Analysis of biomedical signals by the lempel-Ziv complexity: the effect of finite data size

The psychosis-like effects of Δ(9)-tetrahydrocannabinol are associated with increased cortical noise in healthy humans

BACKGROUND

Drugs that induce psychosis may do so by increasing the level of task-irrelevant random neural activity or neural noise. Increased levels of neural noise have been demonstrated in psychotic disorders. We tested the hypothesis that neural noise could also be involved in the psychotomimetic effects of delta-9-tetrahydrocannabinol (Δ(9)-THC), the principal active constituent of cannabis.

METHODS

Neural noise was indexed by measuring the level of randomness in the electroencephalogram during the prestimulus baseline period of an oddball task using Lempel-Ziv complexity, a nonlinear measure of signal randomness. The acute, dose-related effects of Δ(9)-THC on Lempel-Ziv complexity and signal power were studied in humans (n = 24) who completed 3 test days during which they received intravenous Δ(9)-THC (placebo, .015 and .03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design.

RESULTS

Δ(9)-THC increased neural noise in a dose-related manner. Furthermore, there was a strong positive relationship between neural noise and the psychosis-like positive and disorganization symptoms induced by Δ(9)-THC, which was independent of total signal power. Instead, there was no relationship between noise and negative-like symptoms. In addition, Δ(9)-THC reduced total signal power during both active drug conditions compared with placebo, but no relationship was detected between signal power and psychosis-like symptoms.

CONCLUSIONS

At doses that produced psychosis-like effects, Δ(9)-THC increased neural noise in humans in a dose-dependent manner. Furthermore, increases in neural noise were related with increases in Δ(9)-THC-induced psychosis-like symptoms but not negative-like symptoms. These findings suggest that increases in neural noise may contribute to the psychotomimetic effects of Δ(9)-THC.

Cognitive tasks during walking affect cerebral blood flow signal features in middle cerebral arteries and their correlation to gait characteristics

Gait is a complex process involving both cognitive and sensory ability and is strongly impacted by the environment. In this paper, we propose to study of the impact of a cognitive task during gait on the cerebral blood flow velocity, the blood flow signal features and the correlation of gait and blood flow features through a dual task methodology. Both cerebral blood flow velocity and gait characteristics of eleven participants with no history of brain or gait conditions were recorded using transcranial Doppler on mid-cerebral artery while on a treadmill. The cognitive task was induced by a backward counting starting from 10,000 with decrement of 7. Central blood flow velocity raw and envelope features were extracted in both time, frequency and time-scale domain; information-theoretic metrics were also extracted and statistical significances were inspected. A similar feature extraction was performed on the stride interval signal. Statistical differences between the cognitive and baseline trials, between the left and right mid-cerebral arteries signals and the impact of the antropometric variables where studied using linear mixed models. No statistical differences were found between the left and right mid-cerebral arteries flows or the baseline and cognitive state gait features, while statistical differences for specific features were measured between cognitive and baseline states. These statistical differences found between the baseline and cognitive states show that cognitive process has an impact on the cerebral activity during walking. The state was found to have an impact on the correlation between the gait and blood flow features.

A cerebral blood flow evaluation during cognitive tasks following a cervical spinal cord injury: a case study using transcranial Doppler recordings

A spinal cord injury (SCI) is one of the most common neurological disorders. In this paper, we examined the consequences of upper SCI in a male participant on the cerebral blood flow velocity. In particular, transcranial Doppler was used to study these effects through middle cerebral arteries (MCA) during resting-state periods and during cognitive challenges (non-verbal word-generation tasks and geometric-rotation tasks). Signal characteristics were analyzed from raw signals and envelope signals (maximum velocity) in the time domain, the frequency domain and the time-frequency domain. The frequency features highlighted an increase of the peak frequency in L-MCA and R-MCA raw signals, which revealed stronger cerebral blood flow during geometric/verbal processes respectively. This underlined a slight dominance of the right hemisphere during word-generation periods and a slight dominance of the left hemisphere during geometric processes. This finding was confirmed by cross-correlation in the time domain and by the entropy rate in information-theoretic domain. A comparison of our results to other neurological disorders (Alzheimer's disease, Parkinson's disease, autism, epilepsy, traumatic brain injury) showed that the SCI had similar effects such as general decreased cerebral blood flow and similar regular hemispheric dominance in a few cases.

AD, Costa FSM, Bianchi AM. Are Complexity Metrics Reliable in Assessing HRV Control in Obese Patients During Sleep?

