Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study

REM Sleep Stage Identification with Raw Single-Channel EEG

This paper focused on creating an interpretable model for automatic rapid eye movement (REM) and non-REM sleep stage scoring for a single-channel electroencephalogram (EEG). Many methods attempt to extract meaningful information to provide to a learning algorithm. This method attempts to let the model extract the meaningful interpretable information by providing a smaller number of time-invariant signal filters for five frequency ranges using five CNN algorithms. A bi-directional GRU algorithm was applied to the output to incorporate time transition information. Training and tests were run on the well-known sleep-EDF-expanded database. The best results produced 97% accuracy, 93% precision, and 89% recall.

Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

BACKGROUND

Sleep spindles are an indicator of the development and integrity of the central nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming and typically requires experienced experts. Automated detection of sleep spindles would greatly facilitate this analysis. Deep learning methods have been widely used recently in EEG analysis.

METHOD

We have developed a deep learning-based automated sleep spindle detection system, Deep-spindle, which employs a convolutional neural network (CNN) combined with a bidirectional Long Short-Term Memory (LSTM) network, which could assist in the analysis of infant sleep spindles. Deep-spindle was trained on the EEGs of ex-term infants to estimate the number and duration of sleep spindles. The ex-term EEG on channel F4-C4 was split into training (N=81) and validation (N=30) sets. An additional 30 ex-term EEG and 54 ex-preterm infant EEGs (channel F4-C4 and F3-C3) were used as an independent test set.

RESULT

Deep-spindle detected the number of sleep spindles with 91.9% to 96.5% sensitivity and 95.3% to 96.7% specificity, and estimated sleep spindle duration with a percent error of 13.1% to 19.1% in the independent test set. For each detected spindle event, the user is presented with amplitude, power spectral density and the spectrogram of the corresponding spindle EEG, and the probability of the event being a sleep spindle event, providing the user with insight into why the event is predicted as a sleep spindle to provide confidence in the predictions.

CONCLUSION

The Deep-spindle system can reduce physicians' workload, demonstrating the potential to assist physicians in the automated analysis of sleep spindles in infants.

Spindle-AI: Sleep spindle number and duration estimation in infant EEG

OBJECTIVE

Sleep spindle features show developmental changes during infancy and have the potential to provide an early biomarker for abnormal brain maturation. Manual identification of sleep spindles in the electroencephalogram (EEG) is time-consuming and typically requires highly-trained experts. Automated detection of sleep spindles would greatly facilitate this analysis. Research on the automatic detection of sleep spindles in infant EEG has been limited to-date.

METHODS

We present a random forest-based sleep spindle detection method (Spindle-AI) to estimate the number and duration of sleep spindles in EEG collected from 141 ex-term born infants, recorded at 4 months of age. The signal on channel F4-C4 was split into a training set (81 ex-term) and a validation set (30 ex-term). An additional 30 ex-term infant EEGs (channel F4-C4 and channel F3-C3) were used as an independent test set. Fourteen features were selected for input into a random forest algorithm to estimate the number and duration of spindles and the results were compared against sleep spindles annotated by an experienced clinical physiologist.

RESULTS

The prediction of the number of sleep spindles in the independent test set demonstrated 93.3% to 93.9% sensitivity, 90.7% to 91.5% specificity, and 89.2% to 90.1% precision. The duration estimation of sleep spindle events in the independent test set showed a percent error of 5.7% to 7.4%.

CONCLUSION AND SIGNIFICANCE

Spindle-AI has been implemented as a web server that has the potential to assist clinicians in the fast and accurate monitoring of sleep spindles in infant EEGs.

Wavelet skeletons in sleep EEG-monitoring as biomarkers of early diagnostics of mild cognitive impairment

Many neuro-degenerative diseases are difficult to diagnose in their early stages. For example, early diagnosis of Mild Cognitive Impairment (MCI) requires a wide variety of tests to distinguish MCI symptoms and normal consequences of aging. In this article, we use the wavelet-skeleton approach to find some characteristic patterns in the electroencephalograms (EEGs) of healthy adult patients and patients with cognitive dysfunctions. We analyze the EEG activity recorded during natural sleep of 11 elderly patients aged between 60 and 75, six of whom have mild cognitive impairment, and apply a nonlinear analysis method based on continuous wavelet transformskeletons. Our studies show that a comprehensive analysis of EEG signals of the entire sleep state allows us to identify a significant decrease in the average duration of oscillatory patterns in the frequency band [12; 14] Hz in the presence of mild cognitive impairment. Thus, the changes in this frequency range can be interpreted as related to the activity in the motor cortex, as a candidate for developing the criteria for early objective MCI.

