Heart rate, a commonly accessible health data from
most wearables, carries rich information of a person’s well-being,
yet remains of limited deep health applications, due to the lack
of groundtruth of health events and their impact on heart rate
patterns. Specifically, standard health analytics usually are designed
based on well-modeled health conditions thus known data
patterns and rich training data. To bridge the gap, we propose
HeartInsightify, an exploratory framework that facilitates the
process of deriving health-relevant measurable indicators from
longitudinal heart rate data, without any of the above knowledge.
HeartInsightify focuses on comparative and qualitative study,
using model-free statistical methods such as conformal prediction,
to study similarities, perform clustering and detect outliers, and
build multi-resolutional data summaries, allowing human experts
to efficiently examine and verify their health relevance. We
conduct extensive experiments to evaluate HeartInsightify using
individuals’ free-living heart rate data collected through Fitbit
over 6 years. We illustrate the process of analyzing heart rate
data for its health relevance and demonstrate the effectiveness
of HeartInsightify. We envision that HeartInsightify lays the
groundwork for personalized health analytics with continuous
monitoring data from wearables.
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This content will become publicly available on December 1, 2024
DATA ANALYTICS FOR HEALTH-RELEVANT EVENTS DETECTION BASED UPON LONGITUDINAL FITBIT HEART RATE DATA
The increasing prevalence of wearable devices enables low-cost, long-term collection of health relevant data such as heart rate, exercise, and sleep signals. Currently these data are used to monitor short term changes with limited interpretation of their relevance to health. These data provide an untapped resource to monitor daily and long-term activity patterns. Changes and trends identified from such data can provide insights and guidance to the management of many chronic conditions that change over time. In this study we conducted a machine learning based analysis of longitudinal heart rate data collected over multiple years from Fitbit devices. We built a multi-resolutional pipeline for time series analysis, using model-free clustering methods inspired by statistical conformal prediction framework. With this method, we were able to detect health relevant events, their interesting patterns (e.g., daily routines, seasonal differences, and anomalies), and correlations to acute and chronic changes in health conditions. We present the results, lessons, and insights learned, and how to address the challenge of lack of labels. The study confirms the value of long-term heart rate data for health monitoring and surveillance, as complementary to extensive yet intermittent examinations by health care providers.
more » « less- NSF-PAR ID:
- 10499617
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Innovation in Aging
- Volume:
- 7
- Issue:
- Supplement_1
- ISSN:
- 2399-5300
- Page Range / eLocation ID:
- 873 to 873
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Objective The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.
Methods In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology.
Results Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal.
Significance Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.
