On-line apnea-bradycardia detection using hidden semi-Markov models.

International audienceIn this work, we propose a detection method that exploits not only the instantaneous values, but also the intrinsic dynamics of the RR series, for the detection of apnea-bradycardia episodes in preterm infants. A hidden semi-Markov model is proposed to represent and characterize the temporal evolution of observed RR series and different pre-processing methods of these series are investigated. This approach is quantitatively evaluated through synthetic and real signals, the latter being acquired in neonatal intensive care units (NICU). Compared to two conventional detectors used in NICU our best detector shows an improvement of around 13% in sensitivity and 7% in specificity. Furthermore, a reduced detection delay of approximately 3 seconds is obtained with respect to conventional detectors

Stochastic Modeling of Central Apnea Events in Preterm Infants

A near-ubiquitous pathology in very low birth weight infants is neonatal apnea, breathing pauses with slowing of the heart and falling blood oxygen. Events of substantial duration occasionally occur after an infant is discharged from the neonatal intensive care unit (NICU). It is not known whether apneas result from a predictable process or from a stochastic process, but the observation that they occur in seemingly random clusters justifies the use of stochastic models. We use a hidden-Markov model to analyze the distribution of durations of apneas and the distribution of times between apneas. The model suggests the presence of four breathing states, ranging from very stable (with an average lifetime of 12 h) to very unstable (with an average lifetime of 10 s). Although the states themselves are not visible, the mathematical analysis gives estimates of the transition rates among these states. We have obtained these transition rates, and shown how they change with post-menstrual age; as expected, the residence time in the more stable breathing states increases with age. We also extrapolated the model to predict the frequency of very prolonged apnea during the first year of life. This paradigm-stochastic modeling of cardiorespiratory control in neonatal infants to estimate risk for severe clinical events-may be a first step toward personalized risk assessment for life threatening apnea events after NICU discharge

Cardiac Arrhythmia Monitoring and Severe Event Prediction System

Abnormalities of cardiac rhythms are correlated with significant morbidity. For example, atrial fibrillation, affecting at least 2.3 million people in the United States, is associated with an increased risk of both stroke and mortality; supra-ventricular tachycardia, detected in approximately 90,000 cases annually in the United States, ventricular arrhythmias cause 75% to 80% of the cases of sudden cardiac death; bradyarrhythmias may cause syncope, fatigue from chronotropic incompetence, or sudden death from asystole or ventricular tachycardia. Due to the time-sensitive nature of cardiac events, it is of utmost importance to ensure that medical intervention is provided in a timely manner, which could benefit greatly from a cardiac arrhythmia monitoring system that can detect and preferably also predict abnormal cardiac events. In recent years, with the development of medical monitoring devices, vast amounts of physiological signal data have been collected and become available for analysis. However, the extraction of the relevant information from physiological signals is hindered by the complexity within signal morphology, which leads to vague definitions and ambiguous guidelines, causing difficulties even for medical expert. To address the variability-related issues, most traditional methods depend heavily on pre-processing to identify specific morphology types and extract the related features. Despite many successes, one of the drawbacks of these methods is that they require signal data of high quality and tend to be less effective in the presence of noise. Although not an issue in almost noiseless situations, such pre-processing--based methods have become insufficient with the advent of portable arrhythmia monitoring devices in recent years capable of collecting physiological signals in real time, albeit with more noise. Therefore, to enable automated clinical decision, it is desirable to introduce new methods that require minimal pre-processing prior to analysis and are robust to noise. This thesis aims to develop a cardiac arrhythmia monitoring and prediction system applicable to portable arrhythmia monitoring devices. The analysis is based on a novel algorithm which does not rely on the detailed morphological information contained within each heartbeat, thus minimizing the impact of noise. Instead, the method works by analyzing the similarity and variability within strings of consecutive heartbeats, relying only on the broad morphology type of each heartbeat and utilizing the computer's ability to store and process a large number of heartbeats beyond humanly possible. The novel algorithm is based on deterministic probabilistic finite-state automata which have found great success in the field of natural language processing by studying the relationships among different words in a sentence rather than the detailed structure of the individual words. The proposed algorithm has been employed in experiments on both detection and prediction of various cardiac arrhythmia types and has achieved an AUC in the range of 0.70 to 0.95 for detection and prediction of different types of cardiac arrhythmias and cardiac events with data collected from publicly available databases, hospital bedside database and data collected from portable devices. Comparing with other well-established methods, the proposed algorithm has achieved equal or better classification results. In addition, the performance of the proposed algorithm is almost identical with or without any pre-processing on the data. The work in the thesis could be deployed as a cardiac arrhythmia monitoring and severe event prediction system which could alert patients and clinicians of an impending event, thereby enabling timely medical interventions.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169873/1/zcli_1.pd

