Machine Learning Enabled Time Series Analysis in Medicine

Study Overview
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Brief Summary
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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: 50 with heart failure 50 with atrial fibrillation 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.

Official Title
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Pattern Recognition in Heart Rate Variability Using Fitness Trackers in Cardiovascular Disease

Conditions
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Atrial Fibrillation, Heart Failure, Systolic

Intervention / Treatment
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* Device: fitness tracker


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Heart Failure<br>Study participants with systolic heart failure (Left ventricular ejection fraction < 35%) without documented atrial fibrillation | Device: fitness tracker<br>* Study subjects will wear a Fitbit fitness tracker<br>* Other names: Fitbit Charge 5, Fitbit Inspire 2;|
| Atrial Fibrillation<br>Study participants with documented atrial fibrillation without heart failure | Device: fitness tracker<br>* Study subjects will wear a Fitbit fitness tracker<br>* Other names: Fitbit Charge 5, Fitbit Inspire 2;|
| Reference<br>Individuals without cardiovascular disease | Device: fitness tracker<br>* Study subjects will wear a Fitbit fitness tracker<br>* Other names: Fitbit Charge 5, Fitbit Inspire 2;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cardiovascular disease detection with an AI algorithm | adequate sensitivity/specificity in an algorithm to detect atrial fibrillation and heart failure | Three months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Detection of absence of cardiovascular disease |  | Three months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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cardiovascular disease, Artificial intelligence, machine learning