Real-time stress detection based on artificial intelligence for people with an intellectual disability.

People with severe intellectual disabilities (ID) could have difficulty expressing their stress which may complicate timely responses from caregivers. The present study proposes an automatic stress detection system that can work in real-time. The system uses wearable sensors that record physiological signals in combination with machine learning to detect physiological changes related to stress. Four experiments were conducted to assess if the system could detect stress in people with and without ID. Three experiments were conducted with people without ID (n = 14, n = 18, and n = 48), and one observational study was done with people with ID (n = 12). To analyze if the system could detect stress, the performance of random, general, and personalized models was evaluated. The mixed ANOVA found a significant effect for model type, F(2, 134) = 116.50, p < .001. Additionally, the post-hoc t-tests found that the personalized model for the group with ID performed better than the random model, t(11) = 9.05, p < .001. The findings suggest that the personalized model can detect stress in people with and without ID. A larger-scale study is required to validate the system for people with ID.

Multimodal nocturnal seizure detection: Do we need to adapt algorithms for children?

OBJECTIVE: To assess the performance of a multimodal seizure detection device, first tested in adults (sensitivity 86%, PPV 49%), in a pediatric cohort living at home or residential care. METHODS: In this multicenter, prospective, video-controlled cohort-study, nocturnal seizures were detected by heartrate and movement changes in children with epilepsy and intellectual disability. Participants with a history of >1 monthly major motor seizure wore Nightwatch bracelet at night for 3 months. Major seizures were defined as tonic-clonic, generalized tonic >30 s, hyperkinetic, or clusters (>30 min) of short myoclonic or tonic seizures. The video of all events (alarms and nurse diaries) and about 10% of whole nights were reviewed to classify major seizures, and minor or no seizures. RESULTS: Twenty-three participants with focal or generalized epilepsy and nightly motor seizures were evaluated during 1511 nights, with 1710 major seizures. First 1014 nights, 4189 alarms occurred with average of 1.44/h, showing average sensitivity of 79.9% (median 75.4%) with mean PPV of 26.7% (median 11.1%) and false alarm rate of 0.2/hour. Over 90% of false alarms in children was due to heart rate (HR) part of the detection algorithm. To improve this rate, an adaptation was made such that the alarm was only triggered when the wearer was in horizontal position. For the remaining 497 nights, this was tested prospectively, 384 major seizures occurred. This resulted in mean PPV of 55.5% (median 58.1%) and a false alarm rate 0.08/h while maintaining a comparable mean sensitivity of 79.4% (median 93.2%). SIGNIFICANCE: Seizure detection devices that are used in bed which depend on heartrate and movement show similar sensitivity in children and adults. However, children do show general higher false alarm rate, mostly triggered while awake. By correcting for body position, the false alarms can be limited to a level that comes close to that in adults.

Multimodal nocturnal seizure detection in a residential care setting: A long-term prospective trial.

OBJECTIVE: To develop and prospectively evaluate a method of epileptic seizure detection combining heart rate and movement. METHODS: In this multicenter, in-home, prospective, video-controlled cohort study, nocturnal seizures were detected by heart rate (photoplethysmography) or movement (3-D accelerometry) in persons with epilepsy and intellectual disability. Participants with >1 monthly major seizure wore a bracelet (Nightwatch) on the upper arm at night for 2 to 3 months. Major seizures were tonic-clonic, generalized tonic >30 seconds, hyperkinetic, or others, including clusters (>30 minutes) of short myoclonic/tonic seizures. The video of all events (alarms, nurse diaries) and 10% completely screened nights were reviewed to classify major (needing an alarm), minor (needing no alarm), or no seizure. Reliability was tested by interobserver agreement. We determined device performance, compared it to a bed sensor (Emfit), and evaluated the caregivers' user experience. RESULTS: Twenty-eight of 34 admitted participants (1,826 nights, 809 major seizures) completed the study. Interobserver agreement (major/no major seizures) was 0.77 (95% confidence interval [CI] 0.65-0.89). Median sensitivity per participant amounted to 86% (95% CI 77%-93%); the false-negative alarm rate was 0.03 per night (95% CI 0.01-0.05); and the positive predictive value was 49% (95% CI 33%-64%). The multimodal sensor showed a better sensitivity than the bed sensor (n = 14, median difference 58%, 95% CI 39%-80%, p < 0.001). The caregivers' questionnaire (n = 33) indicated good sensor acceptance and usability according to 28 and 27 participants, respectively. CONCLUSION: Combining heart rate and movement resulted in reliable detection of a broad range of nocturnal seizures.

