Sleep spindles are discrete, intermittent patterns of brain activity observed in human electroencephalographic data. Increasingly, these oscillations are of biological and clinical interest because ...of their role in development, learning and neurological disorders. We used an Internet interface to crowdsource spindle identification by human experts and non-experts, and we compared their performance with that of automated detection algorithms in data from middle- to older-aged subjects from the general population. We also refined methods for forming group consensus and evaluating the performance of event detectors in physiological data such as electroencephalographic recordings from polysomnography. Compared to the expert group consensus gold standard, the highest performance was by individual experts and the non-expert group consensus, followed by automated spindle detectors. This analysis showed that crowdsourcing the scoring of sleep data is an efficient method to collect large data sets, even for difficult tasks such as spindle identification. Further refinements to spindle detection algorithms are needed for middle- to older-aged subjects.
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used ...neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.
Background Genome-wide scans have uncovered rare copy number variants conferring high risk of psychiatric disorders. The 15q13.3 microdeletion is associated with a considerably increased risk of ...idiopathic generalized epilepsy, intellectual disability, and schizophrenia. Methods A 15q13.3 microdeletion mouse model ( Dfh15q13/+ ) was generated by hemizygous deletion of the orthologous region and characterized with focus on schizophrenia- and epilepsy-relevant parameters. Results Df(h15q13)/+ mice showed marked changes in neuronal excitability in acute seizure assays, with increased propensity to develop myoclonic and absence-like seizures but decreased propensity for clonic and tonic seizures. Furthermore, they had impaired long-term spatial reference memory and a decreased theta frequency in hippocampus and prefrontal cortex. Electroencephalogram characterization revealed auditory processing deficits similar to those observed in schizophrenia. Gamma band power was increased during active state, but evoked gamma power following auditory stimulus (40 Hz) was dramatically reduced, mirroring observations in patients with schizophrenia. In addition, Df(h15q13)/+ mice showed schizophrenia-like decreases in amplitudes of auditory evoked potentials. Although displaying a grossly normal behavior, Df(h15q13)/+ mice are more aggressive following exposure to mild stressors, similar to what is described in human deletion carriers. Furthermore, Df(h15q13)/+ mice have increased body weight, and a similar increase in body weight was subsequently found in a sample of human subjects with 15q13.3 deletion. Conclusions The Df(h15q13)/+ mouse shows similarities to several alterations related to the 15q13.3 microdeletion syndrome, epilepsy, and schizophrenia, offering a novel tool for addressing the underlying biology of these diseases.
We have witnessed a rapid development of brain-computer interfaces (BCIs) linking the brain to external devices. BCIs can be utilized to treat neurological conditions and even to augment brain ...functions. BCIs offer a promising treatment for mental disorders, including disorders of attention. Here we review the current state of the art and challenges of attention-based BCIs, with a focus on visual attention. Attention-based BCIs utilize electroencephalograms (EEGs) or other recording techniques to generate neurofeedback, which patients use to improve their attention, a complex cognitive function. Although progress has been made in the studies of neural mechanisms of attention, extraction of attention-related neural signals needed for BCI operations is a difficult problem. To attain good BCI performance, it is important to select the features of neural activity that represent attentional signals. BCI decoding of attention-related activity may be hindered by the presence of different neural signals. Therefore, BCI accuracy can be improved by signal processing algorithms that dissociate signals of interest from irrelevant activities. Notwithstanding recent progress, optimal processing of attentional neural signals remains a fundamental challenge for the development of efficient therapies for disorders of attention.
Patients are not able to call for help during a generalized tonic-clonic epileptic seizure. Our objective was to develop a robust generic algorithm for automatic detection of tonic-clonic seizures, ...based on surface electromyography (sEMG) signals suitable for a portable device. Twenty-two seizures were analyzed from 11 consecutive patients. Our method is based on a high-pass filtering with a cutoff at 150 Hz, and monitoring a count of zero crossings with a hysteresis of ±50 μV . Based on data from one sEMG electrode (on the deltoid muscle), we achieved a sensitivity of 100% with a mean detection latency of 13.7 s, while the rate of false detection was limited to 1 false alarm per 24 h. The overall performance of the presented generic algorithm is adequate for clinical implementation.
