Multimodal nocturnal seizure detection in children with epilepsy:A prospective, multicenter, long-term, in-home trial

Objective: There is a pressing need for reliable automated seizure detection in epilepsy care. Performance evidence on ambulatory non-electroencephalography-based seizure detection devices is low, and evidence on their effect on caregiver's stress, sleep, and quality of life (QoL) is still lacking. We aimed to determine the performance of NightWatch, a wearable nocturnal seizure detection device, in children with epilepsy in the family home setting and to assess its impact on caregiver burden. Methods: We conducted a phase 4, multicenter, prospective, video-controlled, in-home NightWatch implementation study (NCT03909984). We included children aged 4–16 years, with ≥1 weekly nocturnal major motor seizure, living at home. We compared a 2-month baseline period with a 2-month NightWatch intervention. The primary outcome was the detection performance of NightWatch for major motor seizures (focal to bilateral or generalized tonic–clonic [TC] seizures, focal to bilateral or generalized tonic seizures lasting &gt;30 s, hyperkinetic seizures, and a remainder category of focal to bilateral or generalized clonic seizures and "TC-like" seizures). Secondary outcomes included caregivers' stress (Caregiver Strain Index [CSI]), sleep (Pittsburgh Quality of Sleep Index), and QoL (EuroQol five-dimension five-level scale). Results: We included 53 children (55% male, mean age = 9.7 ± 3.6 years, 68% learning disability) and analyzed 2310 nights (28 173 h), including 552 major motor seizures. Nineteen participants did not experience any episode of interest during the trial. The median detection sensitivity per participant was 100% (range = 46%–100%), and the median individual false alarm rate was.04 per hour (range = 0–.53). Caregiver's stress decreased significantly (mean total CSI score = 8.0 vs. 7.1, p =.032), whereas caregiver's sleep and QoL did not change significantly during the trial. Significance: The NightWatch system demonstrated high sensitivity for detecting nocturnal major motor seizures in children in a family home setting and reduced caregiver stress.</p

The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing

BACKGROUND: Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals.METHODS: We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI.RESULTS: Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI).CONCLUSION: Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task.</p

Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder

Using brain activity directly as input for assistive tool control can circumventmuscular dysfunction and increase functional independence for physically impaired people. The motor cortex is commonly targeted for recordings, while growing evidence shows that there exists decodable movement-related neural activity outside of the motor cortex. Several decoding studies demonstrated significant decoding from distributed areas separately. Here, we combine information from all recorded non-motor brain areas and decode executed and imagined movements using a Riemannian decoder. We recorded neural activity from 8 epilepsy patients implanted with stereotactic-electroencephalographic electrodes (sEEG), while they performed an executed and imagined grasping tasks. Before decoding, we excluded all contacts in or adjacent to the central sulcus. The decoder extracts a low-dimensional representation of varying number of components, and classified move/no-move using a minimum-distance-to-geometric-mean Riemannian classifier. We show that executed and imagined movements can be decoded from distributed non-motor brain areas using a Riemannian decoder, reaching an area under the receiver operator characteristic of 0.83 ± 0.11. Furthermore, we highlight the distributedness of the movement-related neural activity, as no single brain area is the main driver of performance. Our decoding results demonstrate a first application of a Riemannian decoder on sEEG data and show that it is able to decode from distributed brain-wide recordings outside of the motor cortex. This brief report highlights the perspective to explore motor-related neural activity beyond the motor cortex, as many areas contain decodable information

Neurophysiological signatures reflect differences in visual attention during absence seizures

Objective: Absences affect visual attention and eye movements variably. Here, we explore whether the dissimilarity of these symptoms during absences is reflected in differences in electroencephalographic (EEG) features, functional connectivity, and activation of the frontal eye field. Methods: Pediatric patients with absences performed a computerized choice reaction time task, with simultaneous recording of EEG and eye-tracking. We quantified visual attention and eye movements with reaction times, response correctness, and EEG features. Finally, we studied brain networks involved in the generation and propagation of seizures. Results: Ten pediatric patients had absences during the measurement. Five patients had preserved eye movements (preserved group) and five patients showed disrupted eye movements (unpreserved group) during seizures. Source reconstruction showed a stronger involvement of the right frontal eye field during absences in the unpreserved group than in the preserved group (dipole fraction 1.02% and 0.34%, respectively, p &lt; 0.05). Graph analysis revealed different connection fractions of specific channels. Conclusions: The impairment of visual attention varies among patients with absences and is associated with differences in EEG features, network activation, and involvement of the right frontal eye field. Significance: Assessing the visual attention of patients with absences can be usefully employed in clinical practice for tailored advice to the individual patient.</p

