Carlos A. Robles-Rubio

Automated Off-Line Respiratory Event Detection for the Study of Postoperative Apnea in Infants

Previously, we presented automated methods for thoraco-abdominal asynchrony estimation and movement artifact detection in respiratory inductance plethysmography (RIP) signals. This paper combines and improves these methods to give a method for the automated, off-line detection of pause, movement artifact, and asynchrony. Simulation studies demonstrated that the new combined method is accurate and robust in the presence of noise. The new procedure was successfully applied to cardiorespiratory signals acquired postoperatively from infants in the recovery room. A comparison of the events detected with the automated method to those visually scored by an expert clinician demonstrated a higher agreement (κ = 0.52) than that amongst several human scorers (κ = 0.31) in a clinical study . The method provides the following advantages: first, it is fully automated; second, it is more efficient than visual scoring; third, the analysis is repeatable and standardized; fourth, it provides greater agreement with an expert scorer compared to the agreement between trained scorers; fifth, it is amenable to online detection; and lastly, it is applicable to uncalibrated RIP signals. Examples of applications include respiratory monitoring of postsurgical patients and sleep studies.

Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability

The majority of extreme preterm infants require endotracheal intubation and mechanical ventilation (ETT-MV) during the first days of life to survive. Unfortunately this therapy is associated with adverse clinical outcomes and consequently, it is desirable to remove ETT-MV as quickly as possible. However, about 25% of extubated infants will fail and require re-intubation which is also associated with a 5-fold increase in mortality and a longer stay in the intensive care unit. Therefore, the ultimate goal is to determine the optimal time for extubation that will minimize the duration of MV and maximize the chances of success. This paper presents a new objective predictor to assist clinicians in making this decision. The predictor uses a modern machine learning method (Support Vector Machines) to determine the combination of measures of cardiorespiratory variability, computed automatically, that best predicts extubation readiness. Our results demonstrate that this predictor accurately classified infants who would fail extubation.

Automated unsupervised respiratory event analysis

We recently presented a comprehensive automated off-line method for supervised respiratory event classification from uncalibrated respiratory inductive plethysmography signals. This method required training with a sample of clinical measurements classified by an expert. This human intervention is labor intensive and involves subjective judgments that may introduce bias to the automated classification. To address this we developed a novel method for unsupervised respiratory event classification, named AUREA (Automated Unsupervised Respiratory Event Analysis). This paper describes the algorithm underlying AUREA and demonstrates its successful application to respiratory signals acquired from infants in the postoperative recovery room. The advantages of AUREA are: first, it provides real-time classification of respiratory events; second, it requires no human intervention; and lastly, it has substantially better performance than the supervised method.

Automated analysis of respiratory behavior in extremely preterm infants and extubation readiness.

Rates of extubation failure of extremely preterm infants remain high. Analysis of breathing patterns variability during spontaneous breathing under endotracheal tube continuous positive airway pressure (ETT‐CPAP) is a potential tool to predict extubation readiness.

Automated analysis of respiratory behavior for the prediction of apnea in infants following general anesthesia

Infants recovering from general anesthesia are at risk of postoperative apnea (POA), a potentially life threatening event. There is no accurate way to identify which infants will experience POA, and thus all infants with postmenstrual age <; 60 weeks are monitored for apnea in hospital postoperatively. Using a comprehensive, automated analysis of the postoperative breathing patterns, we identified the occurrence of respiratory pauses in 24 infants at age risk for POA. We determined the POA category for each infant by using K-medoids to cluster the duration of the longest respiratory pause. Two clusters were identified, corresponding to APNEA and NO-APNEA, with a threshold of 14.6 s, a value consistent with the clinically accepted threshold of 15 s. K-medoids derived POA labels were used to evaluate the predictive ability of demographic and anesthetic management variables. Weight and the intraoperative doses of atropine, propofol, and opioids discriminated between the APNEA and NO-APNEA groups. A linear Gaussian discriminant analysis classifier provided a very good classification with a probability of detection PD = 0.73 and a probability of false alarm PFA = 0.22. This approach provides a promising tool for the systematic, objective study of infants at risk of POA.

