Doctor of Philosophy

dissertationPatients sometimes suffer apnea during sedation procedures or after general anesthesia. Apnea presents itself in two forms: respiratory depression (RD) and respiratory obstruction (RO). During RD the patients' airway is open but they lose the drive to breathe. During RO the patients' airway is occluded while they try to breathe. Patients' respiration is rarely monitored directly, but in a few cases is monitored with a capnometer. This dissertation explores the feasibility of monitoring respiration indirectly using an acoustic sensor. In addition to detecting apnea in general, this technique has the possibility of differentiating between RD and RO. Data were recorded on 24 subjects as they underwent sedation. During the sedation, subjects experienced RD or RO. The first part of this dissertation involved detecting periods of apnea from the recorded acoustic data. A method using a parameter estimation algorithm to determine the variance of the noise of the audio signal was developed, and the envelope of the audio data was used to determine when the subject had stopped breathing. Periods of apnea detected by the acoustic method were compared to the periods of apnea detected by the direct flow measurement. This succeeded with 91.8% sensitivity and 92.8% specificity in the training set and 100% sensitivity and 98% specificity in the testing set. The second part of this dissertation used the periods during which apnea was detected to determine if the subject was experiencing RD or RO. The classifications determined from the acoustic signal were compared to the classifications based on the flow measurement in conjunction with the chest and abdomen movements. This did not succeed with a 86.9% sensitivity and 52.6% specificity in the training set, and 100% sensitivity and 0% specificity in the testing set. The third part of this project developed a method to reduce the background sounds that were commonly recorded on the microphone. Additive noise was created to simulate noise generated in typical settings and the noise was removed via an adaptive filter. This succeeded in improving or maintaining apnea detection given the different types of sounds added to the breathing data

Estimation of Surrogate Respiration and Detection of Sleep Apnea Events from Dynamic Data Mining of Multiple Cardiorespiratory Sensors

This research investigates an approach to derive respiration waveform from heart sound signals, and compare the waveform signal obtained thus with those obtained from alternative methods for deriving respiration waveforms from measured ECG signals. The investigations indicate that HSR can lead to a cost effective alternative to the use of respiratory vests to analyze cardiorespiratory dynamics for clinical diagnostics and wellness assessments. The derived respiratory rate was further used to classify Type III sleep apnea periods using recurrence analysis. Detection of patterns causing sleep apnea could open up opportunities to researchers to better understand and predict symptoms leading to disorders linked with sleep apnea like hypertension, sudden infant death syndrome, high blood pressure and a risk of heart attack. Surrogate respiratory signals derived from heart sounds (HSR) are found to have 32% and 36% correlation with the actual respiratory signals recorded at upright and supine positions, respectively, as compared to EMD derived respiration signals (EDR) that have (18% and 26%) correlation with the respiration waveforms measured in upright and supine positions, respectively. Wavelet-derived respiration (WDR) signals show a higher wave-to-wave correlation (55% and 55%) than HSR and EDR waveforms, but the respiratory sinus arrhythmia (RSA), zero crossing intervals, and respiratory rates of the HSR correlate better with the measured values, compared with those from EDR and WDR signals. Three models were implemented using recurrence analysis to classify sleep apnea events and were compared with a vectorized time series derived model. Advanced predictive modeling tools like decision trees, neural networks and regression models were used to classify sleep apnea events form non-apneic events. Model comparison within preliminary analysis model consisting of nasal respiration as well as its time lagged components and heart rate when compared with recurrence models shows that the preliminary analysis model(vectorized time series) has a lower misclassification rate (10%) than the recurrence models( Model 1: 20% Model 2: 14%, Model 3: 12%).Industrial Engineering & Managemen