CardioHelp: A smartphone application for beat-by-beat ECG signal analysis for real-time cardiac disease detection using edge-computing AI classifiers

Ucchwas Talukder Utsha, Bashir I. Morshed

Research output: Contribution to journalArticlepeer-review

Abstract

Cardiovascular diseases are a leading cause of morbidity and mortality worldwide. To diagnose cardiac diseases, physicians often utilize a combination of medical history, physical examination, and several diagnostic tests, such as electrocardiograms (ECG/EKG), echocardiograms, and stress tests. Early detection and effective management of cardiac diseases play a crucial role in improving patient outcomes and reducing healthcare burden. To address this concern, we introduce a novel edge-computing approach for cardiac healthcare using a smartphone application (CardioHelp) that combines heart rate monitoring with the detection of abnormal heartbeats in individuals. Our approach centers around a user-friendly smart-health application designed to visualize ECG signals, track and monitor heart rate continuously, and recognize and notify users of any anomalies through advanced beat-by-beat ECG analysis algorithms and artificial intelligence (AI) techniques including machine learning and deep learning. Our system includes a custom wearable ECG data collection system that can transfer data to CardioHelp in real-time. In this study, we have used the MIT-BIH Arrhythmia dataset to train deep learning models using intricate patterns and features representative of various heart conditions. Among the deep learning models, the Long Short-Term Memory (LSTM) demonstrated superior performance, obtaining an accuracy of 98.74% and precision and recall of 99.95% and 99.86%, respectively. By transferring the MIT-BIH Arrhythmia Database's test dataset through our application as mock real-time data, we assessed our CardioHelp application's accuracy in identifying and classifying various heart conditions. The LSTM model is found to be the most accurate model providing an accuracy of 95.94% for ECG beat classification. The results confirmed the effectiveness of our developed smartphone system, demonstrating its ability to accurately detect and classify cardiac conditions. As our novel approach presents a complimentary cardiac healthcare system using a smart health application, this CardioHelp has the potential to significantly enhance preventive care, enable early intervention, and improve overall cardiovascular health outcomes.

Original languageEnglish
Article number100446
JournalSmart Health
Volume31
DOIs
StatePublished - Mar 2024

Keywords

  • CardioHelp
  • Deep learning
  • Edge computing
  • Electrocardiograms
  • Long Short-Term Memory (LSTM)
  • Pre-trained model
  • Real-time cardiac monitoring

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