and parameters are the key judgement of performance of a complexity algorithm. We proposed an encoding Lempel-Ziv (LZ) complexity algorithm to try to explicitly discern between the randomness and chaos characteristics of signals. Our study also compared the effects of length of time series, the sensitivity to dynamical properties change of time series and quantifying the complexity between gauss noise and 1/f pink noise ELZ with those from classic LZ (CLZ), multi-state LZ (MLZ), sample entropy (SampEn) and permutation entropy (PE). The experimental results showed ELZ could not only distinguish the randomness and chaos characteristics of time series on all time length (i.e. 100, 500, 5 000), but also reflected exactly that the complexity of gauss noise was lower than that of pink noise, and responded change of dynamic characteristics of time series in time. The congestive heart failure (CHF) RR Interval database and the normal sinus rhythm (NSR) RR Interval database created by Massachusetts Institute of Technology (MIT) and Boston Beth Israel Hospital(BIH)were used as real data in our study. The results revealed that the ELZ could show the complexity of congestive heart failure which was lower than that of normal sinus rhythm during all lengths of time series (P<0.01), and the ELZ algorithm had better generalization ability and was independent of length of time series.
In the present study carried out in our laboratory, we recorded local field potential (LFP) signals in primary visual cortex (V1 area) of rats during the anesthesia process in the electrophysiological experiments of invasive microelectrode array implant, and obtained time evolutions of complexity measure Lempel-ziv complexity (LZC) by nonlinear dynamic analysis method. Combined with judgment criterion of tail flick latency to thermal stimulus and heart rate, the visual stimulation experiments are carried out to verify the reliability of anesthetized states by complexity analysis. The experimental results demonstrated that the time varying complexity measures LZC of LFP signals of different channels were similar to each other in the anesthesia process. In the same anesthesia state, the difference of complexity measure LZC between neuronal responses before and after visual stimulation was not significant. However, the complexity LZC in different anesthesia depths had statistical significances. Furthermore, complexity threshold value represented the depth of anesthesia was determined using optimization method. The reliability and accuracy of monitoring the depth of anesthesia using complexity measure LZC of LFP were all high. It provided an effective method of realtime monitoring depth of anesthesia for craniotomy patients in clinical operation.
Patients with type 2 diabetes mellitus often face significant treatment burden, which substantially impacts their quality of life and health outcomes. Reducing treatment burden represents a critical component for improving patient prognosis and enhancing treatment adherence. Based on the cumulative complexity model, this article systematically examines the conceptual connotation and multidimensional characteristics of treatment burden in type 2 diabetes mellitus patients, explores the theoretical extension and application value of cumulative complexity model in the type 2 diabetes mellitus field, elucidates its specific applications and recent advances in treatment burden research, evaluates the limitations of existing assessment tools while proposing a multidimensional assessment framework, and ultimately develops cumulative complexity model based intervention strategies. The findings provide theoretical references for optimizing patient-centered diabetes management approaches and offer novel perspectives for treatment burden intervention.
The linear analysis for heart rate variability (HRV), including time domain method, frequency domain method and timefrequency analysis, has reached a lot of consensus. The nonlinear analysis has also been widely applied in biomedical and clinical researches. However, for nonlinear HRV analysis, especially for shortterm nonlinear HRV analysis, controversy still exists, and a unified standard and conclusion has not been formed. This paper reviews and discusses three shortterm nonlinear HRV analysis methods (fractal dimension, entropy and complexity) and their principles, progresses and problems in clinical application in detail, in order to provide a reference for accurate application in clinical medicine.