Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.

Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives.Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%.Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity.Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

Home recording of 3-Hz spike-wave discharges in adults with absence epilepsy using the wearable Sensor Dot.

OBJECTIVE: Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment. METHODS: Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind-the-ear electrodes to record two-channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3-Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm-labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences. RESULTS: Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm-labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm-labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks. SIGNIFICANCE: The Sensor Dot improved seizure documentation at home, relative to patient self-reporting. Additional benefits were the short review time and the patients' device acceptance due to user-friendliness and comfortability.

In-hospital and home-based long-term monitoring of focal epilepsy with a wearable electroencephalographic device: Diagnostic yield and user experience.

OBJECTIVE: The aim is to report the performance of an electroencephalogram (EEG) seizure-detector algorithm on data obtained with a wearable device (WD) in patients with focal refractory epilepsy and their experience. METHODS: Patients used a WD, the Sensor Dot (SD), to measure two channels of EEG using dry electrode patches during presurgical evaluation and at home for up to 8 months. An automated seizure detection algorithm flagged EEG regions with possible seizures, which we reviewed to evaluate the algorithm's diagnostic yield. In addition, we collected data on usability, side effects, and patient satisfaction with an electronic seizure diary application (Helpilepsy). RESULTS: Sixteen inpatients used the SD for up to 5 days and had 21 seizures. Sixteen outpatients used the device for up to 8 months and reported 101 focal impaired awareness seizures during the periods selected for analysis. Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients. False detections/h, positive predictive value (PPV), and F1 scores were 7.13%, .11%, and .002% for inpatients and 7.77%, .04%, and .001% for outpatients. Artifacts and low signal quality contributed to poor performance metrics. The seizure detector identified 19 nonreported seizures during sleep, when the signal quality was better. Regarding patients' experience, the likelihood of using the device at 6 months was 62%, and side effects were the main reason for dropping out. Finally, daily and monthly questionnaire completion rates were 33% and 65%, respectively. SIGNIFICANCE: Focal seizure detection sensitivity based on behind-the-ear EEG was 52% in inpatients and 23% in outpatients, with high false alarm rates and low PPV and F1 scores. This unobtrusive wearable seizure detection device was well received but had side effects. The current workflow and low performance limit its implementation in clinical practice. We suggest different steps to improve these performance metrics and patient experience.

Automatic annotation correction for wearable EEG based epileptic seizure detection.

Objective. Video-electroencephalography (vEEG), which defines the ground truth for the detection of epileptic seizures, is inadequate for long-term home monitoring. Thanks to advantages in comfort and unobtrusiveness, wearable EEG devices have been suggested as a solution for home monitoring. However, one of the challenges in data-driven automated seizure detection with wearable EEG data is to have reliable seizure annotations. Seizure annotations on the gold-standard 25-channel vEEG recordings may not be optimal to delineate seizure activity on the concomitantly recorded wearable EEG, due to artifacts or absence of ictal activity on the limited set of electrodes of the wearable EEG. This paper aims to develop an automatic approach to correct for imperfect annotations of seizure activity on wearable EEG, which can be used to train seizure detection algorithms.Approach. This paper first investigates the effectiveness of correcting the seizure annotations for the training set with a visual annotation correction. Then a novel approach has been proposed to automatically remove non-seizure data from wearable EEG in epochs annotated as seizures in gold-standard video-EEG recordings. The performance of the automatic annotation correction approach was evaluated by comparing the seizure detection models trained with (a) original vEEG seizure annotations, (b) visually corrected seizure annotations, and (c) automatically corrected seizure annotations.Main results. The automated seizure detection approach trained with automatically corrected seizure annotations was more sensitive and had fewer false-positive detections compared to the approach trained with visually corrected seizure annotations, and the approach trained with the original seizure annotations from gold-standard vEEG.Significance. The wearable EEG seizure detection approach performs better when trained with automatic seizure annotation correction.

Accurate detection of typical absence seizures in adults and children using a two-channel electroencephalographic wearable behind the ears.

OBJECTIVE: Patients with absence epilepsy sensitivity <10% of their absences. The clinical gold standard to assess absence epilepsy is a 24-h electroencephalographic (EEG) recording, which is expensive, obtrusive, and time-consuming to review. We aimed to (1) investigate the performance of an unobtrusive, two-channel behind-the-ear EEG-based wearable, the Sensor Dot (SD), to detect typical absences in adults and children; and (2) develop a sensitive patient-specific absence seizure detection algorithm to reduce the review time of the recordings. METHODS: We recruited 12 patients (median age = 21 years, range = 8-50; seven female) who were admitted to the epilepsy monitoring units of University Hospitals Leuven for a 24-h 25-channel video-EEG recording to assess their refractory typical absences. Four additional behind-the-ear electrodes were attached for concomitant recording with the SD. Typical absences were defined as 3-Hz spike-and-wave discharges on EEG, lasting 3 s or longer. Seizures on SD were blindly annotated on the full recording and on the algorithm-labeled file and consequently compared to 25-channel EEG annotations. Patients or caregivers were asked to keep a seizure diary. Performance of the SD and seizure diary were measured using the F1 score. RESULTS: We concomitantly recorded 284 absences on video-EEG and SD. Our absence detection algorithm had a sensitivity of .983 and false positives per hour rate of .9138. Blind reading of full SD data resulted in sensitivity of .81, precision of .89, and F1 score of .73, whereas review of the algorithm-labeled files resulted in scores of .83, .89, and .87, respectively. Patient self-reporting gave sensitivity of .08, precision of 1.00, and F1 score of .15. SIGNIFICANCE: Using the wearable SD, epileptologists were able to reliably detect typical absence seizures. Our automated absence detection algorithm reduced the review time of a 24-h recording from 1-2 h to around 5-10 min.

