Application of Tsallis entropy to EEG: quantifying the presence of burst suppression after asphyxial cardiac arrest in rats

Electrical brain networks before and after transcranial pulsed shockwave stimulation in Alzheimer's patients

Alzheimer's disease (AD) is a neurodegenerative disorder that dramatically affects cognitive abilities and represents the most common cause of dementia. Currently, pharmacological interventions represent the main treatment to deal with the symptoms of AD; however, alternative approaches are readily sought. Transcranial pulse stimulation (TPS) is an emerging non-invasive neuromodulation technique that uses short, repetitive shockwaves with the potential to provide a wide range of vascular, metabolic, and neurotrophic changes and that has recently been shown to improve cognitive abilities in AD. This exploratory study aims to gain insight into the neurophysiological effect of one session of TPS in AD as reflected in electroencephalographic measures, e.g., spectral power, coherence, Tsallis entropy (TE), and cross-frequency coupling (cfc). We document changes in power (frontal and occipital), coherence (frontal, occipital and temporal), and TE (temporal and frontal) as well as changes in cfc (parietal-frontal, parietal-temporal, frontal-temporal). Our results emphasize the role of electroencephalographic measures as prospective markers for the neurophysiological effect of TPS.

Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction

The healthy function of the vestibular system (VS) is of vital importance for individuals to carry out their daily activities independently and safely. This study carries out Tsallis entropy (TE)-based analysis on insole force sensor data in order to extract features to differentiate between healthy and VS-diseased individuals. Using a specifically developed algorithm, we detrend the acquired data to examine the fluctuation around the trend curve in order to consider the individual's walking habit and thus increase the accuracy in diagnosis. It is observed that the TE value increases for diseased people as an indicator of the problem of maintaining balance. As one of the main contributions of this study, in contrast to studies in the literature that focus on gait dynamics requiring extensive walking time, we directly process the instantaneous pressure values, enabling a significant reduction in the data acquisition period. The extracted feature set is then inputted into fundamental classification algorithms, with support vector machine (SVM) demonstrating the highest performance, achieving an average accuracy of 95%. This study constitutes a significant step in a larger project aiming to identify the specific VS disease together with its stage. The performance achieved in this study provides a strong motivation to further explore this topic.

Time-Frequency Analysis of Somatosensory Evoked High-Frequency (600 Hz) Oscillations as an Early Indicator of Arousal Recovery after Hypoxic-Ischemic Brain Injury

Cardiac arrest (CA) remains the leading cause of coma, and early arousal recovery indicators are needed to allocate critical care resources properly. High-frequency oscillations (HFOs) of somatosensory evoked potentials (SSEPs) have been shown to indicate responsive wakefulness days following CA. Nonetheless, their potential in the acute recovery phase, where the injury is reversible, has not been tested. We hypothesize that time-frequency (TF) analysis of HFOs can determine arousal recovery in the acute recovery phase. To test our hypothesis, eleven adult male Wistar rats were subjected to asphyxial CA (five with 3-min mild and six with 7-min moderate to severe CA) and SSEPs were recorded for 60 min post-resuscitation. Arousal level was quantified by the neurological deficit scale (NDS) at 4 h. Our results demonstrated that continuous wavelet transform (CWT) of SSEPs localizes HFOs in the TF domain under baseline conditions. The energy dispersed immediately after injury and gradually recovered. We proposed a novel TF-domain measure of HFO: the total power in the normal time-frequency space (NTFS) of HFO. We found that the NTFS power significantly separated the favorable and unfavorable outcome groups. We conclude that the NTFS power of HFOs provides earlier and objective determination of arousal recovery after CA.

Automated detection of obstructive sleep apnea in more than 8000 subjects using frequency optimized orthogonal wavelet filter bank with respiratory and oximetry signals

