Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.
Introduction: Still’s murmurs are difficult to distinguish from other heart murmurs at the primary care level. This leads to an estimated 400,000 children being referred to pediatric cardiologists each year for evaluation of heart murmurs in the United States. The murmur is ultimately diagnosed as innocent Still’s murmur in approximately 90% of these children. This diagnosis requires no specialty referral, cardiac testing, exercise restrictions or cardiology follow-up. These unnecessary referrals and associated tests waste healthcare resources, add to healthcare costs, and are a source of avoidable anxiety in children and families while waiting to see a pediatric cardiologist. Hypothesis: We have developed a novel mobile technology to discriminate Still’s murmur from all other murmurs. A smartphone application (app) that could quickly distinguish Still’s murmur from all other murmurs with high accuracy at the point of care could reassure pediatricians in their identification of Still’s murmur, significantly reducing the current rate of unnecessary referrals to cardiologists. Methods: Our current prototype is StethAid, a smartphone app which accepts heart sound recordings, and a cloud-based deep learning algorithm for discriminating Still’s murmur from other murmurs. The algorithm was independently developed and evaluated using archived heart sounds, recorded by one of the authors (RWD), made using an electronic stethoscope (3M Littmann Model 4100). Results: Our algorithm offers a sensitivity (Still’s murmurs correctly identified) of 90% with a specificity (Non-Still’s murmurs correctly identified) of 99% on 5-fold cross-validation over the Murmur Library. The area under the curve was 0.99446. The algorithm’s result is available in real time at the point-of-care (< 1 min). Conclusions: The described point-of-care mobile technology could automatically distinguish Still’s murmur from all other murmurs with high accuracy. The technology could lower the current high rate of specialist referrals associated with Still’s murmur and reduce the related financial and emotional costs. Future directions will include further improvement of the technology and validation through multi-center clinical trials.
screen (from those with history of in-utero exposure) were significant predictors for onset of NAS requiring pharmacological intervention at >96 HOL (OR 0.21; p value 0.011). Conclusions The majority of infants who required pharmacological treatment for NAS during their postnatal observation period were diagnosed within the first 120 HOL. Those atrisk infants, born to mothers with a known history of exposure, who have a negative urine toxicology screen for both baby and mother, should be monitored beyond 5 days as they tend to have a later presentation.
Introduction: Quality of cardiopulmonary resuscitation (CPR) contributes significantly to morbidity and mortality in both in-hospital and out-of-hospital cardiac arrest. Key parameters that determine the CPR quality are compression rate, compression depth, duration of interruptions, chest recoil factor and respiratory rate. Several studies have demonstrated that real-time audiovisual feedback improves CPR quality in both bystanders and hospital staff. This study aims to develop and validate a smart device (phones and wearable technology) application to provide real-time audiovisual and haptic feedback to optimize CPR quality, by calculating aforementioned chest compression parameters. Hypothesis: A mobile application using acceleration sensor data from smart devices can provide accurate real time CPR quality feedback. Methods: A mobile application was developed to track the compression depth, compression rate and pause duration in real time using the data captured from the on-device accelerometer. The mobile device was placed on an adult manikin’s chest along the midline close to the point of compressions. Data from the application was compared directly to data obtained from a validated clinical standard CPR quality tool. Results: CPR quality parameters were obtained from the app and the standard for 60, 10-second-long sessions. Bland-Altman plot analysis for compression depth showed agreement between the app measurements and standard within +/-3.5mm (Figure 1). The intraclass correlation for agreement in the measurement of compression count was 0.92 (95% CI: 0.88-0.95), indicative of very strong agreement. Conclusions: Smart device (phones and wearable technology) applications using acceleration sensor data can accurately provide real-time CPR quality feedback. With further development and validation they can provide a ubiquitous CPR feedback tool valuable for out of hospital arrests and in under-privileged areas worldwide.
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