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Published on: December 11, 2019
Real Time Recognition of Heart Attack in a Smart Phone
Mahshid Zomorodi Rad1, Saeed Rahati Ghuchani2, Kambiz Bahaadinbeigy3
1Department of Biomedical Engineering, Islamic Azad University of Mashhad, Mashhad, Iran.
Insights
This study presents a novel smartphone algorithm for real-time myocardial infarction detection using ECG signals. The algorithm achieves high accuracy, enabling rapid diagnosis and patient notification for timely medical intervention.
Area of Science:
- Biomedical Engineering
- Cardiology
- Mobile Health
Background:
- Cardiovascular disease is a leading cause of mortality globally.
- Myocardial infarction (heart attack) significantly impacts patient outcomes.
- Timely detection and intervention are crucial for improving myocardial infarction prognosis.
Purpose of the Study:
- To develop and evaluate a smartphone-based algorithm for real-time myocardial infarction detection.
- To reduce physician response time and enhance patient awareness of their cardiac condition.
- To leverage mobile technology for accessible and immediate cardiac event monitoring.
Main Methods:
- Utilized smartphone processing for ECG signal analysis.
- Employed time-domain methods to extract ST-segment features for infarction detection.
- Applied thresholding and linear classifiers for real-time risk assessment via LabVIEW Mobile Module.
Main Results:
- Achieved 98% sensitivity in detecting myocardial infarction in real-time.
- Reported 93.3% specificity for the developed algorithm.
- Demonstrated the algorithm's feasibility for real-time application on smartphones.
Conclusions:
- The algorithm's low computational load and high speed enable smartphone deployment.
- Bluetooth connectivity facilitates real-time data transfer from portable monitors.
- Smartphone integration allows for prompt infarction detection, patient notification, and facilitates physician consultation via mobile services.
Background:
In many countries, including our own, cardiovascular disease is the most common cause of mortality and morbidity. Myocardial infarction (heart attack) is of particular importance in heart disease as well as time and type of reaction to acute myocardial infarction and these can be a determining factor in patients' outcome.
Methods:
In order to reduce physician attendance time and keep patients informed about their condition, the smart phone as a common communication device has been used to process data and determine patients' ECG signals. For ECG signal analysis, we used time domain methods for extracting the ST-segment as the most important feature of the signal to detect myocardial infarction and the thresholding methods and linear classifiers by LabVIEW Mobile Module were used to determine signal risk.
Results:
The sensitivity and specificity as criteria to evaluate the algorithm were 98% and 93.3% respectively in real time.
Conclusions:
This algorithm, because of the low computational load and high speed, makes it possible to run in a smart phone. Using Bluetooth to send the data from a portable monitoring system to a smart phone facilitates the real time applications. By using this program on the patient's mobile, timely detection of infarction so to inform patients is possible and mobile services such as SMS and calling for a physician's consultation can be done.
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