The validity of ASMI, RASM, and L3 SMI in assessing skeletal muscle mass in patients with lung cancer.

BACKGROUND AND OBJECTIVES: Both bioelectrical impedance analysis (BIA) and electron computed tomography (CT) can be used as tools for assessing skeletal muscle mass. In order to find a more suitable method for assessing skeletal muscle mass in lung cancer patients, this study conducted a comprehensive comparative analysis of the two methods. METHODS AND STUDY DESIGN: We collected baseline data from patients admitted to the oncology department of the First Hospital of Hebei Medical University from October 2017 to December 2021, and collected data through physical examination, body composition analysis measurements and CT examinations. Then we calculated skeletal muscle mass index (ASMI), relative skeletal muscle index (RASM), and third lumbar spine skeletal muscle index (L3 SMI), respectively. Finally we analyzed the correlation between the three methods and body composition and biochemical indicators and the validity of the three methods. RESULTS: A total of 63 patients, 41 males and 22 females, were screened and eligible for enrollment, and the validity of RASM and ASMI was analyzed using L3 SMI as the diagnostic criteria: the sensitivity of RASM and ASMI were 66.67% and 13.33%, respectively, and the specificity was 70.83% and 39.58%, respectively, and the AUC of ROC was 0.736 (p<0.05), 0.264 (p<0.05). CONCLUSIONS: In this study, L3 SMI was used as the diagnostic criterion and after calculating and comparing the valid parameters of RASM and ASMI, RASM was recommended as the assessment criterion for skeletal muscle mass in Chinese lung cancer patients.

[Research on conditional fluctuation characteristics of CHF heart rate variation].

In this study, we applied generalized autoregressive conditional heteroskedasticity (GARCH) model to conditional fluctuation characteristics of heart rate variation (HRV) series (congestive heart failure, Normal), with all the data from PhysioNet ECG database. Research results proved the existence of condition fluctuation characteristic in the series of changing rate of HRV. In the GARCH model family, threshold GARCH (1,1)(TGARCH (1,1)) model performs best in fitting changing rate of HRV. Although the structure of ARCH (1) model is simple, its error is the closest to that of TGARCH (1, 1) model. The results also showed that the difference was obvious between disease group and normal group. All these results provide a new method to the research and clinical application of HRV.

[A wireless ECG monitor based on ARM].

This paper presents a novel monitor which uses ARM controller AT91SAM7S64 as its main processor, LCM (Liquid Crystal Display Module) for displaying ECG waves, SD (Secure Digital memory) card for data storage and RF module PTR8000 for radio data transmission. This portable monitor boasts alarm function for abnormality and can provide dynamic ECG monitoring for patients.

Transitions in physiological coupling between heartbeat and pulse across sleep stages.

OBJECTIVE: The aim of this study is to investigate the coupling behavior between heartbeat and pulse of blood flow at different sleep stages, and to explore the feasibility of using this coupling strength for automatic sleep staging. APPROACH: The electrocardiogram and photoplethysmography signals are recorded during sleep, and R-wave-to-R-wave intervals (RRI) and pulse-to-pulse intervals (PPI) are extracted respectively. The detrended cross-correlation analysis (DCCA) is applied to quantify long-range cross correlations between the RRIs and PPIs across sleep stages. The DCCA scaling exponents are used as the indicator of coupling strength between heartbeat and pulse, and are compared with detrended fluctuation analysis (DFA) scaling exponents of RRIs and PPIs in the application of sleep stage discrimination. MAIN RESULTS: We find the DCCA scaling exponents between RRIs and PPIs decrease monotonously from wake, REM sleep to light and deep sleep, indicating that the coupling strength between heartbeat and pulse are reduced gradually when entering deep sleep. Statistical analysis shows that the DCCA scaling exponents possess better discrimination ability between wake/REM sleep and light/deep sleep, compared with DFA scaling exponents of RRIs and PPIs. SIGNIFICANCE: Our study reveals the coupling strength between heartbeat and pulse changes regularly across sleep stages, which may help understand the regulation mechanism underlying the cardiovascular system. The DCCA scaling exponents between RRIs and PPIs can be used as an indicator for measuring vigilance level and automatic sleep staging.

[Multiscale entropy analysis of electrocardiogram].

Using the algorithm proposed by Costa M, et al., we studied the multiscale entropy (MSE) of electrocardiogram. The sample entropy (SampEn) of the healthy subjects was found to be higher than that of the subjects with coronary heart disease or myocardial infarction. The healthy subjects' complexity was found to be the highest. The SampEn of the subjects with coronary heart disease was noted to be only slightly higher than that of the subjects with myocardial infarction. These findings show that the complexity of the subjects with coronary heart disease or myocardial infarction is distinctly lower than the complexity of the healthy ones, and the subjects suffereing from coronary heart disease are liable to the onset of myocardial infarction.

