Amine response smartphone-based portable and intelligent polyvinyl alcohol films for real-time detection of shrimp freshness.

Food freshness monitoring is an important component in ensuring food safety for consumers and the food industry. Therefore, there is an urgent need for a portable, low-cost, and efficient detection method to determine the freshness. In this study, polyvinyl alcohol (PVA) was used as polymer carrier to prepare electrospinning film containing curcumin (Cur) and gardenia blue (GB) as intelligent indicator label on food packaging for real-time nondestructive detection of freshness of shrimp. The detection limit of ammonia response is less than or equal to 20 ppm, and the detection time is about 1 min, indicating that it has a sensitive response effect. At the same time, a smartphone application that can identify amines in response to color changes has been developed, and consumers can understand freshness by scanning the label. This study demonstrates the huge potential of smart indicator labels for food freshness monitoring.

Self-administration of complex decongestive therapy facilitated by the mobile application WeChat improves lymphedema and quality of life in breast cancer survivors: an observational study.

Background: Lymphedema is the most common complication of breast cancer patients. Complex decongestive therapy (CDT) is often recommended but the efficacy varies due to the complexity of management. This study investigated a novel model of CDT based on a mobile application with the aim of improving the management of lymphedema in China. Methods: We developed a novel model of CDT for breast cancer survivors with lymphedema, including 5 days of CDT therapy with training provided by medical staff in the outpatient clinic and 3 weeks of self-administrated CDT with daily online instructions during phase I, and a life-long maintenance treatment with online instructions once a week for phase II, which delivered by WeChat public accounts. The breast cancer and lymphedema symptom experience index (BCLE-SEI) and the Short-Form Health Survey (SF-36) were used to assess lymphatic symptoms and quality of life. Arm volume and lymphatic symptoms were assessed at baseline, and at 5 days, 1 month, and 3 months post-treatment. The quality of life was assessed at baseline and at 3 months post-treatment. Results: A total of 88 patients with lymphedema were recruited, of whom, 61 followed the protocols and were further analyzed for this study. The mean relative excess arm volume (EAV) was reduced from a baseline value of 30.72% to 22.05%, 18.46%, and 16.67% at 5 days, 1 month, and 3 months post-therapy, respectively (P=0.000). The BCLE-SEI scores of lymphatic pain, heaviness, and impaired limb mobility were all significantly improved after 3 months of treatment (P<0.05). Moreover, according to the subscale of SF-36, the general health and vitality were significantly improved after 3 months of therapy (56.64 vs. 62.93, P=0.008; and 64.26 vs. 70.08, P=0.024, respectively). Conclusions: The proposed model of CDT based on the mobile application WeChat achieved promising outcomes. The volume of the affected arm, the lymphedema symptoms, and the quality of life were all significantly improved.

Multi-Scale Permutation Entropy: A Potential Measure for the Impact of Sleep Medication on Brain Dynamics of Patients with Insomnia.

Insomnia is a common sleep disorder that is closely associated with the occurrence and deterioration of cardiovascular disease, depression and other diseases. The evaluation of pharmacological treatments for insomnia brings significant clinical implications. In this study, a total of 20 patients with mild insomnia and 75 healthy subjects as controls (HC) were included to explore alterations of electroencephalogram (EEG) complexity associated with insomnia and its pharmacological treatment by using multi-scale permutation entropy (MPE). All participants were recorded for two nights of polysomnography (PSG). The patients with mild insomnia received a placebo on the first night (Placebo) and temazepam on the second night (Temazepam), while the HCs had no sleep-related medication intake for either night. EEG recordings from each night were extracted and analyzed using MPE. The results showed that MPE decreased significantly from pre-lights-off to the period during sleep transition and then to the period after sleep onset, and also during the deepening of sleep stage in the HC group. Furthermore, results from the insomnia subjects showed that MPE values were significantly lower for the Temazepam night compared to MPE values for the Placebo night. Moreover, MPE values for the Temazepam night showed no correlation with age or gender. Our results indicated that EEG complexity, measured by MPE, may be utilized as an alternative approach to measure the impact of sleep medication on brain dynamics.