A new movement artifact detector for photoplethysmographic signals

Oximeters are commonly used in abbreviated cardiorespiratory studies (ACS) to monitor blood oxygen saturation and heart rate using the photoplethysmography (PPG) signal. These data are prone to movement artifacts, especially in infants who move or need to be handled often. Therefore segments of PPG data contaminated by movement artifact must be detected as a first stage of analysis. In ACS this identification is generally done manually, by having an expert visually assess the quality of the signal. This is subjective and very time consuming, especially for long data records. For this reason we present a novel detector of PPG movement artifacts that uses moving average filters to remove trends, reduce the effect of white noise, and notch filter pulse-related information. The normalized root mean square of the filtered signal is then used as a detection statistic. We demonstrate its detection properties using a data set from infants recovering from anesthesia, and show that it performs better than other automated methods based on entropy or higher-order statistics. Furthermore, the new method is more robust than the other methods in the presence of large noise.

Organizational principles of cloud storage to support collaborative biomedical research

This paper describes organizational guidelines and an anonymization protocol for the management of sensitive information in interdisciplinary, multi-institutional studies with multiple collaborators. This protocol is flexible, automated, and suitable for use in cloud-based projects as well as for publication of supplementary information in journal papers. A sample implementation of the anonymization protocol is illustrated for an ongoing study dealing with Automated Prediction of EXtubation readiness (APEX).

Detection of breathing segments in respiratory signals

The typical approach for analysis of respiratory records consists of detection of respiratory pauses and elimination of segments corrupted by movement artifacts. This is motivated by established rules used for manual scoring of respiratory events, which focus on pause segmentation and do not define criteria to identify breathing segments. With this strategy, breathing segments can only be inferred indirectly from the absence of abnormalities, yielding an unclear and ambiguous definition. In this work we present novel detectors for synchronous and asynchronous breathing, and compare them with AUREA, a novel system for Automated Unsupervised Respiratory Event Analysis, which performs indirect classification of breathing. Results from analysis of real infant respiratory data show an improvement in the identification of synchronous and asynchronous breathing of 9% and 27% respectively, demonstrating that direct detection of breathing enhances the classification performance.

Scoring Tools for the Analysis of Infant Respiratory Inductive Plethysmography Signals.

Infants recovering from anesthesia are at risk of life threatening Postoperative Apnea (POA). POA events are rare, and so the study of POA requires the analysis of long cardiorespiratory records. Manual scoring is the preferred method of analysis for these data, but it is limited by low intra- and inter-scorer repeatability. Furthermore, recommended scoring rules do not provide a comprehensive description of the respiratory patterns. This work describes a set of manual scoring tools that address these limitations. These tools include: (i) a set of definitions and scoring rules for 6 mutually exclusive, unique patterns that fully characterize infant respiratory inductive plethysmography (RIP) signals; (ii) RIPScore, a graphical, manual scoring software to apply these rules to infant data; (iii) a library of data segments representing each of the 6 patterns; (iv) a fully automated, interactive formal training protocol to standardize the analysis and establish intra- and inter-scorer repeatability; and (v) a quality control method to monitor scorer ongoing performance over time. To evaluate these tools, three scorers from varied backgrounds were recruited and trained to reach a performance level similar to that of an expert. These scorers used RIPScore to analyze data from infants at risk of POA in two separate, independent instances. Scorers performed with high accuracy and consistency, analyzed data efficiently, had very good intra- and inter-scorer repeatability, and exhibited only minor confusion between patterns. These results indicate that our tools represent an excellent method for the analysis of respiratory patterns in long data records. Although the tools were developed for the study of POA, their use extends to any study of respiratory patterns using RIP (e.g., sleep apnea, extubation readiness). Moreover, by establishing and monitoring scorer repeatability, our tools enable the analysis of large data sets by multiple scorers, which is essential for longitudinal and multicenter studies.

Correlation of clinical parameters with cardiorespiratory behavior in successfully extubated extremely preterm infants

Extremely preterm infants (gestational age ≤ 28 weeks) often require EndoTracheal Tube-Invasive Mechanical Ventilation (ETT-IMV) to survive. Clinicians wean infants off ETT-IMV as early as possible using their judgment and clinical information. However, assessment of extubation readiness is not accurate since 20 to 40% of preterm infants fail extubation. We extended our work in automated prediction of extubation readiness by examining correlations of automated cardiorespiratory features to clinical parameters in successfully extubated infants. Only a few features, mainly those related to variability of breathing synchrony, had any consistent correlation with clinical parameters, namely gestational age, day of life at extubation, and bicarbonate. We conclude that the automated cardiorespiratory features likely provide different information additional to clinical practice.