Ali H. Shoeb

Patient-specific seizure onset detection

This work presents an automated, patient-specific method for the detection of epileptic seizure onsets from noninvasive EEG. We adopt a patient-specific approach to exploit the consistency of an individual patients seizure and non-seizure EEG. Our method uses a wavelet decomposition to construct a feature vector that captures the morphology and spatial distribution of an EEG epoch, and then determines whether that vector is representative of a patients seizure or non-seizure EEG using the support-vector machine classification algorithm. Our completely automated method was tested on non-invasive EEG from thirty-six pediatric subjects suffering from a variety of seizure types. It detected 131 of 139 seizure events within 8.0/spl plusmn/3.2 seconds following electrographic onset, and declared 15 false-detections in 60 hours of clinical EEG. Our patient-specific method can be used to initiate delay-sensitive clinical procedures following seizure onset; for example, the injection of an imaging radiopharmaceutical or stimulation of the vagus nerve.

An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor

An 8-channel scalable EEG acquisition SoC is presented to continuously detect and record patient-specific seizure onset activities from scalp EEG. The SoC integrates 8 high-dynamic range Analog Front-End (AFE) channels, a machine-learning seizure classification processor and a 64 KB SRAM. The classification processor exploits the Distributed Quad-LUT filter architecture to minimize the area while also minimizing the overhead in power × delay . The AFE employs a Chopper-Stabilized Capacitive Coupled Instrumentation Amplifier to show NEF of 5.1 and noise RTI of 0.91 μVrms for 0.5-100 Hz bandwidth. The classification processor adopts a support-vector machine as a classifier, with a GBW controller that gives real-time gain and bandwidth feedback to AFE to maintain accuracy. The SoC is verified with the Childrens Hospital Boston-MIT EEG database as well as with rapid eye blink pattern detection test. The SoC is implemented in 0.18 μm 1P6M CMOS process occupying 25 mm2, and it shows an accuracy of 84.4% in eye blink classification test, at 2.03 μJ/classification energy efficiency. The 64 KB on chip memory can store up to 120 seconds of raw EEG data.

NON-INVASIVE COMPUTERIZED SYSTEM FOR AUTOMATICALLY INITIATING VAGUS NERVE STIMULATION FOLLOWING PATIENT-SPECIFIC DETECTION OF SEIZURES OR EPILEPTIFORM DISCHARGES

OBJECTIVE To demonstrate the feasibility of using a computerized system to detect the onset of a seizure and, in response, initiate Vagus nerve stimulation (VNS) in patients with medically refractory epilepsy. METHODS We designed and built a non-invasive, computerized system that automatically initiates VNS following the real-time detection of a pre-identified seizure or epileptiform discharge. The system detects these events through patient-specific analysis of the scalp electroencephalogram (EEG) and electrocardiogram (ECG) signals. RESULTS We evaluated the performance of the system on 5 patients (A-E). For patients A and B the computerized system initiated VNS in response to seizures; for patients C and D the system initiated VNS in response to epileptiform discharges; and for patient E neither seizures nor epileptiform discharges were observed during the evaluation period. During the 81 hour clinical test of the system on patient A, the computerized system detected 5/5 seizures and initiated VNS within 5 seconds of the appearance of ictal discharges in the EEG; VNS did not seem to alter the electrographic or behavioral characteristics of the seizures in this case. During the same testing session the computerized system initiated false stimulations at the rate of 1 false stimulus every 2.5 hours while the subject was at rest and not ambulating. During the 26 hour clinical test of the system on patient B, the computerized system detected 1/1 seizures and initiated VNS within 16 seconds of the appearance of ictal discharges; VNS did not alter the electrographic duration of the seizure but decreased anxiety and increased awareness during the post-seizure recovery phase. During the same testing session the computerized system did not declare any false detections. SIGNIFICANCE Initiating Vagus nerve stimulation soon after the onset of a seizure may abort or ameliorate seizure symptoms in some patients; unfortunately, a significant number of patients cannot initiate VNS by themselves following the start of a seizure. A system that automatically couples automated detection of seizure onset to initiation of VNS may be helpful for seizure treatment.