Obesity is associated with cardiovascular mortality. Linear methods, including time domain and frequency domain analysis, are normally applied on the heart rate variability (HRV) signal to investigate autonomic cardiovascular control, whose imbalance might promote cardiovascular disease in these patients. However, given the cardiac activity non-linearities, non-linear methods might provide better insight. HRV complexity was hereby analyzed during wakefulness and different sleep stages in healthy and obese subjects. Given the short duration of each sleep stage, complexity measures, normally extracted from long-period signals, needed be calculated on short-term signals. Sample entropy, Lempel-Ziv complexity and detrended fluctuation analysis were evaluated and results showed no significant differences among the values calculated over ten-minute signals and longer durations, confirming the reliability of such analysis when performed on short-term signals. Complexity parameters were extracted from ten-minute signal portions selected during wakefulness and different sleep stages on HRV signals obtained from eighteen obese patients and twenty controls. The obese group presented significantly reduced complexity during light and deep sleep, suggesting a deficiency in the control mechanisms integration during these sleep stages. To our knowledge, this study reports for the first time on how the HRV complexity changes in obesity during wakefulness and sleep. Further investigation is needed to quantify altered HRV impact on cardiovascular mortality in obesity.

Characterizing psychological dimensions in non-pathological subjects through autonomic nervous system dynamics

The objective assessment of psychological traits of healthy subjects and psychiatric patients has been growing interest in clinical and bioengineering research fields during the last decade. Several experimental evidences strongly suggest that a link between Autonomic Nervous System (ANS) dynamics and specific dimensions such as anxiety, social phobia, stress, and emotional regulation might exist. Nevertheless, an extensive investigation on a wide range of psycho-cognitive scales and ANS non-invasive markers gathered from standard and non-linear analysis still needs to be addressed. In this study, we analyzed the discerning and correlation capabilities of a comprehensive set of ANS features and psycho-cognitive scales in 29 non-pathological subjects monitored during resting conditions. In particular, the state of the art of standard and non-linear analysis was performed on Heart Rate Variability, InterBreath Interval series, and InterBeat Respiration series, which were considered as monovariate and multivariate measurements. Experimental results show that each ANS feature is linked to specific psychological traits. Moreover, non-linear analysis outperforms the psychological assessment with respect to standard analysis. Considering that the current clinical practice relies only on subjective scores from interviews and questionnaires, this study provides objective tools for the assessment of psychological dimensions.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

The rotenone-induced rat model of Parkinson's disease: behavioral and electrophysiological findings

Exposure to rotenone leads to parkinsonian features, such as loss of dopaminergic neurons in the substantia nigra and motor impairment, however, the validity of this model has recently been questioned. In rodent and monkey models of Parkinson's disease (PD) abnormal neuronal activity in the basal ganglia motor loop has been described, with hyperactivity of the subthalamic nucleus (STN) similar to that found in PD. The present study aims at providing new and more specific evidence for the validity of the rotenone rat model of PD by examining whether neuronal activity in the STN is altered. Male Sprague Dawley rats were treated with rotenone injections (2.5mg/kg bodyweight intraperitoneally) for 60 days. Behavioral analysis showed an impairment in the rotarod and hanging wire test in the rotenone group (p<0.05), accompanied by a decline in tyrosine hydroxylase immunoreactive neurons in the nigro-striatal region (p<0.001). Thereafter, single unit (SU) activities and local field potentials were recorded in the STN in urethane anesthetized rats. The SU analysis revealed a higher neuronal discharge rate (p<0.001), more bursts per minute (p=0.006) and a higher oscillatory activity (p=0.008) in the STN of rotenone treated rats. Spectral analysis showed an increase of relative beta power in the STN as well as in the motor cortex. We found electrophysiological key features of PD pathology and pathophysiology in the STN of rotenone treated rats. Therefore, the rotenone-induced rat model of PD deserves further attention since it covers more aspects than dopamine depletion and implies the reproducibility of PD specific features.

Nonlinear analysis of saccade speed fluctuations during combined action and perception tasks

BACKGROUND

Saccades are rapid eye movements used to gather information about a scene which requires both action and perception. These are usually studied separately, so that how perception influences action is not well understood. In a dual task, where the subject looks at a target and reports a decision, subtle changes in the saccades might be caused by action-perception interactions. Studying saccades might provide insight into how brain pathways for action and for perception interact.

NEW METHOD

We applied two complementary methods, multifractal detrended fluctuation analysis and Lempel-Ziv complexity index to eye peak speed recorded in two experiments, a pure action task and a combined action-perception task.

RESULTS

Multifractality strength is significantly different in the two experiments, showing smaller values for dual decision task saccades compared to simple-task saccades. The normalized Lempel-Ziv complexity index behaves similarly i.e. is significantly smaller in the decision saccade task than in the simple task.