Wearable wireless electrocardiographic (ECG) monitoring is well-proven for arrythmia detection, but ischemia detection accuracy is not well-described. We aimed to assess the agreement of ST-segment ...deviation from single- versus 12-lead ECG and their accuracy for the detection of reversible ischemia. Bias and limits of agreement (LoA) were calculated between maximum deviations in ST segments from single- and 12-lead ECG during
Rb PET-myocardial cardiac stress scintigraphy. Sensitivity and specificity for reversible anterior-lateral myocardial ischemia detection were assessed for both ECG methods, using perfusion imaging results as a reference. Out of 110 patients included, 93 were analyzed. The maximum difference between single- and 12-lead ECG was seen in II (-0.019 mV). The widest LoA was seen in V5, with an upper LoA of 0.145 mV (0.118 to 0.172) and a lower LoA of -0.155 mV (-0.182 to -0.128). Ischemia was seen in 24 patients. Single-lead and 12-lead ECG both had poor accuracy for the detection of reversible anterolateral ischemia during the test: single-lead ECG had a sensitivity of 8.3% (1.0-27.0%) and specificity of 89.9% (80.2-95.8%), and 12-lead ECG a sensitivity of 12.5% (3.0-34.4%) and a specificity of 91.3% (82.0-96.7%). In conclusion, agreement was within predefined acceptable criteria for ST deviations, and both methods had high specificity but poor sensitivity for the detection of anterolateral reversible ischemia. Additional studies must confirm these results and their clinical relevance, especially in the light of the poor sensitivity for detecting reversible anterolateral cardiac ischemia.
Abstract
Study Objectives
Patients diagnosed with isolated rapid eye movement (REM) sleep behavior disorder (iRBD) and Parkinson’s disease (PD) have altered sleep stability reflecting ...neurodegeneration in brainstem structures. We hypothesize that neurodegeneration alters the expression of cortical arousals in sleep.
Methods
We analyzed polysomnography data recorded from 88 healthy controls (HC), 22 iRBD patients, 82 de novo PD patients without RBD, and 32 with RBD (PD + RBD). These patients were also investigated at a 2-year follow-up. Arousals were analyzed using a previously validated automatic system, which used a central electroencephalography lead, electrooculography, and chin electromyography. Multiple linear regression models were fitted to compare group differences at baseline and change to follow-up for arousal index (ArI), shifts in electroencephalographic signals associated with arousals, and arousal chin muscle tone. The regression models were adjusted for known covariates affecting the nature of arousal.
Results
In comparison to HC, patients with iRBD and PD + RBD showed increased ArI during REM sleep and their arousals showed a significantly lower shift in α-band power at arousals and a higher muscle tone during arousals. In comparison to HC, the PD patients were characterized by a decreased ArI in non-REM (NREM) sleep at baseline. ArI during NREM sleep decreased further at the 2-year follow-up, although not significantly.
Conclusions
Patients with PD and iRBD present with abnormal arousal characteristics as scored by an automated method. These abnormalities are likely to be caused by neurodegeneration of the reticular activation system due to alpha-synuclein aggregation.
Abstract
Study Objectives
Hypocretin deficient narcolepsy (type 1, NT1) presents with multiple sleep abnormalities including sleep-onset rapid eye movement (REM) periods (SOREMPs) and sleep ...fragmentation. We hypothesized that cortical arousals, as scored by an automatic detector, are elevated in NT1 and narcolepsy type 2 (NT2) patients as compared to control subjects.
Methods
We analyzed nocturnal polysomnography (PSG) recordings from 25 NT1 patients, 20 NT2 patients, 18 clinical control subjects (CC, suspected central hypersomnia but with normal cerebrospinal (CSF) fluid hypocretin-1 (hcrt-1) levels and normal results on the multiple sleep latency test), and 37 healthy control (HC) subjects. Arousals were automatically scored using Multimodal Arousal Detector (MAD), a previously validated automatic wakefulness and arousal detector. Multiple linear regressions were used to compare arousal index (ArI) distributions across groups. Comparisons were corrected for age, sex, body-mass index, medication, apnea-hypopnea index, periodic leg movement index, and comorbid rapid eye movement sleep behavior disorder.
Results
NT1 was associated with an average increase in ArI of 4.02 events/h (p = 0.0246) compared to HC and CC, while no difference was found between NT2 and control groups. Additionally, a low CSF hcrt-1 level was predictive of increased ArI in all the CC, NT2, and NT1 groups.
Conclusions
The results further support the hypothesis that a loss of hypocretin neurons causes fragmented sleep, which can be measured as an increased ArI as scored by the MAD.
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