Machine Learning-Empowered Smart Health Wearables for Long-Term High-Resolution Heart Rate and Blood Pressure Tracking

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Machine Learning-Empowered Smart Health Wearables for Long-Term High-Resolution Heart Rate and Blood Pressure Tracking

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Title: Machine Learning-Empowered Smart Health Wearables for Long-Term High-Resolution Heart Rate and Blood Pressure Tracking
Author(s):
Zhang, Qingxue
Advisor: Zhou, Dian
Date Created: 2017-05
Format: Dissertation
Keywords: Show Keywords
Abstract: Wearable computers are paving a promising way for ubiquitous smart health applications. Longterm high-resolution heart rate and blood pressure tracking is highly significant not only for medical purposes such as heart diseases and hypertension management, but also for wellness and fitness applications such as emotion, stress and sport performance monitoring. It is highly challenging to robustly estimate the high-resolution (second-level) heart rate in longterm application scenarios, due to motion artifacts caused by diverse body movements. We propose two novel electrocardiogram (ECG)-based instantaneous heart rate monitoring frameworks tolerant to severe and continuous motion artifacts. The first framework is phase-domain multiview dynamic time warping, which transforms the raw ECG signal to the multi-dimensional phase space to reveal more geometrical characteristics of heartbeats, and then performs multi-view dynamic time warping to identify heartbeats similar to a predefined heartbeat template. The heart rate estimation performance greatly outperforms previously reported approaches. The second framework is a two-stage classification approach, which does not sweep all of the signal stream to find out heartbeats, but instead, just focuses on the signal spikes of interest to classify them as heartbeats or interferential spikes. Since it does not care about the non-spike signal segments, it consumes less computation load. We extract twenty-six features and select out ten features critical to motion artifacts, by a spare support vector machine (SVM). Then they are used to train a SVM model to perform the heartbeat identification. Afterwards, a refinement engine is introduced to purify the heartbeats. The performance is superior to many well-known approaches. Moreover, we propose two wearable cuff-less blood pressure monitoring systems, one with single-arm ECG and photoplethysmogram (PPG) signals and another with ear-ECG and PPG signals. In the first system, the weak single-arm signals are successfully acquired by our bio-potential acquisition platform, and the heartbeats are then identified from these weak signals. A thorough comparative analysis on diverse blood pressure models is also performed to determine an appropriate one for single-arm applications. Experimental results show that the system can robustly estimate the minute-level blood pressure only based on single-arm signals. In the second system, both weak ECG and PPG signals are also successfully acquired by placing all the sensors behind the ear for a super wearability. Moreover, we introduce large amounts of motion artifacts by performing head movements towards practical application scenarios, and then propose an unsupervised learning strategy to automatically evaluate the signal distortion level and perform signal purification. Experimental results also show the effectiveness of the proposed ear-worn blood pressure and heart rate monitoring system. These smart health wearables proposed are expected to contribute to pervasive health, wellness and fitness management.
Degree Name: PHD
Degree Level: Doctoral
Persistent Link: http://hdl.handle.net/10735.1/5775
Terms of Use: ©2017 The Author. Digital access to this material is made possible by the Eugene McDermott Library. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Type : text
Degree Program: Electrical Engineering

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