Random Forest-based Algorithm for Sleep Spindle Detection in Infant EEG

Sleep spindles are associated with normal brain development, memory consolidation and infant sleep-dependent brain plasticity and can be used by clinicians in the assessment of brain development in infants. Sleep spindles can be detected in EEG, however, identifying sleep spindles in EEG recordings manually is very time-consuming and typically requires highly trained experts. Research on the automatic detection of sleep spindles in infant EEGs has been limited to-date. In this study, we present a novel supervised machine learning-based algorithm to detect sleep spindles in infant EEG recordings. EEGs collected from 141 ex-term born infants and 6 ex-preterm born infants, recorded at 4 months of age (adjusted), were used to train and test the algorithm. Sleep spindles were annotated by experienced clinical physiologists as the gold standard. The dataset was split into training (81 ex-term), validation (30 ex-term), and testing (30 ex-term + 6 ex-preterm) set. 15 features were selected for input into a random forest algorithm. Sleep spindles were detected in the ex-term infant EEG test set with 92.1% sensitivity and 95.2% specificity. For ex-preterm born infants, the sensitivity and specificity were 80.3% and 91.8% respectively. The proposed algorithm has the potential to assist researchers and clinicians in the automated analysis of sleep spindles in infant EEG.

Sleep Spindle Detection Using RUSBoost and Synchrosqueezed Wavelet Transform

Sleep spindles are important electroencephalographic (EEG) waveforms in sleep medicine; however, it is burdensome even for experts to detect spindles, so automatic spindle detection methodologies have been investigated. Conventional methods utilize waveforms template matching or machine learning for detecting spindles. In the former approach, it is necessary to tune thresholds for individual adaptation, while the latter approach has the problem of imbalanced data because the amount of sleep spindles is small compared with the entire EEG data. The present work proposes a sleep spindle detection method that combines wavelet synchrosqueezed transform (SST) and random under-sampling boosting (RUSBoost). SST is a time-frequency analysis method suitable for extracting features of spindle waveforms. RUSBoost is a framework for coping with the imbalanced data problem. The proposed SST-RUS can deal with the imbalanced data in spindle detection and does not require threshold tuning because RUSBoost uses majority voting of weak classifiers for discrimination. The performance of SST-RUS was validated using an open-access database called the Montreal archives of sleep studies cohort 1 (MASS-C1), which showed an F-measure of 0.70 with a sensitivity of 76.9% and a positive predictive value of 61.2%. The proposed method can reduce the burden of PSG scoring.

Sleep Spindles: Mechanisms and Functions

Sleep spindles are burstlike signals in the electroencephalogram (EEG) of the sleeping mammalian brain and electrical surface correlates of neuronal oscillations in thalamus. As one of the most inheritable sleep EEG signatures, sleep spindles probably reflect the strength and malleability of thalamocortical circuits that underlie individual cognitive profiles. We review the characteristics, organization, regulation, and origins of sleep spindles and their implication in non-rapid-eye-movement sleep (NREMS) and its functions, focusing on human and rodent. Spatially, sleep spindle-related neuronal activity appears on scales ranging from small thalamic circuits to functional cortical areas, and generates a cortical state favoring intracortical plasticity while limiting cortical output. Temporally, sleep spindles are discrete events, part of a continuous power band, and elements grouped on an infraslow time scale over which NREMS alternates between continuity and fragility. We synthesize diverse and seemingly unlinked functions of sleep spindles for sleep architecture, sensory processing, synaptic plasticity, memory formation, and cognitive abilities into a unifying sleep spindle concept, according to which sleep spindles 1) generate neural conditions of large-scale functional connectivity and plasticity that outlast their appearance as discrete EEG events, 2) appear preferentially in thalamic circuits engaged in learning and attention-based experience during wakefulness, and 3) enable a selective reactivation and routing of wake-instated neuronal traces between brain areas such as hippocampus and cortex. Their fine spatiotemporal organization reflects NREMS as a physiological state coordinated over brain and body and may indicate, if not anticipate and ultimately differentiate, pathologies in sleep and neurodevelopmental, -degenerative, and -psychiatric conditions.

