Electrocardiogram: Acquisition and Analysis for Biological Investigations and Health Monitoring

Electrocardiogram (ECG or EKG) was introduced since 1893 by Einthoven, and it has been used for decades in clinical settings for vital sign monitoring as well as cardiac assessment. The ECG signal with its unique characteristic waves of P waves, QRS complexes, and T waves holds important information about the functionalities of the heart. In recent years, advances in electronics and telecommunications have paved the way for out-of-clinic ECG acquisition and monitoring. The rise of advanced data science techniques, such as machine learning, has further opened doors for distanced, home-based, and automated diagnoses. In parallel, micro- and nanotechnology has enabled significant strides in biological investigations using small animal models, such as zebrafish and mouse, uncovering underlying mechanisms of numerous biological processes. In this chapter, we first introduce the basics of electrocardiogram and the methods for acquisition; and then systems used with zebrafish and humans are discussed. Artificial intelligence, specifically machine learning, is brought into the discussion with an emphasis on the use of convolutional neuron networks for classifying ECG patterns of arrhythmic zebrafish mutants. Finally, the chapter recapitulates with the necessity of translating findings from animal research for use with humans as well as a body sensor network with multimodal sensors which may reveal unprecedented connections among physiological parameters.