Artificial Intelligence of Things Wearable System for Cardiac Disease Detection

Somayeh Nosrati

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Abstract:

This study proposes an artificial intelligence of things (AIoT) system for electrocardiogram (ECG) analysis and cardiac disease detection. The system includes a front-end IoT-based hardware, a user interface on a smart device’s application (APP), a cloud database, and an AI platform for cardiac disease detection. The front-end IoT-based hardware, a wearable ECG patch that includes an analog front-end circuit and a Bluetooth module, can detect ECG signals. The APP on smart devices can not only display users’ real-time ECG signals but also label unusual signals instantly and reach real-time disease detection. These ECG signals will be uploaded to the cloud database. The cloud database is used to store each user’s ECG signals, which form a big-data database for an AI algorithm to detect cardiac disease. The algorithm proposed by this study is based on a convolutional neural network and the average accuracy is 94.96%. The ECG dataset applied in this study is collected from patients in Tainan Hospital, Ministry of Health and Welfare. Moreover, signal verification was also performed by a cardiologist.

Author Keywords

Arrhythmia,
atrial fibrillation,
a convolutional neural network,
electrocardiogram,
the artificial intelligence of things,
wearable device,
application,
cloud server

IEEE Keywords

Electrocardiography,
User interfaces,
Databases,
Classification algorithms,
Signal processing algorithms,
Artificial intelligence,
Cloud computing

Introduction

Arrhythmia is a leading cause of heart disorder. It can be divided into three categories: premature heartbeat, tachycardia, and bradycardia. Although most arrhythmias do not present immediate risk and usually happen in our daily life, atrial fibrillation is the main cause of acute stroke, and ventricular tachycardia is the leading cause of shock or sudden cardiac disease.

Conclusion

This study proposes a complete AIoT system platform, which is an integrated health-care system, including hardware, software, and a cloud database, and is expected to enhance health. The AI-based algorithm for arrhythmia classification, which takes professional cardiologist’s advice as a reference, has a simpler data pre-processing progress and a suitable identification pattern compared with other algorithms. However, to overcome the problems from individual differences and enhance the model’s tolerance, more data are needed from different clinical individuals, which can be used to train the model and further modify the pre-processing function. In addition, this work takes single-lead ECG as the measurement, which means it cannot analyze some types of arrhythmia. In the future, this work hopes to improve the model as simple as possible to realize the AI-based algorithms on-chip. The prospect is to reach a real AIoT-based system for cardiac disease detection and broadcast health care to every individual.

About KSRA

The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.

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