PhD Defense by Erik Reinertsen

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Erik Reinertsen

PhD Defense

Date: Tuesday, Aug 15, 2017

Time: 10 - 11 am

Location: ​Emory University School of Medicine room 170A


Committee members:

​- Gari Clifford, DPhil (advisor)
​- ​Shamim Nemati, PhD
​- ​Amit Shah, MD, MSCR
​- ​Eberhard Voit, PhD
​- ​Lee Cooper, PhD

​Title: Dichotomizing illness from cardiovascular and activity time series



​ ​

Digital sensors on smartphones and wearables measure huge quantities of time series data. Patterns in these da​ta - such as heart rate (HR) and locomotor activity -​ reflect changes in physiology and behavior​ associated with mental and/or cardiovascular illness​. However, most feature extraction approaches do not capture how ​noise and ​information varies with time scale, or is transferred between different signals​. This thesis explores the use of time domain, frequency domain, and complexity measures over several time scales to train machine learning algorithms to classify illness. First, a novel segmentation ​method based on periods of minimum heart rate is shown to improve the classification of post-traumatic stress disorder (PTSD) from healthy controls using heart rate variability metrics. Second, the relationship between analysis window length and classifier accuracy is evaluated in the context of using HR and activity features to dichotomize subjects with schizophrenia from healthy controls. Third, multiscale transfer entropy and network theoretical metrics of heart rate and activity are evaluated as complexity measures that contribute to the classification of atrial fibrillation and PSTD. This work ​contributes to the growing field of ​objective physiological ​sensing that ​could enable long-term ambulatory monitoring of clinically significant conditions.



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