COMPARISON WITH EXISTING METHODS

Compared to the usual statistical and linear approaches, these analyses emphasize the character of the dynamics involved in the fluctuations and offer a sensitive tool for quantitative evaluation of the multifractal features and of the complexity measure in the saccades peak speeds when different brain circuits are involved.

CONCLUSION

Our results prove that the peak speed fluctuations have multifractal characteristics with lower magnitude for the multifractality strength and for the complexity index when two neural pathways are simultaneously activated, demonstrating the nonlinear interaction in the brain pathways for action and perception.

A new algorithm for quadratic sample entropy optimization for very short biomedical signals: application to blood pressure records

This paper describes a new method to optimize the computation of the quadratic sample entropy (QSE) metric. The objective is to enhance its segmentation capability between pathological and healthy subjects for short and unevenly sampled biomedical records, like those obtained using ambulatory blood pressure monitoring (ABPM). In ABPM, blood pressure is measured every 20-30 min during 24h while patients undergo normal daily activities. ABPM is indicated for a number of applications such as white-coat, suspected, borderline, or masked hypertension. Hypertension is a very important clinical issue that can lead to serious health implications, and therefore its identification and characterization is of paramount importance. Nonlinear processing of signals by means of entropy calculation algorithms has been used in many medical applications to distinguish among signal classes. However, most of these methods do not perform well if the records are not long enough and/or not uniformly sampled. That is the case for ABPM records. These signals are extremely short and scattered with outliers or missing/resampled data. This is why ABPM Blood pressure signal screening using nonlinear methods is a quite unexplored field. We propose an additional stage for the computation of QSE independently of its parameter r and the input signal length. This enabled us to apply a segmentation process to ABPM records successfully. The experimental dataset consisted of 61 blood pressure data records of control and pathological subjects with only 52 samples per time series. The entropy estimation values obtained led to the segmentation of the two groups, while other standard nonlinear methods failed.

Assessing randomness and complexity in human motion trajectories through analysis of symbolic sequences

Complexity is a hallmark of intelligent behavior consisting both of regular patterns and random variation. To quantitatively assess the complexity and randomness of human motion, we designed a motor task in which we translated subjects' motion trajectories into strings of symbol sequences. In the first part of the experiment participants were asked to perform self-paced movements to create repetitive patterns, copy pre-specified letter sequences, and generate random movements. To investigate whether the degree of randomness can be manipulated, in the second part of the experiment participants were asked to perform unpredictable movements in the context of a pursuit game, where they received feedback from an online Bayesian predictor guessing their next move. We analyzed symbol sequences representing subjects' motion trajectories with five common complexity measures: predictability, compressibility, approximate entropy, Lempel-Ziv complexity, as well as effective measure complexity. We found that subjects' self-created patterns were the most complex, followed by drawing movements of letters and self-paced random motion. We also found that participants could change the randomness of their behavior depending on context and feedback. Our results suggest that humans can adjust both complexity and regularity in different movement types and contexts and that this can be assessed with information-theoretic measures of the symbolic sequences generated from movement trajectories.

Leveraging social networks for toxicovigilance

The landscape of drug abuse is shifting. Traditional means of characterizing these changes, such as national surveys or voluntary reporting by frontline clinicians, can miss changes in usage the emergence of novel drugs. Delays in detecting novel drug usage patterns make it difficult to evaluate public policy aimed at altering drug abuse. Increasingly, newer methods to inform frontline providers to recognize symptoms associated with novel drugs or methods of administration are needed. The growth of social networks may address this need. The objective of this manuscript is to introduce tools for using data from social networks to characterize drug abuse. We outline a structured approach to analyze social media in order to capture emerging trends in drug abuse by applying powerful methods from artificial intelligence, computational linguistics, graph theory, and agent-based modeling. First, we describe how to obtain data from social networks such as Twitter using publicly available automated programmatic interfaces. Then, we discuss how to use artificial intelligence techniques to extract content useful for purposes of toxicovigilance. This filtered content can be employed to generate real-time maps of drug usage across geographical regions. Beyond describing the real-time epidemiology of drug abuse, techniques from computational linguistics can uncover ways that drug discussions differ from other online conversations. Next, graph theory can elucidate the structure of networks discussing drug abuse, helping us learn what online interactions promote drug abuse and whether these interactions differ among drugs. Finally, agent-based modeling relates online interactions to psychological archetypes, providing a link between epidemiology and behavior. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These tools provide a powerful complement to existing methods of toxicovigilance.