Protocol of the SOMNIA project : an observational study to create a neurophysiological database for advanced clinical sleep monitoring

Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm

Validation and Modification of a Prediction Model for Acute Cardiac Events in Patients With Breast Cancer Treated With Radiotherapy Based on Three-Dimensional Dose Distributions to Cardiac Substructures

PurposeA relationship between mean heart dose (MHD) and acute coronary event (ACE) rate was reported in a study of patients with breast cancer (BC). The main objective of our cohort study was to validate this relationship and investigate if other dose-distribution parameters are better predictors for ACEs than MHD.Patients and MethodsThe cohort consisted of 910 consecutive female patients with BC treated with radiotherapy (RT) after breast-conserving surgery. The primary end point was cumulative incidence of ACEs within 9 years of follow-up. Both MHD and various dose-distribution parameters of the cardiac substructures were collected from three-dimensional computed tomography planning data.ResultsThe median MHD was 2.37 Gy (range, 0.51 to 15.25 Gy). The median follow-up time was 7.6 years (range, 0.1 to 10.1 years), during which 30 patients experienced an ACE. The cumulative incidence of ACE increased by 16.5% per Gy (95% CI, 0.6 to 35.0; P = .042). Analysis showed that the volume of the left ventricle receiving 5 Gy (LV-V5) was the most important prognostic dose-volume parameter. The most optimal multivariable normal tissue complication probability model for ACEs consisted of LV-V5, age, and weighted ACE risk score per patient (c-statistic, 0.83; 95% CI, 0.75 to 0.91).ConclusionA significant dose-effect relationship was found for ACEs within 9 years after RT. Using MHD, the relative increase per Gy was similar to that reported in the previous study. In addition, LV-V5 seemed to be a better predictor for ACEs than MHD. This study confirms the importance of reducing exposure of the heart to radiation to avoid excess risk of ACEs after radiotherapy for BC. (C) 2017 by American Society of Clinical Oncology.</p

Selection of head and neck cancer patients for adaptive radiotherapy to decrease xerostomia

AbstractBackground and purposeThe aim of this study was to develop and validate a method to select head and neck cancer patients for adaptive radiotherapy (ART) pre-treatment. Potential pre-treatment selection criteria presented in recent literature were included in the analysis.Materials and methodsDeviations from the planned parotid gland mean dose (PG ΔDmean) were estimated for 113 head and neck cancer patients by re-calculating plans on repeat CT scans. Uni- and multivariable linear regression analyses were performed to select pre-treatment parameters, and ROC curve analysis was used to determine cut off values, for selecting patients with a PG dose deviation larger than 3Gy. The patient selection method was validated in a second patient cohort of 43 patients.ResultsAfter multivariable analysis, the planned PG Dmean remained the only significant parameter for PG ΔDmean. A sensitivity of 91% and 80% could be obtained using a threshold of PG Dmean of 22.2Gy, for the development and validation cohorts, respectively. This would spare 38% (development cohort) and 24% (validation cohort) of patients from the labour-intensive ART procedure.ConclusionsThe presented method to select patients for ART pre-treatment reduces the labour of ART, contributing to a more effective allocation of the department resources

The Importance of Radiation Dose to the Atherosclerotic Plaque in the Left Anterior Descending Coronary Artery for Radiation-Induced Cardiac Toxicity of Breast Cancer Patients?

