Machine learning prediction of Heart failure and Arrythmia using the mobile self-monitoring ECG devices

Machine learning prediction of Heart failure and Arrythmia using the mobile self-monitoring ECG devices - Machine learning prediction of Heart failure and Arrythmia using the mobile self-monitoring ECG devices

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000042073

Enrollment

500

Registered

2020-10-11

Start date

2020-06-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Heart failure, Arrhythmia

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Adult patients with heart failure and healthy individuals,20 years old or older

Exclusion criteria

Exclusion criteria: Individual under 20 years

Design outcomes

Primary

Measure	Time frame
Onset of heart failure Onset of arrhythmia (atrial fibrillation)	—

Countries

Japan

Contacts

Public ContactEriko Hasumi

University of Tokyo Department of Cardiovascular Medicine

ehasumi-circ@umin.ac.jp0338155411

Outcome results