Machine Learning Enabled Time Series Analysis in Medicine

HagaZiekenhuis200 enrolled

Overview

The goal of this observational cohort study is to investigate the potential of fitness trackers in combination with machine learning algorithms to identify cardiovascular disease specific patterns. Two hundred participants will be enrolled: 1. 50 with heart failure 2. 50 with atrial fibrillation 3. 100 (healthy) individuals without the former two conditions All participants are given a Fitbit device and monitored for three months. Researchers will compare differences in heart rate variability patterns between the groups and devise a machine learning algorithm to detect these patterns automatically.

Study Type

OBSERVATIONAL

Enrollment

200

Conditions

Atrial Fibrillation Heart Failure, Systolic

Interventions

fitness trackerDEVICE

Study subjects will wear a Fitbit fitness tracker

Medical Language ↔ Plain English

Inclusion Criteria: * systolic heart failure (LVEF \< 35%) * Atrial fibrillation without heart failure * Individuals without cardiovascular disease Exclusion Criteria: * \> 85 years old * Recent pulmonary venous antrum isolation procedure (\<1 year) * (end stage) kidney failure * (end stage) liver failure * Study participants with known systemic active inflammatory disease * Study participants with impaired mental state * Inability to use a fitness tracker or mobile phone * Impaired cognition and inability to understand the study protocol

Outcomes

Primary Outcomes

Cardiovascular disease detection with an AI algorithm

adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure

Time frame: Three months

Secondary Outcomes

Detection of absence of cardiovascular disease