On the prediction of clinical outcomes using Heart Rate Variability estimated from wearable devices

This thesis explores the use of Heart Rate Variability as a tool for predicting health outcomes, focusing on data derived from photoplethysmography (PPG) sensors in wrist-worn wearable devices such as smartwatches. These devices offer a unique opportunity for cost-effective, continuous, and unobtrusive monitoring of heart health. However, PPG data is susceptible to motion artefacts, challenging the reliability of Heart Rate Variability metrics derived from it.
A critical finding of this research is the unreliability of specific frequency-domain Heart Rate Variability features, such as the Sympathovagal Balance Index (SVI), due to low signal-to-noise ratio in certain frequency bands. Conversely, the thesis demonstrates that most HRV features, including Root Mean Square of Successive Differences between normal heartbeats (RMSSD) and Standard Deviation of Normal heartbeats (SDNN), can be reliably extracted under conditions of motion, such as during physical activity or recovery from exercise. This is achieved by employing accelerometry data from wearable devices to filter out unreliable PPG data.
The thesis also addresses the issue of missing data in Heart Rate Variability analysis, a consequence of motion artefacts and the energy-saving strategies of wearable devices. By exploring different interpolation methods and their effects on Heart Rate Variability features, this research identifies the best approaches for handling missing data. Particularly, it recommends operating on timestamp time-series over duration time-series, contradicting traditional Heart Rate Variability preprocessing practices. Quadratic interpolation in the time domain was identified as the most effective method, introducing minimal error across numerous Heart Rate Variability features, contrary to interpolation in the duration domain.
The research presented in this thesis evaluates Heart Rate Variability features derived from ultra-short measurement windows, demonstrating the feasibility of accurately estimating RMSSD and SDNN using 30-second and 1-minute time windows, respectively. This study, unique in assessing the effect of missing values on ultra-short Heart Rate Variability data, reveals that missing values significantly impact SDNN estimations while moderately affecting RMSSD. The analysis highlights that ultra-short inter-beat interval time series limit the assessment of very low frequency (VLF) components, increasing bias in SDNN estimates. This finding is particularly significant in light of the prevalent use of SDNN in commercial wearables, underscoring its importance for continuous heart health monitoring. The study notes that the shorter the measurement window and the greater the amount of missing values, the larger the bias observed in SDNN.
A novel aspect of the thesis is the creation of an innovative mathematical model designed to estimate the impact of circadian rhythms on resting heart rate. This model stands out for its computational efficiency, making it particularly suitable for data obtained from wearable devices. It surpasses the single component cosinor model in accuracy, demonstrated by a lower root mean square error (RMSE) in predicting future heart rate values. Additionally, it retains the advantage of providing easily interpretable parameters, such as MESOR, Acrophase, and Amplitude, which are essential for assessing changes in heart activity.
The thesis demonstrates that Heart Rate data can accurately estimate SDNN24 (the Standard Deviation of NN intervals over 24 hours), with a difference of about 0.22±11.47 (RMSE = 53.81 and r2=0.97
). This finding indicates that despite being fragmentary, 24-hour HR data from wrist-worn fitness devices is adequate for estimating SDNN24 and assessing health status, as evidenced by an F1 score of 0.97. The robustness of SDNN24 estimation against noisy data suggests that wrist-worn wearables are capable of reliably monitoring cardiovascular health on a continuous basis, thus facilitating early interventions in response to changes in Sinoatrial Node activity.
The final part of the thesis introduces an innovative approach to health outcome prediction, employing Heart Rate Variability data gathered during exercise alongside Electronic Health Record data. Employing Large Language Models to process EHR data and Convolutional AutoEncoders for Heart Rate Variability analysis, this approach reveals the untapped potential of exercise Heart Rate Variability data in health monitoring and prediction. Deep Learning models incorporating Heart Rate Variability data demonstrated enhanced predictive accuracy for cardiovascular diseases (CVD), coronary heart disease (CHD), and Angina, evidenced by higher Area Under the Curve (AUC) scores compared to models using only Electronic Health Records and demographic/behavioural data. The highest AUC scores achieved were 0.71 for CVD, 0.74 for CHD, and 0.73 for Angina.
In conclusion, this thesis contributes to the field of biomedical engineering by enhancing the understanding and application of HRV analysis in health outcome prediction using wearable device data. It offers insights for future work in continuous, unobtrusive health monitoring and underscores the need for further research in this rapidly evolving domain.

Keywords: HRV
circadian rhythm
EHR
ANS
PPG
Heart Rate Variability
wearable
CVD
SAN
deep learning
Advisor: Clifton, D