IMPORTANCE: Radiation-induced acute coronary events (ACEs) may occur as treatment-related late side effect of breast cancer (BC) radiation. However, the underlying mechanisms behind this radiation-induced cardiac disease remains to be determined. OBJECTIVE: The objective of this study was to test the hypothesis that radiation dose to calcified atherosclerotic plaques in the left anterior descending coronary artery (LAD) is a better predictor for ACEs than radiation dose to the whole heart or left ventricle in BC patients treated with radiotherapy (RT). DESIGN, SETTING, PARTICIPANTS, AND MAIN OUTCOMES AND MEASURES: The study cohort consisted of 910 BC patients treated with postoperative RT after breast conserving surgery. In total, 163 patients had an atherosclerotic plaque in the LAD. The endpoint was the occurrence of an ACE after treatment. For each individual patient, the mean heart dose (MHD), volume of the left ventricle receiving ≥ 5 Gy (LV-V5), mean LAD dose and mean dose to calcified atherosclerotic plaques in the LAD, if present, were acquired based on planning CT-scans. Cox-regression analysis was used to analyse the effects on the cumulative incidence of ACEs. RESULTS: The median follow-up time was 9.2 years (range: 0.1-14.3 years). In total, 38 patients (4.2%) developed an ACE during follow-up. For patients with an atherosclerotic plaque (n=163) the mean dose to the atherosclerotic plaque was the strongest predictor for ACE, even after correction for cardiovascular risk factors (HR: 1.269 (95% CI: 1.090-1.477), P=0.002). The LV-V5 was associated with ACEs in patients without atherosclerotic plaques in the LAD (n=680) (hazard ratio (HR): 1.021 (95% CI: 1.003-1.039; P=0.023). CONCLUSION AND RELEVANCE: The results of this study suggest that radiation dose to pre-existing calcified atherosclerotic plaques in the LAD is strongly associated with the development of ACEs in BC patients

Mechanics of the exceptional anuran ear

The anuran ear is frequently used for studying fundamental properties of vertebrate auditory systems. This is due to its unique anatomical features, most prominently the lack of a basilar membrane and the presence of two dedicated acoustic end organs, the basilar papilla and the amphibian papilla. Our current anatomical and functional knowledge implies that three distinct regions can be identified within these two organs. The basilar papilla functions as a single auditory filter. The low-frequency portion of the amphibian papilla is an electrically tuned, tonotopically organized auditory end organ. The high-frequency portion of the amphibian papilla is mechanically tuned and tonotopically organized, and it emits spontaneous otoacoustic emissions. This high-frequency portion of the amphibian papilla shows a remarkable, functional resemblance to the mammalian cochlea

Pre-treatment radiomic features predict individual lymph node failure for head and neck cancer patients

Background and purpose: To develop and validate a pre-treatment radiomics-based prediction model to identify pathological lymph nodes (pLNs) at risk of failures after definitive radiotherapy in head and neck squamous cell carcinoma patients. Materials and methods: Training and validation cohorts consisted of 165 patients with 558 pLNs and 112 patients with 467 pLNs, respectively. All patients were primarily treated with definitive radiotherapy, with or without systemic treatment. The endpoint was the cumulative incidence of nodal failure. For each pLN, 82 pre-treatment CT radiomic features and 7 clinical features were included in the Cox proportional-hazard analysis. Results: There were 68 and 23 nodal failures in the training and validation cohorts, respectively. Multivariable analysis revealed three clinical features (T-stage, gender and WHO Performance-status) and two radiomic features (Least-axis-length representing nodal size and gray level co-occurrence matrix based - Correlation representing nodal heterogeneity) as independent prognostic factors. The model showed good discrimination with a c-index of 0.80 (0.69–0.91) in the validation cohort, significantly better than models based on clinical features (p < 0.001) or radiomics (p = 0.003) alone. High- and low-risk groups were defined by using thresholds of estimated nodal failure risks at 2-year of 60% and 10%, resulting in positive and negative predictive values of 94.4% and 98.7%, respectively. Conclusion: A pre-treatment prediction model was developed and validated, integrating the quantitative radiomic features of individual lymph nodes with generally used clinical features. Using this prediction model, lymph nodes with a high failure risk can be identified prior to treatment, which might be used to select patients for intensified treatment strategies targeted on individual lymph nodes