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Epilepsy is one of the most common neurological disorders affecting a significant portion of the world’s population and approximately 2.5 million people in the United States. Important biomedical research effort is focused on the development of energy efficient devices for the real-time detection of seizures. In this paper, we propose an Internet of Medical Things (IoMT)-based automated seizure detection system which will detect a seizure from electroencephalography (EEG) signals using a voltage level detector (VLD) and a signal rejection algorithm (SRA). The proposed system analyzes neural signals continuously and extracts the hyper-synchronous pulses for the detection of seizure onset. Within a time frame, if the number of pulses exceeds a predefined threshold value, a seizure is declared. The SRA reduces false detections, which in turn enhances the accuracy of the seizure detector. The design was validated using system-level simulations and consumer electronics proof of concept. The proposed seizure detector reports a sensitivity of 96.9% and specificity of 97.5%. The use of minimal circuitry can lead to reduction of power consumption compared to many contemporary approaches. The proposed approach can be generalized to other sensor modalities and the use of wearable or implantable solutions, or a combination of the two. [ABSTRACT FROM AUTHOR]

Epilepsy, which is characterized by recurrent spontaneous seizures, has a considerably negative impact on both the quality and the expectancy of life of the patient. Approximately 3.4 million individuals in the USA and up to 1% of the world population is afflicted by epilepsy. This necessitates the real-time detection of seizures which can be done by the use of an Internet of Things (IoT) framework for smart healthcare. In this paper, we propose an electroencephalogram (EEG)-based seizure detection system in the IoT framework which uses the discrete wavelet transform (DWT), Hjorth parameters (HPs), statistical features, and a machine learning classifier. Seizure detection is done in two stages. In the first stage, EEG signals are decomposed by the DWT into sub-bands and features (activity, signal complexity, and standard deviation) were extracted from each of these sub-bands. In the second stage, a deep neural network (DNN) classifier is used to classify the EEG data. A prototype of the proposed neuro-detect was implemented using the hardware-in-the-loop approach. The results demonstrate a significant difference in HP values between interictal and ictal EEG with ictal EEG being less complex than interictal EEG. In this approach, we report an accuracy of 100% for a classification of normal versus ictal EEG and 98.6% for normal and interictal versus ictal EEG. [ABSTRACT FROM AUTHOR]

In this paper, we propose bag of adversarial features (BAFs) for identifying mild traumatic brain injury (MTBI) patients from their diffusion magnetic resonance images (MRIs) (obtained within one month of injury) by incorporating unsupervised feature learning techniques. MTBI is a growing public health problem with an estimated incidence of over 1.7 million people annually in USA. Diagnosis is based on clinical history and symptoms, and accurate, concrete measures of injury are lacking. Unlike most of the previous works, which use hand-crafted features extracted from different parts of brain for MTBI classification, we employ feature learning algorithms to learn more discriminative representation for this task. A major challenge in this field thus far is the relatively small number of subjects available for training. This makes it difficult to use an end-to-end convolutional neural network to directly classify a subject from MRIs. To overcome this challenge, we first apply an adversarial auto-encoder (with convolutional structure) to learn patch-level features, from overlapping image patches extracted from different brain regions. We then aggregate these features through a bag-of-words approach. We perform an extensive experimental study on a dataset of 227 subjects (including 109 MTBI patients, and 118 age and sex-matched healthy controls) and compare the bag-of-deep-features with several previous approaches. Our experimental results show that the BAF significantly outperforms earlier works relying on the mean values of MR metrics in selected brain regions. [ABSTRACT FROM AUTHOR]

Background: In the United States, there are over seven million stroke survivors, with many facing gait impairments due to foot drop. This restricts their community ambulation and hinders functional independence, leading to several long-term health complications. Despite the best available physical therapy, gait function is incompletely recovered, and this occurs mainly during the acute phase post-stroke. Therapeutic options are limited currently. Novel therapies based on neurobiological principles have the potential to lead to long-term functional improvements. The Brain-Computer Interface (BCI) controlled Functional Electrical Stimulation (FES) system is one such strategy. It is based on Hebbian principles and has shown promise in early feasibility studies. The current study describes the BCI-FES clinical trial, which examines the safety and efficacy of this system, compared to conventional physical therapy (PT), to improve gait velocity for those with chronic gait impairment post-stroke. The trial also aims to find other secondary factors that may impact or accompany these improvements and establish the potential of Hebbian-based rehabilitation therapies. Methods: This Phase II clinical trial is a two-arm, randomized, controlled, longitudinal study with 66 stroke participants in the chronic (> 6 months) stage of gait impairment. The participants undergo either BCI-FES paired with PT or dose-matched PT sessions (three times weekly for four weeks). The primary outcome is gait velocity (10-meter walk test), and secondary outcomes include gait endurance, range of motion, strength, sensation, quality of life, and neurophysiological biomarkers. These measures are acquired longitudinally. Discussion: BCI-FES holds promise for gait velocity improvements in stroke patients. This clinical trial will evaluate the safety and efficacy of BCI-FES therapy when compared to dose-matched conventional therapy. The success of this trial will inform the potential utility of a Phase III efficacy trial. Trial registration: The trial was registered as "BCI-FES Therapy for Stroke Rehabilitation" on February 19, 2020, at clinicaltrials.gov with the identifier NCT04279067. [ABSTRACT FROM AUTHOR]

Background: Patients with end-stage kidney disease (ESKD) treated with hemodialysis (HD) experience many distressing symptoms. One frequently reported symptom is insomnia. There are unique issues about HD treatments and schedules that disrupt regular sleep/wake routines and possibly contribute to the high severity of insomnia. Despite evidence for broad-ranging health effects of insomnia, very few clinical trials have tested the efficacy of treatments for HD patients. Cognitive-behavioral therapy for insomnia (CBT-I) is a recommended first-line therapy but largely inaccessible to HD patients in the United States, partly because they commit considerable amounts of time to thrice-weekly dialysis treatments. Another important reason could be the logistical and reimbursement challenges associated with providing behavioral health care at the dialysis center. CBT-I delivered by telehealth can overcome barriers to access, but its efficacy has never been rigorously tested for these patients. Pharmacotherapy is the most widely used treatment for insomnia; however, some drugs presently used are unsafe as they are associated with a higher risk for death for HD patients (benzodiazepines and zolpidem-like drugs). The efficacy and safety of other medications (trazodone) for the treatment of insomnia has never been tested for patients treated with HD.Methods: This trial tests the short- and long-term comparative effectiveness of 6-week treatment with telehealth CBT-I, trazodone, or medication placebo. This will be accomplished with a randomized controlled trial (RCT) in which 126 participants treated with HD in community-based dialysis facilities with chronic insomnia will be assigned 1:1:1 to telehealth CBT-I, trazodone, or medication placebo, respectively; short-term effectiveness of each treatment arm will be determined at the end of 6-weeks of treatment and long-term effectiveness at 25-weeks. The primary and secondary patient-reported outcomes will be assessed with computer-based telephone interviewing by research scientists blinded to treatment assignment; additional secondary outcomes will be assessed by participant interview and actigraphy.Discussion: This clinical RCT will provide the first evidence for the comparative effectiveness of two distinct approaches for treating chronic insomnia and other patient-reported outcomes for patients receiving maintenance HD.Trial Registration: NCT03534284 May 23, 2018. SLEEP-HD Protocol Version: 1.3.4 (7/22/2020). [ABSTRACT FROM AUTHOR]