A complexity measure based method for studying the dependance of 222Rn concentration time series on indoor air temperature and humidity

We have suggested a complexity measure based method for studying the dependence of measured (222)Rn concentration time series on indoor air temperature and humidity. This method is based on the Kolmogorov complexity (KL). We have introduced (i) the sequence of the KL, (ii) the Kolmogorov complexity highest value in the sequence (KLM) and (iii) the KL of the product of time series. The noticed loss of the KLM complexity of (222)Rn concentration time series can be attributed to the indoor air humidity that keeps the radon daughters in air.

Assessment of resting-state blood flow through anterior cerebral arteries using trans-cranial doppler recordings

Trans-cranial Doppler (TCD) recordings are used to monitor cerebral blood flow in the main cerebral arteries. The resting state is usually characterized by the mean velocity or the maximum Doppler shift frequency (an envelope signal) by insonating the middle cerebral arteries. In this study, we characterized cerebral blood flow in the anterior cerebral arteries. We analyzed both envelope signals and raw signals obtained from bilateral insonation. We recruited 20 healthy patients and conducted the data acquisition for 15 min. Features were extracted from the time domain, the frequency domain and the time-frequency domain. The results indicate that a gender-based statistical difference exists in the frequency and time-frequency domains. However, no handedness effect was found. In the time domain, information-theoretic features indicated that mutual dependence is higher in raw signals than in envelope signals. Finally, we concluded that insonation of the anterior cerebral arteries serves as a complement to middle cerebral artery studies. Additionally, investigation of the raw signals provided us with additional information that is not otherwise available from envelope signals. Use of direct trans-cranial Doppler raw data is therefore validated as a valuable method for characterizing the resting state.

A New Approach to Detect Epileptic Seizures in Electroencephalograms Using Teager Energy

A Teager energy (TE) based approach to discriminate electroencephalogram signals corresponding to nonseizure (eyes open, eyes closed, or interictal) and seizure (ictal) intervals is proposed. Though a good number of contributions have been made for seizure detection, the challenges of unbalanced data (nonseizure and seizure events) and system computational efficiency still remain a challenge. It is reported in the literature that the seizures are characterized by abnormal sudden discharges in the brain which get manifested in the EEG recordings by frequency changes and increased amplitudes. Teager energy (TE) is capable of tracking such rapid changes in frequency as well as amplitude in the time domain. An important finding of this study is that the mean TE quantifier is largely independent of the window length and exhibits relative consistency when used as a relative measure for comparison. We compared the diagnostic capability of TE quantifier with those of Higuchi’s fractal dimension and sample entropy in discriminating nonseizure and seizure states in the EEGs and found that TE outperforms the other two nonlinear quantifiers. The result shows that the application of this method compares favorably with conventional classification methods in terms of performance and is well suited for real-time automatic epileptic seizure detection.

On the non-randomness of maximum Lempel Ziv complexity sequences of finite size

Random sequences attain the highest entropy rate. The estimation of entropy rate for an ergodic source can be done using the Lempel Ziv complexity measure yet, the exact entropy rate value is only reached in the infinite limit. We prove that typical random sequences of finite length fall short of the maximum Lempel-Ziv complexity, contrary to common belief. We discuss that, for a finite length, maximum Lempel-Ziv sequences can be built from a well defined generating algorithm, which makes them of low Kolmogorov-Chaitin complexity, quite the opposite to randomness. It will be discussed that Lempel-Ziv measure is, in this sense, less general than Kolmogorov-Chaitin complexity, as it can be fooled by an intelligent enough agent. The latter will be shown to be the case for the binary expansion of certain irrational numbers. Maximum Lempel-Ziv sequences induce a normalization that gives good estimates of entropy rate for several sources, while keeping bounded values for all sequence length, making it an alternative to other normalization schemes in use.

Research and application of heart sound alignment and descriptor

The research and application of heart sound (HS) analysis for cardiovascular disease (CVD) diagnosis has attracted more attention recently. Unlike other relevant HS analysis research, such as HS detection/component segmentation, HS feature extraction/classification etc., the proposed research treats HS as a whole and focuses mainly on comparing the similarity of acoustical characteristics reflecting pathological condition between two HSs, one of which is HS under test and another is the HS with known CVD. The concrete procedure refers to alignment of the HS into sequence and evaluating the similarity index through complexity and similarity analysis. In accordance with specific characteristics of HS, several relevant technologies such as musical instrument digital interface (MIDI), binary coding, N-gram, Lempel-Ziv (L-Z) complexity as well as super-symmetric comparison distance (SCD) similarity metric etc. are researched to be adapted and cascaded to realize the aforementioned target successfully. The contribution lies in that the aligning schemes including binary and N-gram are thoroughly investigated and then testing results witnessing the superiority of using N-gram in proposed approach are presented. The success of such a novel approach would not only assign a the new life to the traditional auscultation CVD diagnosis, but also simplify CVD diagnosis greatly leading to extensive application of such an efficient non-invasive physical diagnostic method in e-home healthcare.