[Study on the prediction of cardiovascular disease based on sleep heart rate variability analysis].

The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn't during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.

Deep Learning in Automatic Sleep Staging With a Single Channel Electroencephalography.

This study centers on automatic sleep staging with a single channel electroencephalography (EEG), with some significant findings for sleep staging. In this study, we proposed a deep learning-based network by integrating attention mechanism and bidirectional long short-term memory neural network (AT-BiLSTM) to classify wakefulness, rapid eye movement (REM) sleep and non-REM (NREM) sleep stages N1, N2 and N3. The AT-BiLSTM network outperformed five other networks and achieved an accuracy of 83.78%, a Cohen's kappa coefficient of 0.766 and a macro F1-score of 82.14% on the PhysioNet Sleep-EDF Expanded dataset, and an accuracy of 81.72%, a Cohen's kappa coefficient of 0.751 and a macro F1-score of 80.74% on the DREAMS Subjects dataset. The proposed AT-BiLSTM network even achieved a higher accuracy than the existing methods based on traditional feature extraction. Moreover, better performance was obtained by the AT-BiLSTM network with the frontal EEG derivations than with EEG channels located at the central, occipital or parietal lobe. As EEG signal can be easily acquired using dry electrodes on the forehead, our findings might provide a promising solution for automatic sleep scoring without feature extraction and may prove very useful for the screening of sleep disorders.

Tai Chi training's effect on lower extremity muscle co-contraction during single- and dual-task gait: Cross-sectional and randomized trial studies.

BACKGROUND: Tai Chi (TC) mind-body exercise has been shown to reduce falls and improve balance and gait, however, few studies have evaluated the role of lower extremity muscle activation patterns in the observed benefits of TC on mobility. PURPOSE: To perform an exploratory analysis of the association between TC training and levels of lower extremity muscle co-contraction in healthy adults during walking under single-task (ST) and cognitive dual-task (DT) conditions. METHODS: Surface electromyography of the anterior tibialis and lateral gastrocnemius muscles was recorded during 90 sec trials of overground ST (walking normally) and DT (walking with verbalized serial subtractions) walking. A mean co-contraction index (CCI), across all strides, was calculated based on the percentage of total muscle activity when antagonist muscles were simultaneously activated. A hybrid study design investigated long-term effects of TC via a cross-sectional comparison of 27 TC experts and 60 age-matched TC-naïve older adults. A longitudinal comparison assessed the shorter-term effects of TC; TC-naïve participants were randomly allocated to either 6 months of TC training or to usual care. RESULTS: Across all participants at baseline, greater CCI was correlated with slower gait speed under DT (ß(95% CI) = -26.1(-48.6, -3.7)) but not ST (ß(95% CI) = -15.4(-38.2, 7.4)) walking. Linear models adjusting for age, gender, BMI and other factors that differed at baseline indicated that TC experts exhibited lower CCI compared to TC naives under DT, but not ST conditions (ST: mean difference (95% CI) = -7.1(-15.2, 0.97); DT: mean difference (95% CI) = -10.1(-18.1, -2.4)). No differences were observed in CCI for TC-naive adults randomly assigned to 6 months of TC vs. usual care. CONCLUSION: Lower extremity muscle co-contraction may play a role in the observed benefit of longer-term TC training on gait and postural control. Longer-duration and adequately powered randomized trials are needed to evaluate the effect of TC on neuromuscular coordination and its impact on postural control. TRIAL REGISTRATION: The randomized trial component of this study was registered at ClinicalTrials.gov (NCT01340365).

Changes in EEG permutation entropy in the evening and in the transition from wake to sleep.

Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power-Ptheta: 4-8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25-101 years. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. An MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events.