Obstructive sleep apnea (OSA) is a common respiratory disorder marked by interruption of the respiratory tract and difficulty in breathing. The risk of serious health damage can be reduced if OSA is diagnosed and treated at an early stage. OSA is primarily diagnosed using polysomnography (PSG) monitoring performed for overnight sleep; furthermore, capturing PSG signals during the night is expensive, time-consuming, complex and highly inconvenient to patients. Hence, we are proposing to detect OSA automatically using respiratory and oximetry signals. The aim of this study is to develop a simple and computationally efficient wavelet-based automated system based on these signals to detect OSA in elderly subjects. In this study, we proposed an accurate, reliable, and less complex OSA automated detection system by using pulse oximetry (SpO₂) and respiratory signals including thoracic (ThorRes) movement, abdominal (AbdoRes) movement, and airflow (AF). These signals are collected from the Sleep Heart Health Study (SHHS) database from the National Sleep Research Resource (NSRR), which is one of the largest repositories of publicly available sleep databases. The database comprises of two groups SHHS-1 and SHHS-2, which involves 5,793 and 2,651 subjects, respectively with an average age of ≥60 years. The 30-s epochs of the signals are decomposed into sub-bands using frequency optimized orthogonal wavelet filter bank. Tsallis entropies are extracted from the sub-band coefficients of wavelet filter bank. A total 4,415,229 epochs of respiratory and oximetry signals are used to develop the model. The proposed model is developed using GentleBoost and Random under-sampling Boosting (RUSBoosted Tree) algorithms with 10-fold cross-validation technique. Our developed model has obtained the highest classification accuracy of 89.39% and 84.64% for the imbalanced and balanced datasets, respectively using 10-fold cross-validation technique. Using the 20% hold-out validation, the model yielded an accuracy of 88.26% and 84.31% for the imbalanced and balanced datasets, respectively. Hence, the respiratory and SpO2 signals-based model can be used for automated OSA detection. The results obtained from the proposed model are better than the state-of-the-art models and can be used in-home for screening the OSA.

Linear and Nonlinear Quantitative EEG Analysis during Neutral Hypnosis following an Opened/Closed Eye Paradigm

Hypnotic susceptibility is a major factor influencing the study of the neural correlates of hypnosis using EEG. In this context, while its effects on the response to hypnotic suggestions are undisputed, less attention has been paid to “neutral hypnosis” (i.e., the hypnotic condition in absence of suggestions). Furthermore, although an influence of opened and closed eye condition onto hypnotizability has been reported, a systematic investigation is still missing. Here, we analyzed EEG signals from 34 healthy subjects with low (LS), medium (MS), and (HS) hypnotic susceptibility using power spectral measures (i.e., TPSD, PSD) and Lempel-Ziv-Complexity (i.e., LZC, fLZC). Indeed, LZC was found to be more suitable than other complexity measures for EEG analysis, while it has been never used in the study of hypnosis. Accordingly, for each measure, we investigated within-group differences between rest and neutral hypnosis, and between opened-eye/closed-eye conditions under both rest and neutral hypnosis. Then, we evaluated between-group differences for each experimental condition. We observed that, while power estimates did not reveal notable differences between groups, LZC and fLZC were able to distinguish between HS, MS, and LS. In particular, we found a left frontal difference between HS and LS during closed-eye rest. Moreover, we observed a symmetric pattern distinguishing HS and LS during closed-eye hypnosis. Our results suggest that LZC is better capable of discriminating subjects with different hypnotic susceptibility, as compared to standard power analysis.

An enhanced cerebral recovery index for coma prognostication following cardiac arrest

Prognostication of coma outcomes following cardiac arrest is both qualitative and poorly understood in current practice. Existing quantitative metrics are powerful, but lack rigorous approaches to classification. This is due, in part, to a lack of available data on the population of interest. In this paper we describe a novel retrospective data set of 167 cardiac arrest patients (spanning three institutions) who received electroencephalography (EEG) monitoring. We utilized a subset of the collected data to generate features that measured the connectivity, complexity and category of EEG activity. A subset of these features was included in a logistic regression model to estimate a dichotomized cerebral performance category score at discharge. We compared the predictive performance of our method against an established EEG-based alternative, the Cerebral Recovery Index (CRI) and show that our approach more reliably classifies patient outcomes, with an average increase in AUC of 0.27.

Prediction of the outcome in cardiac arrest patients undergoing hypothermia using EEG wavelet entropy