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 subject's 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.

Autonomic nervous system functioning associated with psychogenic nonepileptic seizures: Analysis of heart rate variability.

OBJECTIVE: Psychogenic nonepileptic seizures (PNESs) resemble epileptic seizures but originate from psychogenic rather than organic causes. Patients with PNESs are often unable or unwilling to reflect on underlying emotions. To gain more insight into the internal states of patients during PNES episodes, this study explored the time course of heart rate variability (HRV) measures, which provide information about autonomic nervous system functioning and arousal. METHODS: Heart rate variability measures were extracted from double-lead electrocardiography data collected during 1-7days of video-electroencephalography monitoring of 20 patients with PNESs, in whom a total number of 118 PNESs was recorded. Heart rate (HR) and HRV measures in time and frequency domains (standard deviation of average beat-to-beat intervals (SDANN), root mean square of successive differences (RMSSD), high-frequency (HF) power, low-frequency (LF) power, and very low-frequency (VLF) power) were averaged over consecutive five-minute intervals. Additionally, quantitative analyses of Poincaré plot parameters (SD1, SD2, and SD1/SD2 ratio) were performed. RESULTS: In the five-minute interval before PNES, HR significantly (p<0.05) increased (d=2.5), whereas SDANN (d=-0.03) and VLF power (d=-0.05) significantly decreased. During PNES, significant increases in HF power (d=0.0006), SD1 (d=0.031), and SD2 (d=0.016) were observed. In the five-minute interval immediately following PNES, SDANN (d=0.046) and VLF power (d=0.073) significantly increased, and HR (d=-5.1) and SD1/SD2 ratio (d=-0.14) decreased, compared to the interval preceding PNES. CONCLUSION: The results suggest that PNES episodes are preceded by increased sympathetic functioning, which is followed by an increase in parasympathetic functioning during and after PNES. Future research needs to identify the exact nature of the increased arousal that precedes PNES.

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.

Resting-state networks and dissociation in psychogenic non-epileptic seizures.

OBJECTIVE: Psychogenic non-epileptic seizures (PNES) are epilepsy-like episodes which have an emotional rather than organic origin. Although PNES have often been related to the process of dissociation, the psychopathology is still poorly understood. To elucidate underlying mechanisms, the current study applied independent component analysis (ICA) on resting-state fMRI to investigate alterations within four relevant networks, associated with executive, fronto-parietal, sensorimotor, and default mode activation, and within a visual network to examine specificity of between-group differences. METHODS: Twenty-one patients with PNES without psychiatric or neurologic comorbidities and twenty-seven healthy controls underwent resting-state functional MR imaging at 3.0T (Philips Achieva). Additional neuropsychological testing included Raven's Matrices test and dissociation questionnaires. ICA with dual regression was used to identify resting-state networks in all participants, and spatial maps of the networks of interest were compared between patients and healthy controls. RESULTS: Patients displayed higher dissociation scores, lower cognitive performance and increased contribution of the orbitofrontal, insular and subcallosal cortex in the fronto-parietal network; the cingulate and insular cortex in the executive control network; the cingulate gyrus, superior parietal lobe, pre- and postcentral gyri and supplemental motor cortex in the sensorimotor network; and the precuneus and (para-) cingulate gyri in the default-mode network. The connectivity strengths within these regions of interest significantly correlated with dissociation scores. No between-group differences were found within the visual network, which was examined to determine specificity of between-group differences. CONCLUSIONS: PNES patients displayed abnormalities in several resting-state networks that provide neuronal correlates for an underlying dissociation mechanism.

Neurophysiological correlates of dissociative symptoms.

OBJECTIVE: Dissociation is a mental process with psychological and somatoform manifestations, which is closely related to hypnotic suggestibility and essentially shows the ability to obtain distance from reality. An increased tendency to dissociate is a frequently reported characteristic of patients with functional neurological symptoms and syndromes (FNSS), which account for a substantial part of all neurological admissions. This review aims to investigate what heart rate variability (HRV), EEG and neuroimaging data (MRI) reveal about the nature of dissociation and related conditions. METHODS: Studies reporting HRV, EEG and neuroimaging data related to hypnosis, dissociation and FNSS were identified by searching the electronic databases Pubmed and ScienceDirect. RESULTS: The majority of the identified studies concerned the physiological characteristics of hypnosis; relatively few investigations on dissociation related FNSS were identified. General findings were increased parasympathetic functioning during hypnosis (as measured by HRV), and lower HRV in patients with FNSS. The large variety of EEG and functional MRI investigations with diverse results challenges definite conclusions, but evidence suggests that subcortical as well as (pre)frontal regions serve emotion regulation in dissociative conditions. Functional connectivity analyses suggest the presence of altered brain networks in patients with FNSS, in which limbic areas have an increased influence on motor preparatory regions. CONCLUSIONS: HRV, EEG and (functional) MRI are sensitive methods to detect physiological changes related to dissociation and dissociative disorders such as FNSS, and can possibly provide more information about their aetiology. The use of such measures could eventually provide biomarkers for earlier identification of patients at risk and appropriate treatment of dissociative conditions.