Surges in sympathetic activity should be a major contributor to the frequent occurrence of cardiovascular events towards the end of nocturnal sleep. We aimed to investigate whether the analysis of hypnopompic heart rate variability (HRV) could assist in the prediction of cardiovascular disease (CVD). 2217 baseline CVD-free subjects were identified and divided into CVD group and non-CVD group, according to the presence of CVD during a follow-up visit. HRV measures derived from time domain analysis, frequency domain analysis and nonlinear analysis were employed to characterize cardiac functioning. Machine learning models for both long-term and short-term CVD prediction were then constructed, based on hypnopompic HRV metrics and other typical CVD risk factors. CVD was associated with significant alterations in hypnopompic HRV. An accuracy of 81.4% was achieved in short-term prediction of CVD, demonstrating a 10.7% increase compared with long-term prediction. There was a decline of more than 6% in the predictive performance of short-term CVD outcomes without HRV metrics. The complexity of hypnopompic HRV, measured by entropy-based indices, contributed considerably to the prediction and achieved greater importance in the proposed models than conventional HRV measures. Our findings suggest that Hypnopompic HRV assists the prediction of CVD outcomes, especially the occurrence of CVD event within two years.

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer's disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.

Sleep heart rate variability assists the automatic prediction of long-term cardiovascular outcomes.

OBJECTIVE: We aimed to investigate the association between sleep HRV and long-term cardiovascular disease (CVD) outcomes, and further explore whether HRV features can assist the automatic CVD prediction. METHODS: We retrospectively analyzed polysomnography (PSG) data obtained from 2111 participants in the Sleep Heart Health Study, who were followed up for a median of 11.8 years after PSG acquisition. During follow-up, 1252 participants suffered CVD events (CVD group) and 859 participants remained CVD-free (non-CVD group). HRV measures, derived from time-domain and frequency-domain, were calculated. Regression models were created to determine the independent predictor for long-term CVD outcomes, and to explore the association between HRV and CVD latency. Furthermore, based on HRV and other clinical features, a model was trained to automatically predict CVD outcomes using the eXtreme Gradient Boosting algorithm. RESULTS: Compared with the non-CVD group, decreased HRV during sleep was found in the CVD group. HRV, particularly its component of high frequency (HF), was demonstrated to be independent predictor of CVD outcomes. Moreover, normalized HF was positively correlated with CVD latency. The proposed prediction model achieved a total accuracy of 75.3%, in which sleep HRV features served as a supplement to the well-recognized CVD risk factors, such as aging, adiposity and sleep disorders. CONCLUSIONS: Association between sleep HRV and long-term CVD outcomes was demonstrated here, suggesting that altered HRV during sleep might occur many years prior to the onset of CVD. Machine learning models, combining sleep HRV and other clinical characteristics, should be promising in the early prediction of CVD outcomes.

Abnormal topology of brain functional networks in unipolar depression and bipolar disorder using optimal graph thresholding.

Two popular debilitating illness, unipolar depression (UD) and bipolar disorder (BD), have the similar symptoms and tight association on the psychopathological level, leading to a clinical challenge to distinguish them. In order to figure out the underlying common and different mechanism of both mood disorders, resting-state functional magnetic resonance imaging (rs-fMRI) data derived from 36 UD patients, 42 BD patients (specially type I, BD-I) and 45 healthy controls (HC) were analyzed retrospectively in this study. Functional brain networks were firstly constructed on both group and individual levels with a density 0.2, which was determined by a network thresholding approach based on modular similarity. Then we investigated the alterations of modular structure and other topological properties of the functional brain network, including global network characteristics and nodal network measures. The results demonstrated that the functional brain networks of UD and BD-I groups preserved the modularity and small-worldness property. However, compared with HC, reduced number of modules was observed in both patients' groups with shared alterations occurring in hippocampus, para hippocampal gyrus, amygdala and superior parietal gyrus and distinct changes of modular composition mainly in the caudate regions of basal ganglia. Additionally, for the network characteristics, compared to HC, significantly decreased global efficiency and small-worldness were observed in BD-I. For the nodal metrics, significant decrease of local efficiency was found in several regions in both UD and BD-I, while a UD-specified increase of participant coefficient was found in the right paracentral lobule and the right thalamus. These findings may contribute to throw light on the neuropathological mechanisms underlying the two disorders and further help to explore objective biomarkers for the correct diagnosis of UD and BD.

Quantifying interdependence using the missing joint ordinal patterns.

