A Low Power Multi-Class Migraine Detection Processor Based on Somatosensory Evoked Potentials

Migraine is a disabling neurological disorder that can be recurrent and persist for long durations. The continuous monitoring of the brain activities can enable the patient to respond on time before the occurrence of the approaching migraine episode to minimize the severity. Therefore, there is a need for a wearable device that can ensure the early diagnosis of a migraine attack. This brief presents a low latency, and power-efficient feature extraction and classification processor for the early detection of a migraine attack. Somatosensory Evoked Potentials (SEP) are utilized to monitor the migraine patterns in an ambulatory environment aiming to have a processor integrated on-sensor for power-efficient and timely intervention. In this work, a complete digital design of the wearable environment is proposed. It allows the extraction of multiple features including multiple power spectral bands using 256-point fast Fourier transform (FFT), root mean square of late HFO bursts and latency of N20 peak. These features are then classified using a multi-classification artificial neural network (ANN)-based classifier which is also realized on the chip. The proposed processor is placed and routed in a 180nm CMOS with an active area of 0.5mm(2). The total power consumption is 249 mu W while operating at a 20MHz clock with full computations completed in 1.31ms

Processing Regular Path Queries on Arbitrarily Distributed Data

Regular Path Queries (RPQs) are a type of graph query where answers are pairs of nodes connected by a sequence of edges matching a regular expression. We study the techniques to process such queries on a distributed graph of data. While many techniques assume the location of each data element (node or edge) is known, when the components of the distributed system are autonomous, the data will be arbitrarily distributed. As the different query processing strategies are equivalently costly in the worst case, we isolate query-dependent cost factors and present a method to choose between strategies, using new query cost estimation techniques. We evaluate our techniques using meaningful queries on biomedical data

Moral Considerations in Pediatric Food Allergies

Food allergies are common health problem among children. They carry a significant risk of severe allergic reactions. These disorders are chronic conditions in which the immune system becomes hypersensitive to some food products. It is estimated that 8% of children under the age of three have a type of food allergy. The common allergenic foods include cow’s milk, wheat, peanuts, egg, soy and fish.The mainstay of treatment is to eliminate the allergenic food from the patient’s diet which in case of a child mandates special behavioral and ethical problems. Considering the growing incidence of food allergy, and the risk of anaphylaxis, diverse moral-ethical challenges face parents, school administrators and health professionals. Older children have the right to keep the fact of their disease private and this is a matter of their autonomy and may be an effort to prevent stigmatization by other students followed by psychosocial discomfort.Some moral & ethical principles in implementing management guidelines for allergic children include: -Imagine if the patient was your own. What level of protection would you expect for him/her? -Do protective policies cause the child to be isolated from others? -Are medical recordings confidential? -Avoid unduly limiting the diet of these children. A certain scenario is an infant with cow milk allergy. In this condition specific consideration should be paid to the mother’s nutritional status when a dietary elimination strategy is to be implemented. Considering the costs /benefits of diagnostic and therapeutic measures in food allergic children is recommended.

Caustic Ingestions

Prevention has a main role in reducing the occurrence of corrosive ingestion especially in children, yet this goal is far from being reached in developing countries, where such injuries are largely unreported and their true prevalence simply cannot be extrapolated from random articles or personal experience. Because of the accidental nature of the ingestions, the case fatality rate for pediatric patients is significantly less than that of adolescents and adults.  Currently, esophagoscopy is recommended for all patients with a history of caustic substance ingestion because clinical criteria have not proved to be reliable predictors of esophageal injury. The presence or absence of three serious signs and symptoms-vomiting, drooling, and stridor—as well as the presence and location of oropharyngeal burns could be  compared with the findings on subsequent esophagoscopy. Medical or endoscopic prevention of stricture is debatable, yet esophageal stents, absorbable or not, show promising data. The purpose of this lecture is to outline the current epidemiology, mechanism of injury, clinical manifestations, management and long-term complications of caustic ingestions in pediatric patients.   Key Words: Caustic, Children, Ingestions

Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering

Objective Accurate and reliable detection of tremor onset in Parkinson’s disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD. Methods We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate. Results The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system. Conclusion The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest. Significance The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor

Migraine classification using somatosensory evoked potentials

Objective: The automatic detection of migraine states using electrophysiological recordings may play a key role in migraine diagnosis and early treatment. Migraineurs are characterized by a deficit of habituation in cortical information processing, causing abnormal changes of somatosensory evoked potentials. Here, we propose a machine learning approach to utilize somatosensory evoked potential-based biomarkers for migraine classification in a noninvasive setting. Methods: Forty-two migraine patients, including 29 interictal and 13 ictal, were recruited and compared with 15 healthy volunteers of similar age and gender distribution. The right median nerve somatosensory evoked potentials were collected from all subjects. State-of-the-art machine learning algorithms including random forest, extreme gradient-boosting trees, support vector machines, K-nearest neighbors, multilayer perceptron, linear discriminant analysis, and logistic regression were used for classification and were built upon somatosensory evoked potential features in time and frequency domains. A feature selection method was employed to assess the contribution of features and compare it with previous clinical findings, and to build an optimal feature set by removing redundant features. Results: Using a set of relevant features and different machine learning models, accuracies ranging from 51.2% to 72.4% were achieved for the healthy volunteers-ictal-interictal classification task. Following model and feature selection, we successfully separated the three groups of subjects with an accuracy of 89.7% for the healthy volunteers-ictal, 88.7% for healthy volunteers-interictal, 80.2% for ictal-interictal, and 73.3% for healthy volunteers-ictal-interictal classification tasks, respectively. Conclusion: Our proposed model suggests the potential use of somatosensory evoked potentials as a prominent and reliable signal in migraine classification. This non-invasive somatosensory evoked potential-based classification system offers the potential to reliably separate migraine patients in ictal and interictal states from healthy controls

NeuralTree: A 256-Channel 0.227uJ/class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel count and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227uJ/class energy efficiency in a compact area of 0.014mm^2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft uECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease from human local field potentials (LFPs) was demonstrated