Cardiac arrest (CA) is the leading cause of death in the United States. Induction of hypothermia has been found to improve the functional recovery of CA patients after resuscitation. However, there is no clear guideline for the clinicians yet to determine the prognosis of the CA when patients are treated with hypothermia. The present work aimed at the development of a prognostic marker for the CA patients undergoing hypothermia. A quantitative measure of the complexity of Electroencephalogram (EEG) signals, called wavelet sub-band entropy, was employed to predict the patients' outcomes. We hypothesized that the EEG signals of the patients who survived would demonstrate more complexity and consequently higher values of wavelet sub-band entropies. A dataset of 16-channel EEG signals collected from CA patients undergoing hypothermia at Long Beach Memorial Medical Center was used to test the hypothesis. Following preprocessing of the signals and implementation of the wavelet transform, the wavelet sub-band entropies were calculated for different frequency bands and EEG channels. Then the values of wavelet sub-band entropies were compared among two groups of patients: survived vs. non-survived. Our results revealed that the brain high frequency oscillations (between 64100 Hz) captured from the inferior frontal lobes are significantly more complex in the CA patients who survived (p-value <; 0.02). Given that the non-invasive measurement of EEG is part of the standard clinical assessment for CA patients, the results of this study can enhance the management of the CA patients treated with hypothermia.

Tsallis entropy and sparse reconstructive dictionary learning for exudate detection in diabetic retinopathy

Computer-assisted automated exudate detection is crucial for large-scale screening of diabetic retinopathy (DR). The motivation of this work is robust and accurate detection of low contrast and isolated hard exudates using fundus imaging. Gabor filtering is first performed to enhance exudate visibility followed by Tsallis entropy thresholding. The obtained candidate exudate pixel map is useful for further removal of falsely detected candidates using sparse-based dictionary learning and classification. Two reconstructive dictionaries are learnt using the intensity, gradient, local energy, and transform domain features extracted from exudate and background patches of the training fundus images. Then, a sparse representation-based classifier separates the true exudate pixels from false positives using least reconstruction error. The proposed method is evaluated on the publicly available e-ophtha EX and standard DR database calibration level 1 (DIARETDB1) databases and high exudate detection performance is achieved. In the e-ophtha EX database, mean sensitivity of 85.80% and positive predictive value of 57.93% are found. For the DIARETDB1 database, an area under the curve of 0.954 is obtained.

Novel Burst Suppression Segmentation in the Joint Time-Frequency Domain for EEG in Treatment of Status Epilepticus

We developed a method to distinguish bursts and suppressions for EEG burst suppression from the treatments of status epilepticus, employing the joint time-frequency domain. We obtained the feature used in the proposed method from the joint use of the time and frequency domains, and we estimated the decision as to whether the measured EEG was a burst segment or suppression segment by the maximum likelihood estimation. We evaluated the performance of the proposed method in terms of its accordance with the visual scores and estimation of the burst suppression ratio. The accuracy was higher than the sole use of the time or frequency domains, as well as conventional methods conducted in the time domain. In addition, probabilistic modeling provided a more simplified optimization than conventional methods. Burst suppression quantification necessitated precise burst suppression segmentation with an easy optimization; therefore, the excellent discrimination and the easy optimization of burst suppression by the proposed method appear to be beneficial.

Maximum-likelihood q-estimator uncovers the role of potassium at neuromuscular junctions

Recently, we demonstrated the existence of nonextensive behavior in neuromuscular transmission (da Silva et al. in Phys Rev E 84:041925, 2011). In this letter, we first obtain a maximum-likelihood q-estimator to calculate the scale factor ([Formula: see text]) and the q-index of q-Gaussian distributions. Next, we use the indexes to analyze spontaneous miniature end plate potentials in electrophysiological recordings from neuromuscular junctions. These calculations were performed assuming both normal and high extracellular potassium concentrations [Formula: see text]. This protocol was used to test the validity of Tsallis statistics under electrophysiological conditions closely resembling physiological stimuli. The analysis shows that q-indexes are distinct depending on the extracellular potassium concentration. Our letter provides a general way to obtain the best estimate of parameters from a q-Gaussian distribution function. It also expands the validity of Tsallis statistics in realistic physiological stimulus conditions. In addition, we discuss the physical and physiological implications of these findings.

Electrophysiological Monitoring of Brain Injury and Recovery after Cardiac Arrest

Reliable prognostic methods for cerebral functional outcome of post cardiac-arrest (CA) patients are necessary, especially since therapeutic hypothermia (TH) as a standard treatment. Traditional neurophysiological prognostic indicators, such as clinical examination and chemical biomarkers, may result in indecisive outcome predictions and do not directly reflect neuronal activity, though they have remained the mainstay of clinical prognosis. The most recent advances in electrophysiological methods--electroencephalography (EEG) pattern, evoked potential (EP) and cellular electrophysiological measurement--were developed to complement these deficiencies, and will be examined in this review article. EEG pattern (reactivity and continuity) provides real-time and accurate information for early-stage (particularly in the first 24 h) hypoxic-ischemic (HI) brain injury patients with high sensitivity. However, the signal is easily affected by external stimuli, thus the measurements of EP should be combined with EEG background to validate the predicted neurologic functional result. Cellular electrophysiology, such as multi-unit activity (MUA) and local field potentials (LFP), has strong potential for improving prognostication and therapy by offering additional neurophysiologic information to understand the underlying mechanisms of therapeutic methods. Electrophysiology provides reliable and precise prognostication on both global and cellular levels secondary to cerebral injury in cardiac arrest patients treated with TH.

