A bacterial algorithm for surface mapping using a Markov modulated Markov chain model of bacterial chemotaxis

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 83-85).Bacterial chemotaxis is the locomotory response of bacteria to chemical stimuli. E. coli movement can be described as a biased random walk, and it is known that the general biological or evolutionary function is to increase exposure to some substances and reduce exposure to others. In this thesis we introduce an algorithm for surface mapping, which tracks the motion of a bacteria-like software agent (based on a low-level model of the biochemical network responsible for chemotaxis) on a surface or objective function. Towards that end, a discrete Markov modulated Markov chains model of the chemotaxis pathway is described and used. Results from simulations using one- and two-dimensional test surfaces show that the software agents, referred to as bacterial agents, and the surface mapping algorithm can produce an estimate which shares some broad characteristics with the surface and uncovers some features of it. We also demonstrate that the bacterial agent, when given the ability to reduce the value of the surface at locations it visits (analogous to consuming a substance on a concentration surface), is more effective in reducing the surface integral within a certain period of time when compared to a bacterial agent lacking the ability to sense surface information or respond to it.by Alaa Amin Kharbouch.S.M

Automatic detection of epileptic seizure onset and termination using intracranial EEG

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 87-90).This thesis addresses the problem of real-time epileptic 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 out 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 tailored to individual patients. In addition, we describe and evaluate an algorithm for the detection of the cessation of seizure activity within IEEG. Seizure end detection algorithms can enable important clinical applications such as the delivery of therapy to ameliorate post-ictal symptoms, the detection of status epilepticus, and the estimation of seizure duration. Our machine-learning-based approach is patient-specific. The algorithm is designed to search for the termination of electrographic seizure activity once a seizure has been discovered by a seizure onset detector. When tested on 65 seizures, 88% of all seizure ends were detected within 15 seconds of the time determined by a clinical expert to represent the electrographic end of a seizure. We explore the effects of channel pre-selection on seizure onset detection. We evaluate and present the results from a seizure detector that has been restricted to use only a small subset of the channels available. These channels are manually chosen to be those that show the earliest ictal activity. The results indicate that performance can suffer in many cases when the algorithm uses a small set of selected channels, often in the form of an increase in false alarm rate. This suggests that the inclusion of a full channel set allows the system to leverage information that is not readily apparent to a clinical reader (from regions seemingly not involved in the onset) to better differentiate ictal and inter-ictal patterns. Finally, we present and evaluate an algorithm for patient-specific feature extraction, where the feature extraction process for a given patient leverages the training data available for that patient. The results from an evaluation of a detector that supplemented the original spectral energy features with features computed in a patient-specific manner show a significant improvement in 3 out of 5 patients. The results suggest that this is a promising avenue for further improvement in the performance of the seizure onset detector.by Alaa Amin Kharbouch.Ph.D

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.Center for Integration of Medicine and Innovative TechnologyQuanta Computer (Firm)Cyberonics, Inc

Deep learning for automated sleep staging using instantaneous heart rate

Abstract Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender

Quantifying neurologic disease using biosensor measurements in-clinic and in free-living settings in multiple sclerosis

Technological advances in passive digital phenotyping present the opportunity to quantify neurological diseases using new approaches that may complement clinical assessments. Here, we studied multiple sclerosis (MS) as a model neurological disease for investigating physiometric and environmental signals. The objective of this study was to assess the feasibility and correlation of wearable biosensors with traditional clinical measures of disability both in clinic and in free-living in MS patients. This is a single site observational cohort study conducted at an academic neurological center specializing in MS. A cohort of 25 MS patients with varying disability scores were recruited. Patients were monitored in clinic while wearing biosensors at nine body locations at three separate visits. Biosensor-derived features including aspects of gait (stance time, turn angle, mean turn velocity) and balance were collected, along with standardized disability scores assessed by a neurologist. Participants also wore up to three sensors on the wrist, ankle, and sternum for 8 weeks as they went about their daily lives. The primary outcomes were feasibility, adherence, as well as correlation of biosensor-derived metrics with traditional neurologist-assessed clinical measures of disability. We used machine-learning algorithms to extract multiple features of motion and dexterity and correlated these measures with more traditional measures of neurological disability, including the expanded disability status scale (EDSS) and the MS functional composite-4 (MSFC-4). In free-living, sleep measures were additionally collected. Twenty-three subjects completed the first two of three in-clinic study visits and the 8-week free-living biosensor period. Several biosensor-derived features significantly correlated with EDSS and MSFC-4 scores derived at visit two, including mobility stance time with MSFC-4 z-score (Spearman correlation -0.546; p= 0.0070), several aspects of turning including turn angle (0.437; p = 0.0372), and maximum angular velocity (0.653; p = 0.0007). Similar correlations were observed at subsequent clinic visits, and in the free-living setting. We also found other passively collected signals, including measures of sleep, that correlated with disease severity. These findings demonstrate the feasibility of applying passive biosensor measurement techniques to monitor disability in MS patients both in clinic and in the free-living setting