[A design of a portable acquistion system for autonomic nervous function data].

In this paper, a design of the portable acquisition system for autonomic nervous function data based on the microcontroller is introduced. The system contains an electrocardiogram amplifier and an AD convertor, using SD memory card as its storage device and thus it can record data for a longer time and exchange data with PC easily. The system with a simple structure realizes its miniaturization and low energy consumption.

Model of the Dynamic Construction Process of Texts and Scaling Laws of Words Organization in Language Systems.

Scaling laws characterize diverse complex systems in a broad range of fields, including physics, biology, finance, and social science. The human language is another example of a complex system of words organization. Studies on written texts have shown that scaling laws characterize the occurrence frequency of words, words rank, and the growth of distinct words with increasing text length. However, these studies have mainly concentrated on the western linguistic systems, and the laws that govern the lexical organization, structure and dynamics of the Chinese language remain not well understood. Here we study a database of Chinese and English language books. We report that three distinct scaling laws characterize words organization in the Chinese language. We find that these scaling laws have different exponents and crossover behaviors compared to English texts, indicating different words organization and dynamics of words in the process of text growth. We propose a stochastic feedback model of words organization and text growth, which successfully accounts for the empirically observed scaling laws with their corresponding scaling exponents and characteristic crossover regimes. Further, by varying key model parameters, we reproduce differences in the organization and scaling laws of words between the Chinese and English language. We also identify functional relationships between model parameters and the empirically observed scaling exponents, thus providing new insights into the words organization and growth dynamics in the Chinese and English language.

Multiscale multifractality analysis of a 12-lead electrocardiogram.

This paper proposes that a multiscale multifractality (MSMF) method be adopted for the spatiotemporal analysis of 12-lead ECG. By using this method, the authors find that, in some frequency range, 12-lead ECG has a more complex fractal structure, and the position of the largest singularity strength range delta alpha is not relying on the data length but on the scale factor. By determining the inflexion, the MSMF proves to be more sensitive in displaying the trend that the singularity strength range delta alpha of human ECG decreases with human aging.

A multi-scale feedback ratio analysis of heartbeat interval series in healthy vs. cardiac patients.

The second-order difference plot, as a modified Poincaré plot, is one of the important approaches for assessing the dynamics of heart rate variability. However, corresponding quantitative analysis methods are relatively limited. Based on the second-order difference plot, we propose a novel method, called the multi-scale feedback ratio analysis, which can measure the feedback properties of heart rate fluctuations on different temporal scales. The index [R(TF([τ(1), τ(2)]) is then defined to quantify the average feedback ratio through a definite scale range. Analysis of Gaussian white, 1/f and Brownian noises show that the feedback ratios are indeed on different levels. The method is then applied to heartbeat interval series derived from healthy subjects, subjects with congestive heart failure and subjects with atrial fibrillation. Results show that, for all groups, the feedback ratios vary with increasing time scales, and gradually reach relatively stable states. The R(TF)([10,20]) values of the three groups are significantly different. Thus, R(TF)([10,20]) becomes an effective parameter for distinguishing healthy and pathologic states. In addition, RTF([10,20]) for healthy, congestive failure and atrial fibrillation subjects are close to those of the 1/f, Brownian and white noises respectively, indicating different intrinsic dynamics.

Modified permutation-entropy analysis of heartbeat dynamics.

Heart rate variability (HRV) contains important information about the modulation of the cardiovascular system. Various methods of nonlinear dynamics (e.g., estimating Lyapunov exponents) and complexity measures (e.g., correlation dimension or entropies) have been applied to HRV analysis. Permutation entropy, which was proposed recently, has been widely used in many fields due to its conceptual and computational simplicity. It maps a time series onto a symbolic sequence of permutation ranks. The original permutation entropy assumes the time series under study has a continuous distribution, thus equal values are rare and can be ignored by ranking them according to their order of emergence, or broken by adding small random perturbations to ensure every symbol in a sequence is different. However, when the observed time series is digitized with lower resolution leading to a greater number of equal values, or the equalities represent certain characteristic sequential patterns of the system, it may not be rational to simply ignore or break them. In the present paper, a modified permutation entropy is proposed that, by mapping the equal value onto the same symbol (rank), allows for a more accurate characterization of system states. The application of the modified permutation entropy to the analysis of HRV is investigated using clinically collected data. Results show that modified permutation entropy can greatly improve the ability to distinguish the HRV signals under different physiological and pathological conditions. It can characterize the complexity of HRV more effectively than the original permutation entropy.