Band specific changes in thalamocortical synchrony in field potentials after cardiac arrest induced global hypoxia

Cardiac Arrest (CA) leads to a global hypoxic-ischemic injury in the brain leading to a poor neurological outcome. Understanding the mechanisms of functional disruption in various regions of the brain may be essential for the development of improved diagnostic and therapeutic solutions. Using controlled laboratory experiment with animal models of CA, our primary focus here is on understanding the functional changes in the thalamus and the cortex, associated with the injury and acute recovery upon resuscitation. Specifically, to study the changes in thalamocortical synchrony through these periods, we acquired local field potentials (LFPs) from the ventroposterior lateral (VPL) nucleus of the thalamus and the forelimb somatosensory cortex (S1FL) in rats after asphyxial CA. Band-specific relative Hilbert phases were used to analyze synchrony between the LFPs. We observed that the CA induced global ischemia changes the local phase-relationships by introducing a phase-lag in both the thalamus and the cortex, while the synchrony between the two regions is nearly completely lost after CA.

Effect of hypothermia on cortical and thalamic signals in anesthetized rats

Beneficial effects of hypothermia on subjects with neuro-pathologies have been well demonstrated in both animal studies and clinical trials. Although it is known that temperature significantly impacts neurological injuries, the underlying mechanism remains unclear. We studied the effect of temperature modulation on neural signals in the cortex and the thalamus in uninjured brains of anesthetized rats. Six rats were divided into a hypothermic (32 to 34 °C, n=3) and a hyperthermic group (38.5 to 39.5 °C, n=3). EEG, and extracellular signals from somatosensory cortex and the ventral posterolateral nucleus of thalamus were recorded at different temperature phases (normothermia (36.5 to 37.5 °C) and hypothermia or hyperthermia). During hypothermia, similar burst suppression (BS) patterns were observed in cortical and thalamic signals as in EEG, but thalamic activity was not completely under suppression when both EEG and cortical signals were electrically silent. In addition, our results showed that hypothermia significantly increased the burst suppression ratio (BSR) in EEG, cortical and thalamic signals by 3.42, 3.25, 7.29 times respectively (P<0.01), and prolonged the latency of neuronal response in cortex to median nerve stimulation from 9 ms to 16 ms (P<0.01). Furthermore, during normothermia, the correlation coefficient between thalamic and cortical signals was 0.35±0.02 while during hypothermia, it decreased to 0.16±0.03 with statistical significance (P<0.01). These results can potentially assist in better understanding the effects of hypothermia.

Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients

A few studies estimating temperature complexity have found decreased Shannon entropy, during severe stress. In this study, we measured both Shannon and Tsallis entropy of temperature signals in a cohort of critically ill patients and compared these measures with the sequential organ failure assessment (SOFA) score, in terms of intensive care unit (ICU) mortality. Skin temperature was recorded in 21 mechanically ventilated patients, who developed sepsis and septic shock during the first 24 h of an ICU-acquired infection. Shannon and Tsallis entropies were calculated in wavelet-based decompositions of the temperature signal. Statistically significant differences of entropy features were tested between survivors and non-survivors and classification models were built, for predicting final outcome. Significantly reduced Tsallis and Shannon entropies were found in non-survivors (seven patients, 33%) as compared to survivors. Wavelet measurements of both entropy metrics were found to predict ICU mortality better than SOFA, according to a combination of area under the curve, sensitivity and specificity values. Both entropies exhibited similar prognostic accuracy. Combination of SOFA and entropy presented improved the outcome of univariate models. We suggest that reduced wavelet Shannon and Tsallis entropies of temperature signals may complement SOFA in mortality prediction, during the first 24 h of an ICU-acquired infection.

