An automated approach: from physiological signals classification to signal processing and analysis

By increased and widespread usage of wearable monitoring devices a huge volume of data is generated which requires various automated methods for analyzing and processing them and also extracting useful information from them. Since it is almost impossible for physicians and nurses to monitor physical activities of their patients for a long time, there is a need for automated data analysis techniques that abstract the information and highlight the significant events for clinicians and healthcare experts. The main objective of this thesis work was towards an automatic digital signal processing approach from physiological signal classification to processing and analyzing the two most vital physiological signals in long-term healthcare monitoring (ECG and IP). At the first stage, an automated generic physiological signal classifier for detecting an unknown recorded signal was introduced and then different algorithms for processing and analyzing the ECG and IP signals were developed and evaluated. This master thesis was a part of DISSE project which its aim was to design a new health-care system with the aim of providing medical expertise more accessible, affordable, and convenient. In this work, different publicly available databases such as MIT-BIH arrhythmia and CEBS were used in the development and evaluation phases. The proposed novel generic physiological signal classifier has the ability to distinguish five types of physiological signals (ECG, Resp, SCG, EMG and PPG) from each other with 100 % accuracy. Although the proposed classifier was not very successful in distinguishing lead I and II of ECG signal from each other (error of 27% was reported) which means that the general purpose features were enough discriminating to recognize different physiological signals from each other but not enough for classifying different ECG leads. For ECG processing and analysis section, three QRS detection methods were implemented which modified Pan-Tompkins gave the best performance with 97% sensitivity and 96,45% precision. The morphological based ectopic detection method resulted in sensitivity of 85,74% and specificity of 84,34%. Furthermore, for the first PVC detection algorithm (sum of trough) the optimal threshold and range were studied according to the AUC of ROC plot which the highest sensitivity and specificity were obtained with threshold of −5 and range of 11 : 25 that were equal to 87% and 82%, respectively. For the second PVC detection method (R-peak with minimum) the best performance was achieved with threshold of −0.7 that resulted in sensitivity of 68% and specificity of 72%. In the IP analysis section, an ACF approach was implemented for respiratory rate estimation. The estimated respira- tion rate obtained from IP signal and oronasal mask were compared and the total MAE and RMSE errors were computed that were equal to 0.40 cpm and 1.20 cpm, respectively. The implemented signal processing techniques and algorithms can be tested and improved with measured data from wearable devices for ambulatory applications

Counterpulsation cardiac assist device controller defection filter simulation and canine experiments