In this paper, we develop the concept of forbidden/missing ordinal patterns into the forbidden/missing joint ordinal patterns and propose the ratio of the number of missing joint ordinal patterns (RMJPs) as a sign of interdependence. RMJP in a surrogate analysis can be used to differentiate the forbidden joint ordinal patterns from the missing joint ordinal patterns due to small sample effects. We first apply RMJP to the simulated time series: a two-component autoregressive fractionally integrated moving average process, the Hénon map, and the Rössler system using active control and discuss the effect of the length of the time series, embedding dimension, and noise contamination. RMJP has been proven to be capable of measuring the interdependence in the numerical simulation. Then, RMJP is further used on the electroencephalogram (EEG) time series for empirical analysis to explore the interdependence of brain waves. With results by RMJP obtained from a widely used open dataset of the sleep EEG time series from healthy subjects, we find that RMJP can be used to quantify the brain wave interdependence under different sleep/wake stages, reveal the overall sleep architecture, and indicate a higher level of interdependence as sleep gets deeper. The findings are consistent with existing knowledge in sleep medicine. The proposed RMJP method has shown its validity and applicability and may assist automatic sleep quantification or bring insight into the understanding of the brain activity during sleep. Furthermore, RMJP can be used on sleep EEG under various pathological conditions and in large-scale sleep studies, helping to investigate the mechanisms of the sleep process and neuron synchronization.

Visibility graph analysis of temporal irreversibility in sleep electroencephalograms.

The study of sleep has continued to garner increased attention. However, most studies assume stationarity of sleep electroencephalogram (EEG) signals, whereas they are typically nonlinear and nonstationary. Little work has focused on the time irreversibility of sleep EEG signals. Hence, the aim of this work is to reveal the temporally irreversible structures of rapid-eye-movement (REM) and non-REM sleep using a visibility algorithm, which is robust to nonstationarity and finite-size effect. Results show that the temporal structure of non-REM sleep is more irreversible than that of REM sleep. The degree of irreversibility is highest in slow-wave sleep. Moreover, statistical analysis suggests that aging is the major factor that affects the irreversibility of sleep signals, while gender and body mass index contribute insignificantly. The dominant role of slow oscillations on the irreversible structures of the sleep signals is also indicated.

Age-Related Alterations in Electroencephalography Connectivity and Network Topology During n-Back Working Memory Task.

The study of the healthy brain in elders, especially age-associated alterations in cognition, is important to understand the deficits created by Alzheimer's disease (AD), which imposes a tremendous burden on individuals, families, and society. Although, the changes in synaptic connectivity and reorganization of brain networks that accompany aging are gradually becoming understood, little is known about how normal aging affects brain inter-regional synchronization and functional networks when items are held in working memory (WM). According to the classic Sternberg WM paradigm, we recorded multichannel electroencephalography (EEG) from healthy adults (young and senior) in three different conditions, i.e., the resting state, 0-back (control) task, and 2-back task. The phase lag index (PLI) between EEG channels was computed and then weighted and undirected network was constructed based on the PLI matrix. The effects of aging on network topology were examined using a brain connectivity toolbox. The results showed that age-related alteration was more prominent when the 2-back task was engaged, especially in the theta band. For the younger adults, the WM task evoked a significant increase in the clustering coefficient of the beta-band functional connectivity network, which was absent in the older adults. Furthermore, significant correlations were observed between the behavioral performance of WM and EEG metrics in the theta and gamma bands, suggesting the potential use of those measures as biomarkers for the evaluation of cognitive training, for instance. Taken together, our findings shed further light on the underlying mechanism of WM in physiological aging and suggest that different EEG frequencies appear to have distinct functional correlates in cognitive aging. Analysis of inter-regional synchronization and topological characteristics based on graph theory is thus an appropriate way to explore natural age-related changes in the human brain.

Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset.

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1-30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.

[The analysis of event-related potentials in normal brain aging based on N-back cognitive tasks].

