53 research outputs found
Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery
The electrocardiogram or ECG has been in use for over 100 years and remains
the most widely performed diagnostic test to characterize cardiac structure and
electrical activity. We hypothesized that parallel advances in computing power,
innovations in machine learning algorithms, and availability of large-scale
digitized ECG data would enable extending the utility of the ECG beyond its
current limitations, while at the same time preserving interpretability, which
is fundamental to medical decision-making. We identified 36,186 ECGs from the
UCSF database that were 1) in normal sinus rhythm and 2) would enable training
of specific models for estimation of cardiac structure or function or detection
of disease. We derived a novel model for ECG segmentation using convolutional
neural networks (CNN) and Hidden Markov Models (HMM) and evaluated its output
by comparing electrical interval estimates to 141,864 measurements from the
clinical workflow. We built a 725-element patient-level ECG profile using
downsampled segmentation data and trained machine learning models to estimate
left ventricular mass, left atrial volume, mitral annulus e' and to detect and
track four diseases: pulmonary arterial hypertension (PAH), hypertrophic
cardiomyopathy (HCM), cardiac amyloid (CA), and mitral valve prolapse (MVP).
CNN-HMM derived ECG segmentation agreed with clinical estimates, with median
absolute deviations (MAD) as a fraction of observed value of 0.6% for heart
rate and 4% for QT interval. Patient-level ECG profiles enabled quantitative
estimates of left ventricular and mitral annulus e' velocity with good
discrimination in binary classification models of left ventricular hypertrophy
and diastolic function. Models for disease detection ranged from AUROC of 0.94
to 0.77 for MVP. Top-ranked variables for all models included known ECG
characteristics along with novel predictors of these traits/diseases.Comment: 13 pages, 6 figures, 1 Table + Supplemen
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Real-world heart rate norms in the Health eHeart study.
Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18-45, ≤100 in those aged 45-60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
We train and validate a semi-supervised, multi-task LSTM on 57,675
person-weeks of data from off-the-shelf wearable heart rate sensors, showing
high accuracy at detecting multiple medical conditions, including diabetes
(0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep
apnea (0.8298). We compare two semi-supervised train- ing methods,
semi-supervised sequence learning and heuristic pretraining, and show they
outperform hand-engineered biomarkers from the medical literature. We believe
our work suggests a new approach to patient risk stratification based on
cardiovascular risk scores derived from popular wearables such as Fitbit, Apple
Watch, or Android Wear.Comment: Presented at AAAI 201
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