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

OBJECTIVE

Burst suppression is an electroencephalogram pattern in which bursts of electrical activity alternate with an isoelectric state. This pattern is commonly seen in states of severely reduced brain activity such as profound general anesthesia, anoxic brain injuries, hypothermia and certain developmental disorders. Devising accurate, reliable ways to quantify burst suppression is an important clinical and research problem. Although thresholding and segmentation algorithms readily identify burst suppression periods, analysis algorithms require long intervals of data to characterize burst suppression at a given time and provide no framework for statistical inference.

APPROACH

We introduce the concept of the burst suppression probability (BSP) to define the brain's instantaneous propensity of being in the suppressed state. To conduct dynamic analyses of burst suppression we propose a state-space model in which the observation process is a binomial model and the state equation is a Gaussian random walk. We estimate the model using an approximate expectation maximization algorithm and illustrate its application in the analysis of rodent burst suppression recordings under general anesthesia and a patient during induction of controlled hypothermia.

MAIN RESULT

The BSP algorithms track burst suppression on a second-to-second time scale, and make possible formal statistical comparisons of burst suppression at different times.

SIGNIFICANCE

The state-space approach suggests a principled and informative way to analyze burst suppression that can be used to monitor, and eventually to control, the brain states of patients in the operating room and in the intensive care unit.

Real-time segmentation of burst suppression patterns in critical care EEG monitoring

OBJECTIVE

Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients.

METHODS

A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression.

RESULTS

Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings.

CONCLUSIONS

Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers.

SIGNIFICANCE

Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth.

Investigating univariate temporal patterns for intrinsic connectivity networks based on complexity and low-frequency oscillation: a test-retest reliability study

Intrinsic connectivity networks (ICNs) are composed of spatial components and time courses. The spatial components of ICNs were discovered with moderate-to-high reliability. So far as we know, few studies focused on the reliability of the temporal patterns for ICNs based their individual time courses. The goals of this study were twofold: to investigate the test-retest reliability of temporal patterns for ICNs, and to analyze these informative univariate metrics. Additionally, a correlation analysis was performed to enhance interpretability. Our study included three datasets: (a) short- and long-term scans, (b) multi-band echo-planar imaging (mEPI), and (c) eyes open or closed. Using dual regression, we obtained the time courses of ICNs for each subject. To produce temporal patterns for ICNs, we applied two categories of univariate metrics: network-wise complexity and network-wise low-frequency oscillation. Furthermore, we validated the test-retest reliability for each metric. The network-wise temporal patterns for most ICNs (especially for default mode network, DMN) exhibited moderate-to-high reliability and reproducibility under different scan conditions. Network-wise complexity for DMN exhibited fair reliability (ICC<0.5) based on eyes-closed sessions. Specially, our results supported that mEPI could be a useful method with high reliability and reproducibility. In addition, these temporal patterns were with physiological meanings, and certain temporal patterns were correlated to the node strength of the corresponding ICN. Overall, network-wise temporal patterns of ICNs were reliable and informative and could be complementary to spatial patterns of ICNs for further study.

Assessing thalamocortical functional connectivity with Granger causality

Assessment of network connectivity across multiple brain regions is critical to understanding the mechanisms underlying various neurological disorders. Conventional methods for assessing dynamic interactions include cross-correlation and coherence analysis. However, these methods do not reveal the direction of information flow, which is important for studying the highly directional neurological system. Granger causality (GC) analysis can characterize the directional influences between two systems. We tested GC analysis for its capability to capture directional interactions within both simulated and in vivo neural networks. The simulated networks consisted of Hindmarsh-Rose neurons; GC analysis was used to estimate the causal influences between two model networks. Our analysis successfully detected asymmetrical interactions between these networks ( , t -test). Next, we characterized the relationship between the "electrical synaptic strength" in the model networks and interactions estimated by GC analysis. We demonstrated the novel application of GC to monitor interactions between thalamic and cortical neurons following ischemia induced brain injury in a rat model of cardiac arrest (CA). We observed that during the post-CA acute period the GC interactions from the thalamus to the cortex were consistently higher than those from the cortex to the thalamus ( 1.983±0.278 times higher, p = 0.021). In addition, the dynamics of GC interactions between the thalamus and the cortex were frequency dependent. Our study demonstrated the feasibility of GC to monitor the dynamics of thalamocortical interactions after a global nervous system injury such as CA-induced ischemia, and offers preferred alternative applications in characterizing other inter-regional interactions in an injured brain.