Sleep spindle detection based on non-experts: A validation study

Accurate and efficient detection of sleep spindles is a methodological challenge. The present study describes a method of using non-experts for manual detection of sleep spindles. We recruited five experts and 168 non-experts to manually identify spindles in stage N2 and stage N3 sleep data using a MATLAB interface. Scorers classified each spindle into definite and indefinite spindle (with weights of 1 and 0.5, respectively). First, a method of optimizing the thresholds of the expert/non-expert group consensus according to the results of experts and non-experts themselves is described. Using this method, we established expert and non-expert group standards from expert and non-expert scorers, respectively, and evaluated the performance of the non-expert group standards by compared with the expert group standard (termed EGS). The results indicated that the highest performance was the non-expert group standard when definite spindles were only considered (termed nEGS-1; F1 score = 0.78 for N2; 0.68 for N3). Second, four automatic spindle detection methods were compared with the EGS. We found that the performance of nEGS-1 versus EGS was higher than that of the four automated methods. Our results also showed positive correlation between the mean F1 score of individual expert in EGS and the F1 score of nEGS-1 versus EGS across 30 segments of stage N2 data (r = 0.61, P < 0.001). Further, we found that six and nine non-experts were needed to manually identify spindles in stages N2 and N3, respectively, while maintaining acceptable performance of nEGS-1 versus EGS (F1 score = 0.79 for N2; 0.64 for N3). In conclusion, this study establishes a detailed process for detection of sleep spindles by non-experts in a crowdsourcing scheme.

Fast and Stable Signal Deconvolution via Compressible State-Space Models

OBJECTIVE

Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred, and undersampled data, where the signals are in the form of discrete events distributed in time and space.

METHODS

We introduce compressible state-space models as a framework to model and estimate such discrete events. These state-space models admit abrupt changes in the states and have a convergent transition matrix, and are coupled with compressive linear measurements. We consider a dynamic compressive sensing optimization problem and develop a fast solution, using two nested expectation maximization algorithms, to jointly estimate the states as well as their transition matrices. Under suitable sparsity assumptions on the dynamics, we prove optimal stability guarantees for the recovery of the states and present a method for the identification of the underlying discrete events with precise confidence bounds.

RESULTS

We present simulation studies as well as application to calcium deconvolution and sleep spindle detection, which verify our theoretical results and show significant improvement over existing techniques.

CONCLUSION

Our results show that by explicitly modeling the dynamics of the underlying signals, it is possible to construct signal deconvolution solutions that are scalable, statistically robust, and achieve high temporal resolution.

SIGNIFICANCE

Our proposed methodology provides a framework for modeling and deconvolution of noisy, blurred, and undersampled measurements in a fast and stable fashion, with potential application to a wide range of biological data.

Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods

Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.

Automatic sleep spindle detection: benchmarking with fine temporal resolution using open science tools

Sleep spindle properties index cognitive faculties such as memory consolidation and diseases such as major depression. For this reason, scoring sleep spindle properties in polysomnographic recordings has become an important activity in both research and clinical settings. The tediousness of this manual task has motivated efforts for its automation. Although some progress has been made, increasing the temporal accuracy of spindle scoring and improving the performance assessment methodology are two aspects needing more attention. In this paper, four open-access automated spindle detectors with fine temporal resolution are proposed and tested against expert scoring of two proprietary and two open-access databases. Results highlight several findings: (1) that expert scoring and polysomnographic databases are important confounders when comparing the performance of spindle detectors tested using different databases or scorings; (2) because spindles are sparse events, specificity estimates are potentially misleading for assessing automated detector performance; (3) reporting the performance of spindle detectors exclusively with sensitivity and specificity estimates, as is often seen in the literature, is insufficient; including sensitivity, precision and a more comprehensive statistic such as Matthew's correlation coefficient, F1-score, or Cohen's κ is necessary for adequate evaluation; (4) reporting statistics for some reasonable range of decision thresholds provides a much more complete and useful benchmarking; (5) performance differences between tested automated detectors were found to be similar to those between available expert scorings; (6) much more development is needed to effectively compare the performance of spindle detectors developed by different research teams. Finally, this work clarifies a long-standing but only seldomly posed question regarding whether expert scoring truly is a reliable gold standard for sleep spindle assessment.

