PHM for Biomedical Analytics: A Case Study on Neurophysiologic Data from Patients with Traumatic Brain Injury A thesis submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial fulfillment of the requirements for the Degree of Master of Science In the Department of Mechanical Engineering of the College of Engineering 2016 by Laura Pahren B.S. in Mechanical Engineering, The Ohio State University (2014) Committee Chair: Dr. Jay Lee Committee Member: Dr. Brandon Foreman Committee Member: Dr. Jay Kim i ABSTRACT Neurological data is the principal feedback for clinicians treating comatose patients in the Neuro-Intensive Care Unit (NICU), making this data critical in determining treatment, and hence patient outcomes. If this data is misinterpreted, patients can endure varying degrees of long term cognitive disabilities, or death. Therefore, understanding the signals themselves, their relationships to patient outcomes, and developing heterogeneous models for patient-specific modeling has become a key area of interest. This study has been conducted for 7 comatose patients, who have suffered traumatic brain injuries (TBI) and were treated in the University of Cincinnati’s Neuro ICU Department. The primary signals of interest were 15 channels of cortical depth electroencephalogram (EEG) and intracranial pressure (ICP). Data was collected within 12 to 24 hours of injury and for 48 to 72 hours after, with intermittent gaps. The aim of this project was to investigate the existence of an EEG and ICP signal relationship, develop a biomedical data cleaning protocol for the inclusion of future signals and determine prominent ICP thresholds in relation to EEG variables. After extracting various EEG features such as energy in key sub bands, Hjorth parameters, wavelet features and time domain statistics, data was classified into different mean peak ICP threshold ranges. These feature data sets are then central to determining whether varying ICP changes can be quantified based on the cortical EEG recordings and whether a common data element can be identified for deeper understanding of these signal relationships. Long term, by realizing the complex causal relationships of neurological data, ICP may be assessed via surface EEG, eliminating the need to drill into the skull and its associated risks. Moreover, further neurophysiological brain mapping can create knowledge that can enable more ii informed decision-making in ICP-moderating intervention to reduce secondary brain injuries. The criteria and future work vital to determining the details of this relationship are assessed after a comprehensive case study has been made to verify of the existence of an EEG/ICP relationship by modeling EEG variables in a neural networked-based self- organizing map (SOM). The accuracy of the clusters developed in the SOM are assessed using image processing techniques to estimate its ability to distinguish between the corresponding ICP values’ threshold adherence using external validity measures. Furthermore, to mitigate issues of dynamic brain states, the windows of time for the modeled data were determined from consistent segments of strong negatively or positively correlated ICP and cerebral blood flow values, which can be indicative of intracranial compliance, cerebral spinal fluid regulation and cerebral autoregulation. From this analysis, an average estimated external validity was determined to be 85.3%, which an estimated external validity high of 98.0%. These results lay the groundwork for further defining the exact nature of this ICP/EEG relationship for clinical use. iii iv ACKNOWLEGEMENTS I’d like to express my gratitude for the help and support of my committee chair, Dr. Jay Lee. I’d also like to thank Dr. Brandon Foreman for the incredible opportunity to collaborate with him in such a meaningful and impactful field of study, as well as for sharing his endless knowledge and insight. I would like to thank Dr. Jay Kim for his persistence in encouraging me with my studies. I could not have done it without Dr. Hossein Davari, who constantly guided throughout this project. Many thanks are also due to many members of the Center for Intelligent Maintenance Systems: She Zhi, Jin Chao, Matt Buzza, Yuan Di, Patrick Brown and any others who I may have forgotten. Finally, I would like to thank my family; my husband for his patience and support throughout this process, my parents for all the support throughout my education to get me to this point and my brothers who have always remained my greatest role models. v TABLE OF CONTENTS ABSTRACT ......................................................................................................................ii ACKNOWLEGEMENTS .................................................................................................. v TABLE OF CONTENTS ..................................................................................................vi LIST OF TABLES ........................................................................................................... x 1. INTRODUCTION ...................................................................................................... 1 1.1. Background ...................................................................................................... 1 1.2. Research Objectives ........................................................................................ 6 1.3. Thesis Layout ................................................................................................... 9 2. LITERATURE REVIEW .......................................................................................... 11 2.1. Sensor Systems in the Neuro Intensive Care Unit ...................................... 11 2.2. Clinical Studies and Interpretation of Neurophysiologic Signals .............. 15 2.3. Application of Data Analytic Tools ............................................................... 22 3. TECHNICAL APPROACH ..................................................................................... 33 3.1. Overview ......................................................................................................... 33 3.2. Data Collection ............................................................................................... 34 3.3. EEG Signal Analysis ...................................................................................... 35 3.3.1. EEG Data Cleaning ................................................................................... 36 3.3.2. EEG Signal Processing and Feature Extraction ........................................ 40 vi 3.4. ICP Signal Analysis ........................................................................................ 46 3.4.1. ICP Data Cleaning ..................................................................................... 46 3.4.2. ICP Signal Processing and Feature Extraction .......................................... 48 3.5. EEG Feature Selection ................................................................................... 51 3.6. Brain State Segmentation.............................................................................. 56 3.6.1. Cerebral Blood Flow Signal Processing .................................................... 56 3.6.2. Data Segmentation .................................................................................... 58 3.7. Pattern Recognition Using Self-Organizing Map ......................................... 60 4. RESULTS AND DISCUSSION ............................................................................... 63 4.1. ICP-EEG Relationships .................................................................................. 63 4.2. Data Cleaning Strategies ............................................................................... 73 5. CONCLUSIONS AND FUTURE WORK ................................................................. 76 5.1. Research Findings ......................................................................................... 76 5.2. Broader Impacts ............................................................................................. 77 5.3. Recommendations for Future Work ............................................................. 78 REFERENCES .............................................................................................................. 80 vii LIST OF FIGURES Figure 1: Data Collection Schematic ............................................................................. 12 Figure 2: X-ray Showing Sensor Set-up ........................................................................ 14 Figure 3: Hemodynamics Feedback Loop (Ursino & Lodi, 1997) .................................. 22 Figure 4: Adapted PHM-based Approach ...................................................................... 23 Figure 5: Relationship Model ......................................................................................... 24 Figure 6: Fuzzy Logic Example ..................................................................................... 25 Figure 7: Hierarchical Clustering ................................................................................... 27 Figure 8: K-means Example .......................................................................................... 29 Figure 9: ANN Layers .................................................................................................... 31 Figure 10: Overall Neurophysiologic Assessment Approach ......................................... 33