The purpose of this study is to investigate the change of the whole brain event-related potentials(P300) in normal brain aging based on N-back cognitive tasks. The P300 of 15 normal young people and 10 normal old people were evaluated based on N-back cognitive tasks and analyzed. The results showed that the P300 latency of old people was longer in whole brain than young people, and amplitude was increased in the frontal-central region, while significantly increased in the pre-frontal region in the same load cognitive tasks. With the cognitive task load increasing, the amplitude of old people in high-load task was higher in the whole brain than that in low-load task, mainly in in the frontal region, but the difference was not statistically significant. The latency in the high-load task was shorter in the frontal-central region of right brain than the low-load task, and the difference was statistically significant. Thus, P300 showed that the normal brain aging process is mainly reflected in the pre-frontal region, and the high-load cognitive task could better reflect the change of brain function compared with the low-load cognitive task. The finding is of revelatory meaning for diagnosis of early dementia in patients.

[Investigation into Feasibility of Congestive Heart Failure Diagnosis Based on Analysis of Very Short-term Heart Rate Variability].

The analysis parameters for the characterization of heart rate variability(HRV)within a very short time(< 1min)usually exhibit complicate variation patterns over time,which may easily interfere the judgment to the status of the cardiovascular system.In this study,long-term HRV sequence of 41 cases of healthy people(control group)and 25 cases of congestive heart failure(CHF)patients(experimental group)was divided into multiple segments of very short time series.The variation coefficient of the same HRV parameter under multiple segments of very short time series and the testing proportion with statistically significant differences under multiple interclass t-test were calculated.On this account,part of HRV analysis parameters under very short time were discussed to reveal the stability of difference of the cardiovascular system function under different status.Furthermore,with analyzing the receiver operating characteristic(ROC)curve and modeling the artificial neural network(ANN),the classification effects of these parameters between the control group and the experimental group were assessed.The results demonstrated that1 the indices of entropy of degree distribution based on the complex network analysis had a lowest variation coefficient and was sensitive to the pathological status(in 79.75% cases,there has statistically significant differences between the control group and experimental group),which can be served as an auxiliary index for clinical doctor to diagnose for CHF patient;2after conducting ellipse fitting to Poincare plot,in 98.5% cases,there had statistically significant differences for the ratio of ellipse short-long axis(SDratio)between the control group and the experimental group;when modeling the ANN and solely adopting SDratio,the classification accuracy to the control group and experimental group was 71.87%,which demonstrated that SDratio might be acted as the intelligent diagnosis index for CHF patients;3 however,more sensitive and robust indices were still needed to find out for the very-short HRV analysis and for the diagnosis of CHF patients as well.

[Epilepsy Electroencephalogram Signal Analysis Based on Improved k-nearest Neighbor Network].

The study of complex networks has become a hot research area of electroencephalogram signal.Electroencephalogram time series generated by the network keeps node information of network,so studying the time series from the network can also achieve the purpose of study epileptic electroencephalogram.In this paper,we propose a method to analyze epileptic electroencephalogram based on time series which is based on improved k-nearest neighbor network.The results of the experiment showed that studying power spectrum of time series from network was easier than power spectrum of time series directly generated from the original brain data to distinguish between normal controls and epileptic patients.In addition,studying the clustering coefficient of improved k-nearest neighbor network was able to distinguish between normal persons and patients with epilepsy.This study can provide important reference for the study of epilepsy and clinical diagnosis.

Combination of equiprobable symbolization and time reversal asymmetry for heartbeat interval series analysis.

Symbolic dynamics method and time reversal asymmetry analysis are both important approaches in the study of heartbeat interval series. However, there is limited research work reported on combining these two methods. We provide a method of time reversal asymmetry analysis which focuses on the differences between the forward and backward embedding "m words" after the operation of equiprobable symbolization. To investigate the total amplitude as well as the distribution features of the difference, four indices are proposed. Based on the application to simulation series, we found that these measures can successfully detect time reversal asymmetry in chaos series. With application to human heartbeat interval series (RR series), it is suggested that the distribution features of the forward-backward difference can sensitively capture the dynamical changes caused by diseases or aging. In particular, the index E(D), which reflects the random degree of the forward-backward difference distribution, can significantly discriminate healthy subjects from diseased ones. We conclude that RR series from healthy subjects show more asymmetry in temporal structure on the original time scale from the perspective of equiprobable symbolization, whereas diseases account for loss of this asymmetry.