Your browser doesn't support javascript. This means that the content or functionality of our website will be limited or unavailable. If you need more information about Vinnova, please contact us.

Clinical evaluation of a Smartphone based method for diagnosis of heart rhythm disorders

Reference number
Coordinator Danderyds sjukhus AB - Hjärtkliniken
Funding from Vinnova SEK 700 000
Project duration May 2019 - August 2020
Status Completed
Venture Medtech4Health innovators

Purpose and goal

The project aims to validate and study the use of smartphones to record heart rhythm instead of using traditional ECG recording. Patients who have undergone electrical cardioversion have recorded the heart rhythm in a home environment twice daily for 30 days and the compliance with the measurements has been very good, as well as the interest of the respondents to participate. The project has shown that it is possible for this patient group to use a smartphone with the aim of monitoring heart rhythm.

Expected results and effects

During the project, 280 patients who underwent electrical cardioversion for atrial fibrillation recorded their heart rythm both with the smartphone camera (PPG) and with ECG equipment for 60 seconds twice daily for 30 days, which generated over 18,000 recordings of both ECG and PPG which are to be compared for validation. The willingness to participate in the project has been very high among patients and the compliance to make registrations at home has been very high. The system has shown good opportunities for remote contact and direct feedback from health care staff to patient.

Planned approach and implementation

Patients who have undergone cardioversion at Danderyds Hospital have been asked to participate. The participants have been equipped with a smart phone that also has an ECG recorder connected. Participants performed simultaneous measurements with so-called photoplethysmography (PPG) and ECG for 60 seconds, which was performed twice daily for 30 days. PPG recordings will be validated against ECG recordings to determine the sensitivity and specificity of the method. Machine learning will also be used for PPG interpretation, in addition to manual interpretation by cardiologists

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 20 November 2020

Reference number 2019-01378

Page statistics