Artificial Intelligence-Enabled ECG Big Data Mining for Pervasive Heart Health Monitoring

Abstract

The ECG signal is a gold standard physiological signal to reflect heart health and has been studied for many decades. It can not only be leveraged to generate real-time emergency alarms before a heart attack but also be mined in a long-term manner for risk pattern discovery. ECG signal has specific characteristics in multiple domain, such as the temporal, frequency, statistical, and phase domains, each of which can reveal some interesting medical hints related to the mechanical and electrical behaviors of the heart. Traditionally, ECG signals are acquired in clinics or hospitals, where very high signal quality can be guaranteed. However, along with the advance of wearable computers and mobile computing platforms, there are many emerging ECG-based heart disease management possibilities, i.e., possibly, we can monitor the ECG signal in our daily lives and effectively track our heart health without going to medical facilities. However, it is very challenging to deal with motion artifacts during people’s physical activates. Many researchers have proposed a large number of ECG signal processing algorithms and studied a bunch of potential applications. We have introduced artificial intelligence into wearable ECG-based heart rate monitoring during severe human activities, which greatly outperform previous studies. We have also studied novel ECG applications, which can capture single-arm-ECG for 24-hour heart disease monitoring. The goal in this work is to eliminate the uncomfortableness and inconvenience induced by traditional ECG configurations, i.e., the 12-lead ECG placement methods. Especially, previously, people put the ECG electrodes on the chest, which requires a chest strap to fix the electrodes. Leveraging advanced signal sensing and artificial intelligence algorithms, we have successfully demonstrated the potential of this highly wearable arm-worn heart rate monitor. This chapter will systematically introduce the current advancement of ECG signal processing algorithms and applications, including previous works and our research progress. The readers from both academia and industry can benefit from the chapter by understanding the advancement, challenges, and future opportunities from this chapter.