A primer on coupled state-switching models for multiple interacting time series

State-switching models such as hidden Markov models or Markov-switching regression models are routinely applied to analyse sequences of observations that are driven by underlying non-observable states. Coupled state-switching models extend these approaches to address the case of multiple observation sequences whose underlying state variables interact. In this paper, we provide an overview of the modelling techniques related to coupling in state-switching models, thereby forming a rich and flexible statistical framework particularly useful for modelling correlated time series. Simulation experiments demonstrate the relevance of being able to account for an asynchronous evolution as well as interactions between the underlying latent processes. The models are further illustrated using two case studies related to a) interactions between a dolphin mother and her calf as inferred from movement data; and b) electronic health record data collected on 696 patients within an intensive care unit.Comment: 30 pages, 9 figure

Online change detection techniques in time series: an overview

Time-series change detection has been studied in several fields. From sensor data, engineering systems, medical diagnosis, and financial markets to user actions on a network, huge amounts of temporal data are generated. There is a need for a clear separation between normal and abnormal behaviour of the system in order to investigate causes or forecast change. Characteristics include irregularities, deviations, anomalies, outliers, novelties or surprising patterns. The efficient detection of such patterns is challenging, especially when constraints need to be taken into account, such as the data velocity, volume, limited time for reacting to events, and the details of the temporal sequence.This paper reviews the main techniques for time series change point detection, focusing on online methods. Performance criteria including complexity, time granularity, and robustness is used to compare techniques, followed by a discussion about current challenges and open issue

Recent development of respiratory rate measurement technologies

Respiratory rate (RR) is an important physiological parameter whose abnormity has been regarded as an important indicator of serious illness. In order to make RR monitoring simple to do, reliable and accurate, many different methods have been proposed for such automatic monitoring. According to the theory of respiratory rate extraction, methods are categorized into three modalities: extracting RR from other physiological signals, RR measurement based on respiratory movements, and RR measurement based on airflow. The merits and limitations of each method are highlighted and discussed. In addition, current works are summarized to suggest key directions for the development of future RR monitoring methodologies

Bayesian Condition Monitoring in Neonatal Intensive Care

Institute for Adaptive and Neural ComputationThe observed physiological dynamics of an infant receiving intensive care contain a great deal of information about factors which cannot be examined directly, including the state of health of the infant and the operation of the monitoring equipment. This type of data tends to contain both common, recognisable patterns (e.g. as caused by certain clinical operations or artifacts) and some which are rare and harder to interpret. The problem of identifying the presence of these patterns using prior knowledge is clinically significant, and one which is naturally described in terms of statistical machine learning. In this thesis I develop probabilistic dynamical models which are capable of making useful inferences from neonatal intensive care unit monitoring data. The Factorial Switching Kalman Filter (FSKF) in particular is adopted as a suitable framework for monitoring the condition of an infant. The main contributions are as follows: (1) the application of the FSKF for inferring common factors in physiological monitoring data, which includes finding parameterisations of linear dynamical models to represent common physiological and artifactual conditions, and adapting parameter estimation and inference techniques for the purpose; (2) the formulation of a model for novel physiological dynamics, used to infer the times in which something is happening which is not described by any of the known patterns. EM updates are derived for the latter model in order to estimate parameters. Experimental results are given which show the developed methods to be effective on genuine monitoring data