Computer-aided feedback of surgical knot tying using optical tracking

BACKGROUND

Quantifying the information content of hand motion during surgical knot tying using information theory based entropy measures enables the comparison of different groups: novice and expert. We hypothesized that complexity would differ between the 2 groups and predicted based on motor learning models that complexity/information would reduce with increased expertise.

METHODS

Six degrees of freedom hand-motion data during surgical knot tying were acquired using an infrared optical hand tracking device. Multiple data samples were obtained from 2 groups: novice (third-year medical students) and expert (attending surgeons). After preprocessing each knot tying data sample into a binary symbolic time series, 3 nonlinear complexity measures were calculated: Lempel Ziv complexity, Shannon entropy, and Renyi entropy. The Shannon and Renyi entropies were calculated using a word length of 6. A Student t test was used to test whether the 2 groups were from the same population when using these entropy measures, applying a p value of 0.05 to reject the null hypothesis.

RESULTS

The expert surgeons were found to have less complex patterns of motion compared with the novice group. This finding was statistically significant using Lempel Ziv complexity (p = 0.004), Shannon entropy (p = 0.006), and Renyi entropy with q = 2 (p = 0.006). Using Renyi entropy with q = 0.5, the 2 groups were not significantly different (p = 0.26).

CONCLUSIONS

The ability to separate novice from expert populations during surgical knot tying using information theory entropy measures could form the basis of a low-cost educational tool to provide feedback and to assess skill acquisition using low-fidelity bench models.

A tunable support vector machine assembly classifier for epileptic seizure detection

Automating the detection of epileptic seizures could reduce the significant human resources necessary for the care of patients suffering from intractable epilepsy and offer improved solutions for closed-loop therapeutic devices such as implantable electrical stimulation systems. While numerous detection algorithms have been published, an effective detector in the clinical setting remains elusive. There are significant challenges facing seizure detection algorithms. The epilepsy EEG morphology can vary widely among the patient population. EEG recordings from the same patient can change over time. EEG recordings can be contaminated with artifacts that often resemble epileptic seizure activity. In order for an epileptic seizure detector to be successful, it must be able to adapt to these different challenges. In this study, a novel detector is proposed based on a support vector machine assembly classifier (SVMA). The SVMA consists of a group of SVMs each trained with a different set of weights between the seizure and non-seizure data and the user can selectively control the output of the SVMA classifier. The algorithm can improve the detection performance compared to traditional methods by providing an effective tuning strategy for specific patients. The proposed algorithm also demonstrates a clear advantage over threshold tuning. When compared with the detection performances reported by other studies using the publicly available epilepsy dataset hosted by the University of BONN, the proposed SVMA detector achieved the best total accuracy of 98.72%. These results demonstrate the efficacy of the proposed SVMA detector and its potential in the clinical setting.

A rule-based seizure prediction method for focal neocortical epilepsy

OBJECTIVE

In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy.

METHODS

Five univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-s segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316 h of iEEG data containing 49 seizures recorded in 11 patients with medically intractable focal neocortical epilepsy.

RESULTS

For seizure occurrence periods of 30 and 50 min our method showed an average sensitivity of 79.9% and 90.2% with an average false prediction rate of 0.17 and 0.11/h, respectively. In terms of sensitivity and false prediction rate, the system showed superiority to random and periodical predictors.

CONCLUSIONS

The nonlinear analysis of iEEG in the period prior to seizures revealed patient-specific spatio-temporal changes that were significantly different from those observed within baselines in the majority of the seizures analyzed in this study.

SIGNIFICANCE

The present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset.

Characterization of early partial seizure onset: frequency, complexity and entropy

OBJECTIVE

A clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed.

METHODS

Eighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel-Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures).

RESULTS

GAD, Lempel-Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset.

CONCLUSIONS

Increases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset.

SIGNIFICANCE

Differences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

Blood pressure and heart rate variability complexity analysis in pregnant women with hypertension

BACKGROUND

In this work, we perform a comparative analysis of blood pressure and heart rate variability complexity during pregnancy between normal, hypertensive, and preeclamptic women.