The analgesic efficacy of forearm versus upper arm intravenous regional anesthesia (Bier's block): A randomized controlled non-inferiority trial.

STUDY OBJECTIVE: This study aimed to assess if a forearm (FA) intravenous regional anesthesia (IVRA) with a lower, less toxic, local anesthetic dosage is non-inferior to an upper arm (UA) IVRA in providing a surgical block in patients undergoing hand and wrist surgery. DESIGN: Observer-blinded, randomized non-inferiority study. SETTING: Operating room. PATIENTS: 280 patients undergoing hand surgery were randomly assigned to UA IVRA (n = 140) or FA IVRA (n = 140). INTERVENTIONS: Forearm IVRA or upper arm IVRA in patients undergoing hand and wrist surgery. MEASUREMENTS: The primary outcome was block success rate of both techniques. Block success was defined as no need of additional analgesics. A second, alternative non-inferiority outcome was defined as no need of conversion to general anesthesia. A difference in success rate of <5% was considered non-inferior. Secondary endpoints were tourniquet pain measured with a Numerical Rating Scale (0-10), satisfaction of patients and surgeons, onset time, surgical time and total OR time. MAIN RESULTS: Non-inferiority of block success rate, defined as no need of additional analgesics or conversion to general anesthesia was inconclusive (5.24%, 95% CI:-4.34%,+14.82%). Non-inferiority of no need of conversion to general anesthesia was confirmed (+0.73%, 95% CI:-0.69%,+2.15%). No differences were observed in onset time (FA: 5 (5, 8) vs UA: 6 (5, 7) min, p = 0.74), surgical time (FA: 8 (5, 12) vs UA: 7 (5, 11) min, p = 0.71), nor total OR stay time (FA: 34 (27, 41) vs UA: 35 (32, 39) min, p = 0.09). Tourniquet pain after 10 min was significantly lower after FA IVRA compared to UA IVRA (FA: 2.00 (0.00, 4.00) vs UA: 3.00 (1.00,5.00) min, p = 0.003). CONCLUSION: We failed to demonstrate non-inferiority of forearm IVRA with a lower dosage of LA in providing a surgical block without rescue opioids and LA. Non-inferiority of no need of conversion to general anesthesia was confirmed.

Photobiomodulation therapy for the prevention of acute radiation dermatitis in head and neck cancer patients (DERMISHEAD trial).

BACKGROUND AND PURPOSE: The purpose of this study was to investigate the effectiveness of photobiomodulation therapy (PBMT) for the prevention of acute radiation dermatitis (ARD) in head and neck cancer (HNC) patients. MATERIALS AND METHODS: A randomised, placebo-controlled trial (RCT) with 46 HNC patients who underwent radiotherapy (RT) with or without concomitant chemotherapy was set up (DERMISHEAD trial). Patients were randomised to receive PBM or placebo treatments from the first day of RT (2×/week) alongside the institutional skincare. The severity of skin reactions was assessed by the National Cancer Institute-Common Terminology Criteria for Adverse Events version 4.03 (NCI-CTCAE v4.03) and the Radiotherapy-Induced Skin Reaction Assessment Scale (RISRAS). Quality of life (QoL) was evaluated using the Skindex-16 questionnaire. RESULTS: PBMT significantly reduced NCI-CTCAE grade 2-3 ARD with 49% at the end of RT. CONCLUSION: The results of the first RCT in HNC patients showed that PBMT is an effective method to prevent the development of severe ARD. These results support the implementation of PBM in the clinical oncology - radiotherapy practice.

Letter to the Editor concerning the article "Application of red light phototherapy in the treatment of radioactive dermatitis in patients with head and neck cancer".

The aim of this Letter to the Editor was to report some methodological shortcomings in the recently published article "Application of red light phototherapy in the treatment of radioactive dermatitis in patients with head and neck cancer" by Zhang et al. There are some issues regarding the incomplete photobiomodulation (PBM) parameters, the chosen outcome measures, and some missing reference articles. In conclusion, the results of this study should be interpreted with caution and further research is necessary.