Nonlinear analysis of the change points between A and B phases during the Cyclic Alternating Pattern under normal sleep

This study analyzes the nonlinear properties of the EEG at transition points of the sequences that build the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon built up by consecutive sequences of activations and non-activations observed during the sleep time. The sleep condition can be evaluated from the patterns formed by these sequences. Eleven recordings from healthy and good sleepers were included in this study. We investigated the complexity properties of the signal at the onset and offset of the activations. The results show that EEG signals present significant differences (p&#60;0.05) between activations and non-activations in the Sample Entropy and Tsallis Entropy indices. These indices could be useful in the development of automatic methods for detecting the onset and offset of the activations, leading to significant savings of the physician's time by simplifying the manual inspection task.

Scalp EEG-based discrimination of cognitive deficits after traumatic brain injury using event-related Tsallis entropy analysis

Traumatic brain injury (TBI) is the leading cause of death and disability in children and adolescents in the U.S. This is a pilot study, which explores the discrimination of chronic TBI from normal controls using scalp EEG during a memory task. Tsallis entropies are computed for responses during an old-new memory recognition task. A support vector machine model is constructed to discriminate between normal and moderate/severe TBI individuals using Tsallis entropies as features. Numerical analyses of 30 records (15 normal and 15 TBI) show a maximum discrimination accuracy of 93% (p-value = 7.8557E-5) using four features. These results suggest the potential of scalp EEG as an efficacious method for noninvasive diagnosis of TBI.

Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms

PURPOSE

Architectural distortion is an important sign of early breast cancer. We present methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions.

METHODS

Potential sites of architectural distortion were detected using node maps obtained through the application of a bank of Gabor filters and linear phase portrait modeling. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by three types of entropy measures of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum, including Shannon's entropy, Tsallis entropy for nonextensive systems, and Rényi entropy for extensive systems.

RESULTS

Using the entropy measures with stepwise logistic regression and the leave-one-patient-out method for feature selection and cross-validation, an artificial neural network resulted in an area under the receiver operating characteristic curve of 0.75. Free-response receiver operating characteristics indicated a sensitivity of 0.80 at 5.2 false positives (FPs) per patient.

CONCLUSION

The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, with a high sensitivity and a moderate number of FPs per patient. The results are promising and may be improved with additional features to characterize subtle abnormalities and larger databases including prior mammograms.

The utility of amplitude-integrated EEG and NIRS measurements as indices of hypoxic ischaemia in the newborn pig

OBJECTIVE

The early detection and stratification of potential hypoxic ischaemia (HI) injury in neonates are crucial for reducing the risk of neural disability. This study investigates early changes in brain function caused by acute HI of varying severities in the neonatal pig.

METHODS

Two non-invasive techniques, amplitude-integrated electroencephalogram (aEEG) and near-infrared spectroscopy (NIRS), were used to monitor electrocortical and cerebral haemodynamic function, respectively. The fraction of inspired oxygen (FiO(2)) was varied to produce different HI severities. The sensitivity and HI correlation of these methods were systematically analysed to assess their abilities to both detect injury early and assess HI severity accurately.

RESULTS

The tissue oxygen index measured via NIRS detected acute changes in cerebral oxygenation and was highly sensitive to HI (sensitivity=0.97), whereas aEEG was comparatively insensitive to HI. On the other hand, aEEG measurements correlated well with FiO(2) during the entire HI event as well as the 3-h recovery period (R=0.43-0.61). NIRS measurements did not correlate well with FiO(2).

CONCLUSIONS

Parameters measured via aEEG and NIRS displayed different time profiles during and following the HI event.

SIGNIFICANCE

These results highlight the potential advantage of using aEEG and NIRS in conjunction to monitor neonatal brain function, and provide an objective and rigorous method for the characterisation of cerebral function both during and following HI insults.

Nonextensivity and self-affinity in the mammalian neuromuscular junction

We study time series and the spontaneous miniature end-plate potentials (MEPPs) of mammals recorded at neuromuscular junctions using two different approaches: generalized thermostatistics and detrended fluctuation analysis (DFA). Classical concepts establish that the magnitude of these potentials is characterized by Gaussian statistics and that their intervals are randomly displayed. First we show that MEPP distributions adequately satisfy the q-Gaussian distributions that maximize the Tsallis entropy, indicating their nonextensive and nonequilibrium behavior. We then examine the intervals between the miniature potentials via DFA, where the profile of the intervals between events configures a deviation from the expected random behavior. Some possible physiological substrates for these findings are discussed.