METHODS AND RESULTS

A total of 563 short electrocardiographic (10 min) records were obtained from 217 pregnant women (135 normal, 55 hypertensive, and 27 preeclamptic) during several gestational ages in sitting position. We used a mixed unbalanced model for the longitudinal statistical analysis and besides the conventional spectral analysis, we applied Lempel-Ziv complexity, sample entropy, approximated entropy, and detrended fluctuation analysis in the complexity measurement.

CONCLUSIONS

The obtained results revealed significant differences between pathological and normal states with important considerations related to pregnancy adaptability and evolution as well as the relationship of complexity and blood pressure with factors such as maternal age, familial history of diabetes or hypertension, and parity.

Complexity of VTA DA neural activities in response to PFC transection in nicotine treated rats

Background

The dopaminergic (DA) neurons in the ventral tegmental area (VTA) are widely implicated in the addiction and natural reward circuitry of the brain. These neurons project to several areas of the brain, including prefrontal cortex (PFC), nucleus accubens (NAc) and amygdala. The functional coupling between PFC and VTA has been demonstrated, but little is known about how PFC mediates nicotinic modulation in VTA DA neurons. The objectives of this study were to investigate the effect of acute nicotine exposure on the VTA DA neuronal firing and to understand how the disruption of communication from PFC affects the firing patterns of VTA DA neurons.

Methods

Extracellular single-unit recordings were performed on Sprague-Dawley rats and nicotine was administered after stable recording was established as baseline. In order to test how input from PFC affects the VTA DA neuronal firing, bilateral transections were made immediate caudal to PFC to mechanically delete the interaction between VTA and PFC.

Results

The complexity of the recorded neural firing was subsequently assessed using a method based on the Lempel-Ziv estimator. The results were compared with those obtained when computing the entropy of neural firing. Exposure to nicotine triggered a significant increase in VTA DA neurons firing complexity when communication between PFC and VTA was present, while transection obliterated the effect of nicotine. Similar results were obtained when entropy values were estimated.

Conclusions

Our findings suggest that PFC plays a vital role in mediating VTA activity. We speculate that increased firing complexity with acute nicotine administration in PFC intact subjects is due to the close functional coupling between PFC and VTA. This hypothesis is supported by the fact that deletion of PFC results in minor alterations of VTA DA neural firing when nicotine is acutely administered.

Complexity measures of brain wave dynamics

To understand the nature of brain dynamics as well as to develop novel methods for the diagnosis of brain pathologies, recently, a number of complexity measures from information theory, chaos theory, and random fractal theory have been applied to analyze the EEG data. These measures are crucial in quantifying the key notions of neurodynamics, including determinism, stochasticity, causation, and correlations. Finding and understanding the relations among these complexity measures is thus an important issue. However, this is a difficult task, since the foundations of information theory, chaos theory, and random fractal theory are very different. To gain significant insights into this issue, we carry out a comprehensive comparison study of major complexity measures for EEG signals. We find that the variations of commonly used complexity measures with time are either similar or reciprocal. While many of these relations are difficult to explain intuitively, all of them can be readily understood by relating these measures to the values of a multiscale complexity measure, the scale-dependent Lyapunov exponent, at specific scales. We further discuss how better indicators for epileptic seizures can be constructed.

A Lempel-Ziv complexity measure for muscle fatigue estimation

This paper presents a Lempel-Ziv complexity measure for analysis of surface electromyography signals. The Lempel-Ziv measure provides a metric for the number of distinct deterministic patterns and the rate of their creation in signals. We propose a ternary Lempel-Ziv measure, improving upon the binary Lempel-Ziv measure, and making it more suited for the analysis of biological signals. The Lempel-Ziv measure is evaluated with a muscle fatigue experiment in which participants perform static, cyclic, and random contractions. Results show this complexity measure shows a greater correlation to a steadily increasing muscle fatigue level compared to the conventional median frequency. This measure is computationally easy to compute and does not require power spectrum estimation and signal stationarity assumptions.

Prefrontal cortex deletion affects the dopaminergic neural firing complexity in nicotine-treated ventral tegmental area

Nicotine, an addictive substance in cigarette, triggers glutamatergic synaptic plasticity on ventral tegmental area (VTA) dopamine (DA) neurons. The functional coupling between prefrontal cortex (PFC) and VTA has been demonstrated, but little is known how PFC mediates nicotinic modulation in VTA DA neurons. In this study, we tested the hypothesis that systemic exposure to nicotine significantly increases the VTA DA neuron's complexity of firing. The complexity of the neural firing of VTA DA neurons was significantly increased in PFC intact subjects, as determined using the advanced nonlinear dynamic method based on the Lempel-Ziv estimator. To further understand the functional coupling between PFC and VTA, we used LZ complexity method to estimate the complexity of firing of PFC transected subjects. Interestingly, without the input from PFC, the change in complexity estimated from VTA for PFC transected subjects is not significant. The results suggest PFC plays an important role in mediating VTA activity and that the LZ complexity method is a useful tool for the characterization of the dynamical changes in VTA DA neurons firing activities.