Electronic control systems for counterpulsation Cardiac Assist Devices (CADs) are an essential part of cardiac assistance. Synchronization of the counterpulsation CAD controller with the cardiac cycle is critical to the efficacy of the CAD. The robustness of counterpulsation CAD controllers varies with the ability of the CAD controller to properly trigger on aortic pressure (Pa) and electrocardiogram (ECG) signals for sinusoid rhythms, non-sinusoid rhythms and non-ideal signals resulting from surgical intervention. An analog-to-digital converter and digital-to-analog converter based CAD controller development platform was devised on a 33Mhz PC-AT. Counterpulsation Pa systolic rise and dicrotic notch detectors were demonstrated with a 15cc pediatric Intraaortic Balloon (IAB) and 50cc Extraaortic Counterpulsation Device (EACD) CADs using mongrel canine experimental models in which biological variation due to changing heart rate and arrhythmia as well as surgical interference due to mechanical ventilation, electrocautery, signal attenuation and random noise was present. The robust Pa triggering algorithm was based on a derivative comparator riding clipper algorithm for the Pa-based controller. In order to empirically determine the robustness of the Pa triggering algorithms, a simulation platform, Pa trace model, and Pa trace artifact and physiological variation models were devised. Each set of simulation experiments utilized a different Pa trace artifact or physiological variation model to determine the capability of the Pa trigger algorithm to withstand the effects of the Pa detection impediments while maintaining 100% accuracy of the dicrotic notch detection. Multiple simulation experiments were conducted in which the same nominally adjusted interference was increased to benchmark the immunity threshold of the dicrotic notch detector. Biological variation and deviations in Pa artifacts due to clinical conditions experienced in cardiothoracic surgery were investigated. Pa triggering was unhindered by biological variation of a Pa trace with a 3 mmHg dicrotic notch deflection along with a Pa trace with no dicrotic notch deflection present. Pa triggering was unhindered by heart rate variability ranging from 60 to 80 bpm due to respiration. Pa triggering was unhindered by clinical conditions including 40 mmHg changes in the Pa baseline modeling mechanical ventilation, aortic trace attenuation modeling variations in pressure transducer positioning and blood coagulation on the pressure catheter tip ranging from 100% to 200% of the Pa trace amplitude every four seconds, uniformly distributed noise with a mean of 0.5mmHg and standard deviation of 0.289mmHg and Gaussian distributed noise with a zero mean and standard deviation of 0.6nunHg. The results of the simulation experiments performed quantified the robustness of the Pa detection algorithm. Development of a fault tolerant counterpulsation CAD control system required the development of a robust ECG triggering algorithm to operate in tandem with the Pa triggering algorithm. An ECG detector was developed to provide robust control for a range of ECG traces due to biological variation and signal interference. The ECG R-wave detection algorithm is based on a modified version of the Washington University QRS-complex DD/1 algorithm (Detection and Delineation 1) which uses the associated AZTEC (Amplitude Zero Threshold Epic Coding) preprocessing algorithm and provides accurate ECG-based CAD control R-wave detection for 96.56% of the R-waves stored within the MIT/BIH ECG Arrhythmia database with a maximum detection delay of 8 milliseconds. Further IAB experiments performed with mongrel canine experimental models demonstrated that the systolic time interval to heart rate relationship existing in humans (essential to human patient CAD control inflation prediction) is not prevalent in canine mongrels particularly when treated with beta-blockers. In order to execute both Pa and ECG C software detection algorithms for a fault tolerant counterpulsation CAD controller, investigation into the communications throughput of a quad-transputer board was performed. Development of streamlined communication primitives led to a communication processor utilization of 8.3%, deemed efficient enough for fault tolerant multiprocessor CAD control implementation

Morphological Variability Analysis of Physiologic Waveform for Prediction and Detection of Diseases

For many years it has been known that variability of the morphology of high-resolution (∼30-1000 Hz) physiological time series data provides additional prognostic value over lower resolution (≤ 1Hz) derived averages such as heart rate (HR), breathing rate (BR) and blood pressure (BP). However, the field has remained rather ad hoc, based on hand-crafted features. Using a model-based approach we explore the nature of these features and their sensitivity to variabilities introduced by changes in both the sampling period (HR) and observational reference frame (through breathing). HR and BR are determined as having a statistically significant confounding effect on the morphological variability (MV) evaluated in high-resolution physiological time series data, thus an important gap is identified in previous studies that ignored the effects of HR and BR when measuring MV. We build a best-in-class open-source toolbox for exploring MV that accounts for the confounding factors of HR and BR. We demonstrate the toolbox’s utility in three domains on three different signals: arterial BP in sepsis; photoplethysmogram in coarctation of the aorta; and electrocardiogram (ECG) in post-traumatic stress disorder (PTSD). In each of the three case studies, incorporating features that capture MV while controlling for BR and/or HR improved disease classification performance compared to previously established methods that used features from lower resolution time series data. Using the PTSD example, we then introduce a deep learning approach that significantly improves our ability to identify the effects of PTSD on ECG morphology. In particular, we show that pre-training the algorithm on a database of over 70,000 ECGs containing a set of 25 rhythms, allowed us to boost performance from an area under the receiver operating characteristic curve (AUROC) of 0.61 to 0.85. This novel approach to identifying morphology indicates that there is much more to morphological variability during stressful PTSD-related events than the simple periodic modulation of the T-wave amplitude. This research indicates that future work should focus on identifying the etiology of the dynamic features in the ECG that provided such a large boost in performance, since this may reveal novel underlying mechanisms of the influence of PTSD on the myocardium.Ph.D