Spindles in Svarog: framework and software for parametrization of EEG transients

We present a complete framework for time-frequency parametrization of EEG transients, based upon matching pursuit (MP) decomposition, applied to the detection of sleep spindles. Ranges of spindles duration (>0.5 s) and frequency (11-16 Hz) are taken directly from their standard definitions. Minimal amplitude is computed from the distribution of the root mean square (RMS) amplitude of the signal within the frequency band of sleep spindles. Detection algorithm depends on the choice of just one free parameter, which is a percentile of this distribution. Performance of detection is assessed on the first cohort/second subset of the Montreal Archive of Sleep Studies (MASS-C1/SS2). Cross-validation performed on the 19 available overnight recordings returned the optimal percentile of the RMS distribution close to 97 in most cases, and the following overall performance measures: sensitivity 0.63 ± 0.06, positive predictive value 0.47 ± 0.08, and Matthews coefficient of correlation 0.51 ± 0.04. These concordances are similar to the results achieved on this database by other automatic methods. Proposed detailed parametrization of sleep spindles within a universal framework, encompassing also other EEG transients, opens new possibilities of high resolution investigation of their relations and detailed characteristics. MP decomposition, selection of relevant structures, and simple creation of EEG profiles used previously for assessment of brain activity of patients in disorders of consciousness are implemented in a freely available software package Svarog (Signal Viewer, Analyzer and Recorder On GPL) with user-friendly, mouse-driven interface for review and analysis of EEG. Svarog can be downloaded from http://braintech.pl/svarog.

Combining time-frequency and spatial information for the detection of sleep spindles

EEG sleep spindles are short (0.5-2.0 s) bursts of activity in the 11-16 Hz band occurring during non-rapid eye movement (NREM) sleep. This sporadic activity is thought to play a role in memory consolidation, brain plasticity, and protection of sleep integrity. Many automatic detectors have been proposed to assist or replace experts for sleep spindle scoring. However, these algorithms usually detect too many events making it difficult to achieve a good tradeoff between sensitivity (Se) and false detection rate (FDr). In this work, we propose a semi-automatic detector comprising a sensitivity phase based on well-established criteria followed by a specificity phase using spatial and spectral criteria. In the sensitivity phase, selected events are those which amplitude in the 10-16 Hz band and spectral ratio characteristics both reject a null hypothesis (p < 0.1) stating that the considered event is not a spindle. This null hypothesis is constructed from events occurring during rapid eye movement (REM) sleep epochs. In the specificity phase, a hierarchical clustering of the selected candidates is done based on events' frequency and spatial position along the anterior-posterior axis. Only events from the classes grouping most (at least 80%) spindles scored by an expert are kept. We obtain Se = 93.2% and FDr = 93.0% in the first phase and Se = 85.4% and FDr = 86.2% in the second phase. For these two phases, Matthew's correlation coefficients are respectively 0.228 and 0.324. Results suggest that spindles are defined by specific spatio-spectral properties and that automatic detection methods can be improved by considering these features.

A comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequencies

Sleep spindles are frequently studied for their relationship with state and trait cognitive variables, and they are thought to play an important role in sleep-related memory consolidation. Due to their frequent occurrence in NREM sleep, the detection of sleep spindles is only feasible using automatic algorithms, of which a large number is available. We compared subject averages of the spindle parameters computed by a fixed frequency (FixF) (11–13 Hz for slow spindles, 13–15 Hz for fast spindles) automatic detection algorithm and the individual adjustment method (IAM), which uses individual frequency bands for sleep spindle detection. Fast spindle duration and amplitude are strongly correlated in the two algorithms, but there is little overlap in fast spindle density and slow spindle parameters in general. The agreement between fixed and manually determined sleep spindle frequencies is limited, especially in case of slow spindles. This is the most likely reason for the poor agreement between the two detection methods in case of slow spindle parameters. Our results suggest that while various algorithms may reliably detect fast spindles, a more sophisticated algorithm primed to individual spindle frequencies is necessary for the detection of slow spindles as well as individual variations in the number of spindles in general.

Pattern recognition with adaptive-thresholds for sleep spindle in high density EEG signals

Medicine and Surgery, University of Pisa, via Savi 10, 56126, Pisa, Italy Sleep spindles are electroencephalographic oscillations peculiar of non-REM sleep, related to neuronal mechanisms underlying sleep restoration and learning consolidation. Based on their very singular morphology, sleep spindles can be visually recognized and detected, even though this approach can lead to significant mis-detections. For this reason, many efforts have been put in developing a reliable algorithm for spindle automatic detection, and a number of methods, based on different techniques, have been tested via visual validation. This work aims at improving current pattern recognition procedures for sleep spindles detection by taking into account their physiological sources of variability. We provide a method as a synthesis of the current state of art that, improving dynamic threshold adaptation, is able to follow modification of spindle characteristics as a function of sleep depth and inter-subjects variability. The algorithm has been applied to physiological data recorded by a high density EEG in order to perform a validation based on visual inspection and on evaluation of expected results from normal night sleep in healthy subjects.