Modeling carbachol-induced hippocampal network synchronization using hidden Markov models

In this work we studied the neural state transitions undergone by the hippocampal neural network using a hidden Markov model (HMM) framework. We first employed a measure based on the Lempel-Ziv (LZ) estimator to characterize the changes in the hippocampal oscillation patterns in terms of their complexity. These oscillations correspond to different modes of hippocampal network synchronization induced by the cholinergic agonist carbachol in the CA1 region of mice hippocampus. HMMs are then used to model the dynamics of the LZ-derived complexity signals as first-order Markov chains. Consequently, the signals corresponding to our oscillation recordings can be segmented into a sequence of statistically discriminated hidden states. The segmentation is used for detecting transitions in neural synchronization modes in data recorded from wild-type and triple transgenic mice models (3xTG) of Alzheimer's disease (AD). Our data suggest that transition from low-frequency (delta range) continuous oscillation mode into high-frequency (theta range) oscillation, exhibiting repeated burst-type patterns, occurs always through a mode resembling a mixture of the two patterns, continuous with burst. The relatively random patterns of oscillation during this mode may reflect the fact that the neuronal network undergoes re-organization. Further insight into the time durations of these modes (retrieved via the HMM segmentation of the LZ-derived signals) reveals that the mixed mode lasts significantly longer (p < 10(-4)) in 3xTG AD mice. These findings, coupled with the documented cholinergic neurotransmission deficits in the 3xTG mice model, may be highly relevant for the case of AD.

Multiscale dynamic analysis of blast furnace system based on intensive signal processing

In this paper, the Hilbert-Huang transform method and time delay embedding method are applied to multiscale dynamic analysis on the time series of silicon content in hot metal collected from a medium-sized blast furnace with the inner volume of 2500 m3. The results provide clear evidence of multiscale features in blast furnace ironmaking process. Ten intrinsic mode functions (IMFs) are decomposed from the silicon content time series; the presence of noninteger fractal dimension, positive finite Kolmogorov entropy, and positive finite maximum Lyapunov exponent are found in some IMF components. In addition, the coupling of subscale structures of blast furnace system is studied using the dimension of interaction dynamics and a robust algorithm for detecting interdependence. It is found that IMF(3) is the main driver in the coupling system IMF(2) and IMF(3) while for the coupling system IMF(3) and IMF(4) neither subsystem can act as the driver. All these provide a guideline for studying blast furnace ironmaking process with multiscale theory and methods, and may open way for more candidate tools to model and control blast furnace system in the future.

An information-theoretic approach to the prediction of protein structural class

An information-theoretical approach, which combines a sequence decomposition technique and a fuzzy clustering algorithm, is proposed for prediction of protein structural class. This approach could bypass the process of selecting and comparing sequence features as done previously. First, distances between each pair of protein sequences are estimated using a conditional decomposition technique in information theory. Then, the fuzzy k-nearest neighbor algorithm is used to identify the structural class of a protein given as set of sample sequences. To verify the strength of our method, we choose three widely used datasets constructed by Chou and Zhou. It is shown by the Jackknife test that our approach represents an improvement in the prediction of accuracy over existing methods.

Measuring body temperature time series regularity using Approximate Entropy and Sample Entropy

Approximate Entropy (ApEn) and Sample Entropy (SampEn) have proven to be a valuable analyzing tool for a number of physiological signals. However, the characterization of these metrics is still lacking. We applied ApEn and SampEn to body temperature time series recorded from patients in critical state. This study was aimed at finding the optimal analytical configuration to best distinguish between survivor and non-survivor records, and at gaining additional insight into the characterization of such tools. A statistical analysis of the results was conducted to support the parameter and metric selection criteria for this type of physiological signal.

Discrimination of endocardial electrogram disorganization using a signal regularity analysis

Measures from the theory of nonlinear dynamics were applied on complex fractionated atrial electrograms (CFAEs) in order to characterize their physiological dynamic behavior. The results were obtained considering 113 short term atrial electrograms (A-EGMs) which were annotated by three experts into four classes of fractionation according to A-EGMs signal regularity. The following measures were applied on A-EGM signals: General Correlation Dimension, Approximate Entropy, Detrended Fluctuation Analysis, Lempel-Ziv Complexity, and Katz-Sevcik, Variance and Box Counting Fractal Dimension. Assessment of disorganization was evaluated by a Kruskal Wallis statistical test. Except Detrended Fluctuation Analysis and Variance Fractal Dimension, the CFAE disorganization was found statistically significant even for low significant level alpha = 0.001. Moreover, the increasing complexity of A-EGM signals was reflected by higher values of General Correlation Dimension of order 1 and Approximate Entropy.

