mCardia | A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening
Partners
Copenhagen Center for Health Technology (CACHET)
Technical University of Denmark, DTU Healthtech
Bispebjerg and Frederiksberg Hospital
Cortrium APS
Process
Literature Review, Co-Design Workshops, Requirement Analysis, Graphic Design, Mobile and Web App Design, Prototyping, Study Design, Technical Report
Heart-related diseases are the most common causes of mortality worldwide, causing 17.9 million deaths (nearly 31% of all global deaths) in the year 2016. Heart rhythm disorders, also known as arrhythmias, such as Atrial Fibrillation and Atrial Flutter are the most common causes of heart failure, hospitalization, and death related to cardiovascular diseases. Early diagnosis and treatment are important to provide timely treatment and prevent life-threatening heart conditions. The ECG is a standard, low-cost, non-invasive, and effective tool for the diagnosis and classification of heart rhythm disorders. Research suggests that continuous monitoring of ECG (Holter monitoring) with contextual information is essential to interpret heart rhythm disorders and predict heart-related diseases.
The goal of this research project was to mitigate some of the challenges of the current Holter monitoring systems, including (i) arrhythmia misinterpretation due to the lack of contextual information, (ii) lack of user engagement in longitudinal ECG collection, and (iii) recall bias on patient’s self-reported symptoms and events. To address these challenges, I designed mCardia – a context-aware ambulatory heart monitoring system.
Adopting the user-centered design approach, a series of interviews and co-design workshops with both patients and clinicians were conducted to develop a Requirement Analysis Document (RAD). According to the requirements, we designed the prototype of the mCardia system, which included (i) a patient-facing mobile app that enabled real-time reflection on cardiovascular data such as Heart Rate (HR), HR Variability, and MET level collected via a two-channel Holter device and contextual information, including sleep and step count and (ii) a web app that allowed clinicians to analyze and annotate the ECG data with the contextual information.
Publications
Devender Kumar, Raju Maharjan, Alban Maxhuni, Helena Dominguez, Anne Frølich, and Jakob E. Bardram. 2022. mCardia: A Context-Aware ECG Collection System for Ambulatory Arrhythmia Screening. ACM Health. DOI:https://doi.org/10.1145/3494581.
Raju Maharjan, Per Bækgaard, and Jakob E. Bardram. 2018. Leveraging Multi-modal User-labeled Data for Improved Accuracy in Interpretation of ECG Recordings. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp ’18). Association for Computing Machinery, New York, NY, USA, 636–641. DOI: https://doi.org/10.1145/3267305.3267548