Amplitude normalization applied to an artificial neural network-based automatic sleep spindle detection system

Sleep spindles are significant rhythmic transients present in the sleep electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Automatic sleep spindle detection techniques are sought for the automation of sleep staging and the detailed study of sleep spindle patterns, of possible physiological significance. A deficiency of many of the available automatic detection techniques is their reliance on the amplitude level of the recorded EEG voltage values. In the present work, an automatic sleep spindle detection system that has been previously proposed, using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), was evaluated using a voltage amplitude normalization procedure, with the aim of making the performance of the ANN independent of the absolute voltage level of the individual subjects' recordings. The application of the normalization procedure led to a reduction in the false positive rate (FPR) as well as in the sensitivity. When the ANN was trained on a combination of data from healthy subjects, the reduction of FPR was from 42.6% to 19%, while the sensitivity of the ANN was kept at acceptable levels, i.e., 73.4% for the normalized procedure vs 84.6% for the non-normalized procedure.

Performance evaluation of an Artificial Neural Network automatic spindle detection system

Sleep spindles are transient waveforms found in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Sleep spindles are used for the classification of sleep stages and have been studied in the context of various psychiatric and neurological disorders, such as Alzheimer's disease (AD) and the so-called Mild Cognitive Impairment (MCI), which is considered to be a transitional stage between normal aging and dementia. The visual processing of whole-night sleep EEG recordings is tedious. Therefore, various techniques have been proposed for automatically detecting sleep spindles. In the present work an automatic sleep spindle detection system, that has been previously proposed, using a Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN), is evaluated in detecting spindles of both healthy controls, as well as MCI and AD patients. An investigation is carried also concerning the visual detection process, taking into consideration the feedback information provided by the automatic detection system. Results indicate that the sensitivity of the detector was 81.4%, 62.2%, and 83.3% and the false positive rate was 34%, 11.5%, and 33.3%, for the control, MCI, and AD groups, respectively. The visual detection process had a sensitivity rate ranging from 46.5% to 60% and a false positive rate ranging from 4.8% to 19.2%.

DiBa: a data-driven Bayesian algorithm for sleep spindle detection

Although the spontaneous brain rhythms of sleep have commanded much recent interest, their detection and analysis remains suboptimal. In this paper, we develop a data-driven Bayesian algorithm for sleep spindle detection on the electroencephalography (EEG). The algorithm exploits the Karhunen-Loève transform and Bayesian hypothesis testing to produce the instantaneous probability of a spindle's presence with maximal resolution. In addition to possessing flexibility, transparency, and scalability, this algorithm could perform at levels superior to standard methods for EEG event detection.

Validating an automated sleep spindle detection algorithm using an individualized approach

The goal of the current investigation was to develop a systematic method to validate the accuracy of an automated method of sleep spindle detection that takes into consideration individual differences in spindle amplitude. The benchmarking approach used here could be employed more generally to validate automated spindle scoring from other detection algorithms. In a sample of Stage 2 sleep from 10 healthy young subjects, spindles were identified both manually and automatically. The minimum amplitude threshold used by the Prana (PhiTools, Strasbourg, France) software spindle detection algorithm to identify a spindle was subject-specific and determined based upon each subject's mean peak spindle amplitude. Overall sensitivity and specificity values were 98.96 and 88.49%, respectively, when compared to manual scoring. Selecting individual amplitude thresholds for spindle detection based on systematic benchmarking data may validate automated spindle detection methods and improve reproducibility of experimental results. Given that interindividual differences are accounted for, we feel that automatic spindle detection provides an accurate and efficient alternative approach for detecting sleep spindles.

Non-linear recurrence analysis of NREM human sleep microstructure discloses deterministic oscillation patterns related to sleep stage transitions and sleep maintenance

Sleep is a dynamic process aimed at obtaining the required neurophysiological states at certain times, according to circadian and homeostatic needs and despite external or internal interfering stimuli. In this context, peculiar transient synchronized EEG patterns (TSEP) are supposed to play the main role in the building up of EEG synchronization and in the flexible adaptation against perturbations Our study aimed at disclosing and quantifying attractor driven, hidden periodicity or, conversely, chaotic oscillation patterns in the series of these TSEP related to sleep stage transitions and sleep maintenance. At first we devised a multistep algorithm, able to capture TSEP from EEG during sleep in 10 healthy volunteers. The time series of TSEP were then analyzed according to the Recurrence Plot (RP). TSEP series showed to form a pseudo-periodic series which becomes progressively denser and more stable until steady slow wave NREM sleep is reached, but looses stability just before REM sleep starts. This suggests that deterministic oscillatory patterns maybe adequate descriptors of the balance between homeostatic needs for NREM sleep and REM sleep pressure, supported by different cortical neuronal populations interactions.