EEG source localization in full-term newborns with hypoxic-ischemia.

The aim of this study was to evaluate EEG source localization by standardized weighted low-resolution brain electromagnetic tomography (swLORETA) for monitoring of fullterm newborns with hypoxic-ischemic encephalopathy, using a standard anatomic head model. Three representative examples of neonatal hypoxic-ischemia were included. The method was validated with MRI data. Hypoxic-ischemic areas, visible on MRI, correlated well with swLORETA current density distributions. In addition, neonatal seizure activity may be localized. The calculated current density distributions provide easy-to-interpret localized information about neonatal brain function, which may enable detailed longitudinal monitoring and potential assessment of treatment efficacy.

Subjectively impaired bed mobility in Parkinson disease affects sleep efficiency.

BACKGROUND: Impaired bed mobility (IBM) may be an important reason for the high prevalence of sleep insomnia in Parkinson disease (PD). Here we assessed the influence of subjectively IBM on both subjective and objective sleep parameters in insomnia PD patients with (PD+IBM) and without (PD-IBM) concerns of IBM and controls with primary insomnia. METHODS: We included 44 PD patients with sleep initiation or maintenance concerns and 44 control subjects with primary insomnia. Sleep questionnaires, polysomnographic sleep parameters, activity data, and the number of body position changes were compared between PD patients and controls as well as within the PD group between PD+IBM vs PD-IBM subjects. RESULTS: There were 54.5% of PD subjects who reported having IBM. In the PD+IBM group, the number of body position changes was significantly lower than in PD-IBM (0.4/h [0.0-1.8] vs 1.4/h [0.0-4.6], P=.015). Sleep efficiency (SE) was lower in PD+IBM patients (63.5; 26.2-85.6) compared to PD-IBM patients (78.4; 54.8-92.6; P<.001). CONCLUSION: PD patients who report IBM have fewer sleep-related body position changes (i.e., nocturnal hypokinesia) than PD patients without such concerns. Furthermore, objective SE is significantly diminished in these patients.

Sympathetic activity and hypothalamo-pituitary-adrenal axis activity during sleep in post-traumatic stress disorder: a study assessing polysomnography with simultaneous blood sampling.

BACKGROUND: Nightmares and insomnia in PTSD are hallmark symptoms, yet poorly understood in comparison to the advances toward a biological framework for the disorder. According to polysomnography (PSG), only minor changes in sleep architecture were described. This warrants alternative methods for assessing sleep regulation in PTSD. METHODS: After screening for obstructive sleep apnea and period limb movement disorder, veterans with PTSD (n=13), trauma controls (TCs, n=17) and healthy controls (HCs, n=15) slept in our sleep laboratory on two consecutive nights with an IV catheter out of which blood was sampled every 20min from 22:00h to 08:00h. Nocturnal levels of plasma adrenocorticotropic hormone (ACTH), cortisol, melatonin were assessed in conjunction with PSG registration, as well as subjective sleep parameters. RESULTS: PTSD patients showed a significant increase in awakenings during sleep in comparison to both control groups. These awakenings were correlated with ACTH levels during the night, and with the subjective perception of sleep depth. Also, heart rate (HR) was significantly increased in PTSD patients as compared with both control groups. The diurnal regulation of ACTH, cortisol and melatonin appeared undisturbed. PTSD patients exhibited lower cortisol levels at borderline significance (p=0.056) during the first half of the night. ACTH levels and cortisol levels during the first half of the night were inversely related to slow wave sleep (SWS). CONCLUSION: This study suggests that hypothalamo-pituitary-adrenal (HPA) axis activity is related to sleep fragmentation in PTSD. Also, activity of the sympathetic nervous system (SNS) is increased during sleep in PTSD. Further research is necessary to explore the potential causal relationship between sleep problems and the activity of the HPA-axis and SNS in PTSD.

Time-frequency analysis of accelerometry data for detection of myoclonic seizures.

