Identification of sympathetic and parasympathetic nerves function in cardiovascular regulation using ANFIS approximation

Journal of Dynamic Systems, Measurement, and Control

Rehman

Lingappan

et al. 2016

Self Cite

This paper is concerned with the mathematical modeling and detection of endotracheal (ET) intubation in children under general anesthesia during surgery. In major pediatric surgeries, the airway is often secured with an endotracheal tube (ETT) followed by initiation of mechanical ventilation. Clinicians utilize auscultation of breath sounds and capnography to verify correct ETT placement. However, anesthesia providers often delay timely charting of ET intubation. This latency in event documentation results in decreased efficacy of clinical decision support systems. In order to target this problem, we collected real inpatient data and designed an algorithm to accurately detect the intubation time within the clinically valid range; the results show that we are able to achieve high accuracy in more than 96% of the cases. Automatic detection of ET intubation time would thus enhance better real-time data capture to support future improvement in clinical decision support systems.

Section: Resultsmentioning

confidence: 73%

Automatic Detection of Endotracheal Intubation During the Anesthesia Procedure

Journal of Dynamic Systems, Measurement, and Control

Rehman

Lingappan

et al. 2016

Self Cite

“…The CI techniques include data mining algorithms and techniques like decision tree (DT) [37], [39], neural networks (NN) [19], support vector machine (SVM) [35], [36], [40], adaptive neuro-fuzzy inference system (ANFIS) [34], [41], etc. Among the many different CI techniques, we have chosen DT for the task of PVL prediction in the current study.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Periventricular Leukomalacia Occurrence in Neonates After Heart Surgery

IEEE J. Biomed. Health Inform.

Buckley

Lynch

et al. 2014

Self Cite

This paper is concerned with predicting the occurrence of Periventricular Leukomalacia (PVL) using vital and blood gas data which are collected over a period of twelve hours after neonatal cardiac surgery. A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected. In view of the fact that blood gas and vital data have different sampling rates, in this study we have divided the data into two categories: (i) high resolution (vital), and (ii) low resolution (blood gas), and designed a separate classifier based on each data category. The developed algorithm is composed of several stages; first, a feature pool has been extracted from each data category and the extracted features have been ranked based on the data reliability and their mutual information content with the output. An optimal feature subset with the highest discriminative capability has been formed using simultaneous maximization of the class separability measure and mutual information of a set. Two separate decision trees (DT) have been developed for the classification purpose and more importantly to discover hidden relationships that exist among the data to help us better understand PVL pathophysiology. The DT result shows that high amplitude twenty minute variations and low sample entropy in the vital data and the defined out of range index as well as maximum rate of change in blood gas data are important factors for PVL prediction. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of data collection, we show that the first six hours of data contain sufficient information for PVL occurrence prediction.

“…Data mining facilitates data exploration using data analysis methods with sophisticated algorithms in order to discover unknown patterns. The CI techniques include data mining algorithms and techniques such as decision tree (DT) [14]–[16], artificial neural networks (ANNs) [17], support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) [18]. …”

Section: Introductionmentioning

confidence: 99%

Application of decision tree in the prediction of periventricular leukomalacia (PVL) occurrence in neonates after heart surgery

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Licht

Nataraj

2012

This paper is concerned with the prediction of the occurrence of Periventricular Leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after the cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. The decision tree classification technique has been selected as a tool for prediction of the PVL because of its capacity for discovering rules and novel associations in the data. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected from Children's Hospital of Philadelphia (CHOP) are used for this study. Vital data contain heart rate (HR), mean arterial pressure (MAP), right atrium pressure (RAP), blood hemoglobin (Hb), hemoglobin oxygen content (HbO2), oxygen saturation (SpO2) and relative cerebral blood flow (rCBF). Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and min and max of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in blood gas data, maximum rate of change in the concentration of bicarbonate ions in blood (HCO3) and minimum rate of change in the partial pressure of dissolved CO2 in the blood (PaCO2) are the most important factors for prediction of the PVL. Among vital features the kurtosis of HR and Hb are the most important parameters.