Jbam Johan Arends

An investigation of the phase locking index for measuring of interdependency of cortical source signals recorded in the EEG

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this article, we investigate the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). To this end, we consider simple analytical models for the mapping of simulated CSSs into the EEG. For these models, the PLI is investigated analytically and through numerical simulations. An evaluation is made of the sensitivity of the PLI to the amount of crosstalk between the sources through biological tissues of the head. It is found that the PLI is a useful interdependency measure for CSSs, especially when the amount of crosstalk is small. Another common interdependency measure is the coherence. A direct comparison of both measures has not been made in the literature so far. We assess the performance of the PLI and coherence for estimation and detection purposes based on, respectively, a normalized variance and a novel statistical measure termed contrast. Based on these performance measures, it is found that the PLI is similar or better than the CM in most cases. This result is also confirmed through analysis of EEGs recorded from epileptic patients.

Automated detection of tonic seizures using 3-D accelerometry

A first approach is presented for the detection of accelerometry (ACM) patterns associated with tonic seizures. First it is shown that during tonic seizures the typical ACM-pattern is mainly caused by change of position towards the field of gravity and that the acceleration caused by movement is negligible. To this end a mechanical model of the arm and physiological information about muscle contraction during tonic seizures are used. Then six features are computed that represent the main characteristics of ACM-patterns associated with tonic seizures. Linear discriminant analysis is used for classification. For training and evaluation ACM-data are used from mentally retarded patients with severe epilepsy. It was possible to detect tonic seizures with a success rate around 0.80 and with a positive predictive value (PPV) of 0.35. For off-line analysis this is acceptable, especially when 42 % of the false alarms are actually motor seizures of another type. The missed seizures, were not clearly visible in the ACM-signal. For these seizures additional ACM-sensors or a combination with other sensor types might be necessary. The results show that our approach is useful for the automated detection of tonic seizures and that it is a promising contribution in a complete multi-sensor seizure detection setup.

Time delay between cardiac and brain activity during sleep transitions

Human sleep consists of wake, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep that includes light and deep sleep stages. This work investigated the time delay between changes of cardiac and brain activity for sleep transitions. Here, the brain activity was quantified by electroencephalographic (EEG) mean frequency and the cardiac parameters included heart rate, standard deviation of heartbeat intervals, and their low- and high-frequency spectral powers. Using a cross-correlation analysis, we found that the cardiac variations during wake-sleep and NREM sleep transitions preceded the EEG changes by 1–3 min but this was not the case for REM sleep transitions. These important findings can be further used to predict the onset and ending of some sleep stages in an early manner.

Actigraphy as a diagnostic aid for REM sleep behavior disorder in Parkinson's disease

BackgroundRapid eye movement (REM) sleep behavior disorder (RBD) is a common parasomnia in Parkinson’s disease (PD) patients. The current International Classification of Sleep Disorders (ICSD-II) requires a clinical interview combined with video polysomnography (video-PSG) to diagnose. The latter is time consuming and expensive and not always feasible in clinical practice. Here we studied the use of actigraphy as a diagnostic tool for RBD in PD patients.MethodsWe studied 45 consecutive PD patients (66.7% men) with and without complaints of RBD. All patients underwent one night of video-PSG and eight consecutive nights of actigraphy. Based on previous studies, the main outcome measure was the total number of bouts classified as “wake”, compared between patients with (PD + RBD) and without RBD (PD- RBD).Results23 (51.1%) patients had RBD according to the ICSD-II criteria. The total number of wake bouts was significantly higher in RBD patients (PD + RBD 73.2 ± 40.2 vs. PD-RBD 48.4 ± 23.3, p = .016). A cut off of 95 wake bouts per night resulted in a specificity of 95.5%, a sensitivity of 20.1% and a positive predictive value of 85.7%. Seven patients were suspected of RBD based on the interview alone, but not confirmed on PSG; six of whom scored below 95 wake bouts per night on actigraphy.ConclusionPD patients with RBD showed a significantly higher number of bouts scored as “wake” using actigraphy, compared to patients without RBD. In clinical practice, actigraphy has a high specificity, but low sensitivity in the diagnosis of RBD. The combination of actigraphy and previously reported RBD questionnaires may be a promising method to diagnose RBD in patients with PD.

Epileptic seizure detection on patients with mental retardation based on EEG features : a pilot study

Mental retardation (MR) is one of the most common secondary disabilities in people with Epilepsy. However, to our knowledge there are no reliable seizure detection methods specified for MR-patients. In this paper we performed a pilot study on a group of six patients with mental retardation to assess what EEG features potentially work well on this group. A group of EEG features on the time, frequency and spatio-temporal domain were extracted, the modified wrapper approach was then employed as an improved feature subset selection method. Results show high variance on obtained features subset across this group, meanwhile there exist some common features which characterize the high-frequency components of epileptic EEG signals.

Seizure detection using dynamic warping for patients with intellectual disability

Electroencephalography (EEG) is paramount for both retrospective analysis and real-time monitoring of epileptic seizures. Studies have shown that EEG-based seizure detection is very difficult for a specific epileptic population with intellectual disability due to the cerebral development disorders. In this work, a seizure detection method based on dynamic warping (DW) is proposed for patients with intellectual disability. It uses an EEG template of an individual subjects dominant seizure type, to extract the morphological features from EEG signals. A linear discriminant analysis (LDA) classifier is used to perform the seizure detection. Results show that the DW-based feature in the frequency domain is superior than that in the time domain, and the features extracted using wavelet transform method.

Analysis of the Phase Locking Index for Measuring of Interdependency of Cortical Signals Recorded in the EEG

The phase locking index (PLI) was introduced to quantify in a statistical sense the phase synchronization of two signals. It has been commonly used to process biosignals. In this paper, we analyze the PLI for measuring the interdependency of cortical source signals (CSSs) recorded in the Electroencephalogram (EEG). The main focus of the analysis is the probability density function, which describes the sensitivity of the PLI to the joint noise ensemble in the CSSs. Since this function is mathematically intractable, we derive approximations and analyze them for a simple analytical model of the CSS mixture in the EEG. The accuracies of the approximate probability density functions (APDFs) are evaluated using simulations for the model. The APDFs are found sufficiently accurate and thus are applicable for practical intents and purposes. They can hence be used to determine the confidence intervals and significance levels for detection methods for interdependencies, e.g., between cortical signals recorded in the EEG.