Four time-frequency and time-scale methods are studied for their ability of detecting myoclonic seizures from accelerometric data. Methods that are used are: the short-time Fourier transform (STFT), the Wigner distribution (WD), the continuous wavelet transform (CWT) using a Daubechies wavelet, and a newly introduced model-based matched wavelet transform (MOD). Real patient data are analyzed using these four time-frequency and time-scale methods. To obtain quantitative results, all four methods are evaluated in a linear classification setup. Data from 15 patients are used for training and data from 21 patients for testing. Using features based on the CWT and MOD, the success rate of the classifier was 80%. Using STFT or WD-based features, the classification success is reduced. Analysis of the false positives revealed that they were either clonic seizures, the onset of tonic seizures, or sharp peaks in "normal" movements indicating that the patient was making a jerky movement. All these movements are considered clinically important to detect. Thus, the results show that both CWT and MOD are useful for the detection of myoclonic seizures. On top of that, MOD has the advantage that it consists of parameters that are related to seizure duration and intensity that are physiologically meaningful. Furthermore, in future work, the model can also be useful for the detection of other motor seizure types.

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.

Detection of subtle nocturnal motor activity from 3-D accelerometry recordings in epilepsy patients.

This paper presents a first step towards reliable detection of nocturnal epileptic seizures based on 3-D accelerometry (ACM) recordings. The main goal is to distinguish between data with and without subtle nocturnal motor activity, thus reducing the amount of data that needs further (more complex) analysis for seizure detection. From 15 ACM signals (measured on five positions on the body), two features are computed, the variance and the jerk. In the resulting 2-D feature space, a linear threshold function is used for classification. For training and testing, the algorithm ACM data along with video data is used from nocturnal registrations in seven mentally retarded patients with severe epilepsy. Per patient, the algorithm detected 100% of the periods of motor activity that are marked in video recordings and the ACM signals by experts. From all the detections, 43%-89% was correct (mean =65%). We were able to reduce the amount of data that need to be analyzed considerably. The results show that our approach can be used for detection of subtle nocturnal motor activity. Furthermore, our results indicate that our algorithm is robust for fluctuations across patients. Consequently, there is no need for training the algorithm for each new patient.

Separation of spatially localized cortical activities in the EEG: model-based analysis and optimization.

The mapping of brain sources into the scalp electroencephalogram (EEG) depends on volume conduction properties of the head and on an electrode montage involving a reference. In this article, the source mapping (SM) is formalized mathematically in the form of an observation function (OF) matrix. The OF-matrix is used to analyze and optimize the SM for a generation model for the desynchronized spontaneous EEG. The optimization leads to a novel reference that minimizes the impact in the EEG of the sources located distant from the electrodes. Thereby, this reference separates spatially localized cortical activities in the EEG. For this reason, it is called the localized reference (LR). The LR is compared with the Hjorth Laplacian reference (HR), which is commonly used for recordings of localized cortical activities. The comparison is made in terms of the relative power contribution of the sources into EEG channels. For the model, the LR is found to have up to 15-20% better performance than the HR, and thus the LR is considered a good alternative to the HR when a head model is available. The HR is, however, a fair approximation of the LR and thus is close to optimum for practical intents and purposes.

Model for arm movements during myoclonic seizures.

A model is formulated for arm movements during myoclonic (epileptic) seizures. The system described in the model, consists of a mechanical and an electrophysiological part. The model output is compared to real patient accelerometry (ACM)-data from six epilepsy patients. Eight out of ten myoclonic seizures have a good fit to the model. The values of the model parameters tuned to the real seizures are physiologically feasible. Using mean parameter values leads to agreeable fits in six out of ten myoclonic seizures. Two of the four parameters seem to be robust for variation in patient and seizure. The presented model approach leads to a better understanding of patterns in ACM-recordings that are associated with myoclonic seizures and in the future can contribute to automated detection of these patterns.

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.

The potential value of three-dimensional accelerometry for detection of motor seizures in severe epilepsy.

Seizure detection results based on the visual analysis of three-dimensional (3D) accelerometry (ACM) and video/EEG recordings are reported for 18 patients with severe epilepsy. They were monitored for 36 hours during which 897 seizures were detected. This was seven times higher than the number of seizures reported by nurses during the registration period. The results in this article demonstrate that 3D ACM is a valuable sensing method for seizure detection in this population. Four hundred twenty-eight (48%) seizures were detected by ACM. With 3D ACM alone it was possible to detect all the seizures in 10 of the 18 patients. Three-dimensional ACM also was complementary to EEG in our population. ACM patterns during seizures were stereotypical in 95% of the motor seizures. These characteristic patterns are a starting point for automated seizure detection.