Application of paraconsistent artificial neural networks as a method of aid in the diagnosis of Alzheimer disease

The visual analysis of EEG has shown useful in helping the diagnosis of Alzheimer disease (AD) when the diagnosis remains uncertain, being used in some clinical protocols. However, such analysis is subject to the inherent equipment imprecision, patient movement, electrical records, and physician interpretation of the visual analysis variation. The Artificial Neural Network (ANN) could be a helpful tool, appropriate to address problems such as prediction and pattern recognition. In this work, it has use a new class of ANN, the Paraconsistent Artificial Neural Network (PANN), which is capable of handling uncertain, inconsistent, and paracomplete information, for recognizing predetermined patterns of EEG and to assess its value as a possible auxiliary method for AD diagnosis. Thirty three patients with Alzheimer's disease and 34 controls patients of EEG records were obtained during relaxed wakefulness. It was considered as normal patient pattern, the background EEG activity between 8.0 and 12.0 Hz (with an average frequency of 10 Hz), allowing a range of 0.5 Hz. The PANN was able to recognize waves that belonging to their respective bands of clinical use (theta, delta, alpha, and beta), leading to an agreement with the clinical diagnosis at 82% of sensitivity and at 61% of specificity. Supported with these results, the PANN could be a promising tool to manipulate EEG analysis, bearing in mind the following considerations: the growing interest of specialists in EEG analysis visual and the ability of the PANN to deal directly imprecise, inconsistent and paracomplete data, providing an interesting quantitative and qualitative analysis.

Detection of signs of brain dysfunction in epileptic children by recognition of transient changes in the correlation of seizure-free EEG

Seizure-free EEG signals recorded from epileptic children were compared with EEG signals recorded from normal children. The comparison was based on the detection of transient events characterized by decrease in the correlation between different traces. For this purpose, a conceptually and mathematically simple method was applied. Two clear and remarkable phenomena, able to quantitatively discriminate between the two groups of subjects, were evidenced, with high statistical significance. In fact, it was observed that: (a) The number of events for the epileptic group was larger; (b) Applying restrictive criteria for event definition, the number of subjects in the epileptic group presenting events was larger. The results support the hypothesis of a decrease in brain correlation in children with epilepsy under treatment. This confirms the efficacy of the EEG signal in evaluating cortical functional differences not visible by visual inspection, independently of the cause (epilepsy or drugs), and demonstrate the specific effectiveness of the analysis method applied.

Paraconsistent artificial neural networks and Alzheimer disease: a preliminary study

EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis.

Development and comparison of four sleep spindle detection methods

OBJECTIVE

The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles.

METHODS AND MATERIALS

All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored bilateral spindles occurring in frontopolar or central brain location. In the present work a new sigma index for spindle detection was developed, based on the fast Fourier transform (FFT) spectrum, aiming at approximating our previous fuzzy spindle detector. The sigma index was complemented with spindle amplitude analysis, based on finite impulse response (FIR) filtering, to form of a combination detector of bilateral spindles. In this combination detector, the spindle amplitude distribution of each recording was estimated and used to tune two different amplitude thresholds. This combination detector was compared to bilaterally extracted sigma indexes and fuzzy detections, which aim to be independent of absolute spindle amplitudes. As a fourth method a fixed spindle amplitude detector was included.

RESULTS

The combination detector provided the best overall performance; in S2 sleep a 70% true positive rate was reached with a specificity of 98.6%, and a false-positive rate of 32%. The bilateral sigma indexes provided the second best results, followed by fuzzy detector, while the fixed amplitude detector provided the poorest results so that in S2 sleep a 70% true positive rate was reached with a specificity of 97.7% and false-positive rate of 46%. The spindle amplitude distributions automatically determined for each recording by the combination detector were compared to amplitudes of visually scored spindles and they proved to correspond well. Inter-hemispheric amplitude variation of visually scored bilateral spindles is also presented.

CONCLUSION

Flexibility is beneficial in the detection of bilateral spindles. The present work advances automated spindle detection and increases the knowledge of bilateral sleep spindle characteristics.