Multistate Lempel-Ziv (MLZ) index interpretation as a measure of amplitude and complexity changes

The Lempel-Ziv complexity (LZ) has been widely used to evaluate the randomness of finite sequences. In general, the LZ complexity has been used to determine the complexity grade present in biomedical signals. The LZ complexity is not able to discern between signals with different amplitude variations and similar random components. On the other hand, amplitude parameters, as the root mean square (RMS), are not able to discern between signals with similar power distributions and different random components. In this work, we present a novel method to quantify amplitude and complexity variations in biomedical signals by means of the computation of the LZ coefficient using more than two quantification states, and with thresholds fixed and independent of the dynamic range or standard deviation of the analyzed signal: the Multistate Lempel-Ziv (MLZ) index. Our results indicate that MLZ index with few quantification levels only evaluate the complexity changes of the signal, with high number of levels, the amplitude variations, and with an intermediate number of levels informs about both amplitude and complexity variations. The study performed in diaphragmatic mechanomyographic signals shows that the amplitude variations of this signal are more correlated with the respiratory effort than the complexity variations. Furthermore, it has been observed that the MLZ index with high number of levels practically is not affected by the existence of impulsive, sinusoidal, constant and Gaussian noises compared with the RMS amplitude parameter.

Hippocampal gamma oscillations in rats

Previous studies suggested that gamma oscillations in the brain are associated with higher order cognitive functions, including selective visual attention, motor task planning, sensory perception, working memory, and dreaming rapid eye movement (REM) sleep. These oscillations are mainly observed in the cortical regions and also occur in neocortical and subcortical areas and hippocampus. These oscillations may occur under certain pathological conditions, such as epilepsy, and are mainly observed in the cortical regions and hippocampus. The previous studies have suggested that epilepsy may be associated with disturbances of autonomic nervous system(ANS) and with changes in autonomic cardioregulatory function. In this article, we investigate the influence of acute exposure to 2-aminoethoxy-diphenylborate (2-APB), a membrane-permeable inositol 1,4,5-trisphosphate (IP) receptor, and store-operated Ca(2+) channel (SOC) blocker on the complexity of hippocampal gamma oscillations. Our central hypothesis is that acute exposure to 2-APB significantly reduces the hippocampal gamma oscillations. To test this hypothesis, we use brain-slice recordings and the advanced nonlinear dynamical analysis method based on the Lempel-Ziv (LZ) estimator. Our nonlinear dynamical analysis results estimated from brain-slice recordings suggested that 2-APB exposure significantly reduces the hippocampal gamma oscillations.

Nonlinear dynamical analysis of carbachol induced hippocampal oscillations in mice

AIM

Hippocampal neuronal network and synaptic impairment underlie learning and memory deficit in Alzheimer's disease (AD) patients and animal models. In this paper, we analyzed the dynamics and complexity of hippocampal neuronal network synchronization induced by acute exposure to carbachol, a nicotinic and muscarinic receptor co-agonist, using the nonlinear dynamical model based on the Lempel-Ziv estimator. We compared the dynamics of hippocampal oscillations between wild-type (WT) and triple-transgenic (3xTg) mice, as an AD animal model. We also compared these dynamic alterations between different age groups (5 and 10 months). We hypothesize that there is an impairment of complexity of CCh-induced hippocampal oscillations in 3xTg AD mice compared to WT mice, and that this impairment is age-dependent.

METHODS

To test this hypothesis, we used electrophysiological recordings (field potential) in hippocampal slices.

RESULTS

Acute exposure to 100 micromol/L CCh induced field potential oscillations in hippocampal CA1 region, which exhibited three distinct patterns: (1) continuous neural firing, (2) repeated burst neural firing and (3) the mixed (continuous and burst) pattern in both WT and 3xTg AD mice. Based on Lempel-Ziv estimator, pattern (2) was significantly lower than patterns (1) and (3) in 3xTg AD mice compared to WT mice (P<0.001), and also in 10-month old WT mice compared to those in 5-month old WT mice (P<0.01).

CONCLUSION

These results suggest that the burst pattern (theta oscillation) of hippocampal network is selectively impaired in 3xTg AD mouse model, which may reflect a learning and memory deficit in the AD patients.