An 8-channel scalable EEG acquisition SoC with fully integrated patient-specific seizure classification and recording processor

Tracking seizure activity to determine proper medication requires a small form factor, ultra-low power sensor with continuous EEG classification. Technical challenges arise from: 1) patient-to-patient variation of seizure pattern on EEG, 2) fully integrating an ultra-low power variable dynamic range instrumentation circuits with seizure detection processor, and 3) reducing communication overhead. Reference [1] extracted EEG features locally on-chip to reduce the data being transmitted, and saved power by 1/14 when compared to raw EEG data transmission. However, it still needs data transmission and off-chip classification to detect and to store seizure activity. This paper presents an ultra-low power scalable EEG acquisition SoC for continuous seizure detection and recording with fully integrated patient-specific Support Vector Machine (SVM)-based classification processor.

Impact of pediatric epilepsy on sleep patterns and behaviors in children and parents

Purpose:  Disrupted sleep patterns in children with epilepsy and their parents are commonly described clinically. A number of studies have shown increased frequency of sleep disorders among pediatric epilepsy patients; however, few have characterized the association between epilepsy and parental sleep quality and household sleeping arrangements. The purpose of this study was to explore the effect of pediatric epilepsy on child sleep, parental sleep and fatigue, and parent‐child sleeping arrangements, including room sharing and cosleeping.

An algorithm for seizure onset detection using intracranial EEG

This article addresses the problem of real-time seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 of 10 patients. These results indicate that a sensitive, specific, and relatively short-latency detection system based on machine learning can be employed for seizure detection from EEG using a full set of intracranial electrodes to individual patients. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

A machine-learning algorithm for detecting seizure termination in scalp EEG

Efforts to develop algorithms that can robustly detect the cessation of seizure activity within scalp EEGs are now underway. Such algorithms can facilitate novel clinical applications such as the estimation of a seizures duration; the delivery of therapies designed to mitigate postictal period symptoms; or detection of the presence of status epilepticus. In this article, we present and evaluate a novel, machine learning-based method for detecting the termination of electrographic seizure activity. When tested on 133 seizures from a public database, our method successfully detected the end of 132 seizures within 10.3 ± 5.5 seconds of the time determined by an electroencephalographer to represent the electrographic end of seizure. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test electrographic seizures within a 15-second error margin compared with electroencephalographer determinations. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Detecting Seizure Onset in the Ambulatory Setting: Demonstrating Feasibility

Ambulatory EEG recorders are commercially available. The majority of these recorders are only capable of capturing and storing EEG for later review by clinicians. A few models are equipped with real-time seizure event detectors, but these detectors make no guarantees on when during a seizure a detection is made. This renders current ambulatory EEG recorders unsuitable for activating alarms or initiating therapies to acutely impact seizure progression in the ambulatory setting. Integrating seizure onset detectors into existing ambulatory recorders will make these applications feasible. Successful integration requires that these detectors be executable on the resource-limited digital signal processors found within ambulatory recorders. In this paper we describe the integration of a patient-specific seizure onset detector with a commercially available ambulatory EEG recorder, and demonstrate how such integration could enable the detection of seizure onset in the ambulatory setting

Impact of Patient-Specificity on Seizure Onset Detection Performance

In this paper we quantify the degree to which patient- specificity affects the detection latency, sensitivity, and specificity of a seizure detector using 536 hours of continuously recorded scalp EEG from 16 epilepsy patients. We demonstrate that a detector that knows of an individuals seizure and non- seizure EEG outperforms a detector limited to knowledge of an individuals non-seizure EEG, and a detector limited to knowledge of population seizure and non-seizure EEG.

Building a High-Quality Mobile Telemedicine System using Network Striping over Dissimilar Wireless Wide Area Networks

This paper describes an approach to building a high-quality mobile telemedicine system that overcomes the limitations of individual public wireless data networks. Public wireless data channels do not have the capacity to handle the high-bandwidth video needed for applications such as remote evaluation of trauma and stroke patients. Network striping allows us to aggregate multiple physical channels to meet the bandwidth requirements for the video. We have developed flexible network-striping software middleware, and are building a telemedicine system using that middleware. Our approach uses existing communications infrastructure and conventional-off-the-shelf components, making the system easy to deploy