• Wenxi ChenEmail author


This chapter provides a tutorial overview and future perspectives on the historical development and latest advances in electrocardiogram (ECG)-related studies with a focus on the three aspects of signal acquisition, data analysis, and practical application. A development procedure from signal acquisition to knowledge formulation is outlined based on the Signal, Data, Information, Knowledge, Wisdom hierarchical model.

Engineering principles and representative methodologies for ECG acquisition are thoroughly surveyed from Einthoven to Holter. To meet the special requirements for seamless healthcare monitoring, the comprehensive investigation on signal acquisition focuses on measurement methodologies applicable to various daily life scenarios. Acquisition methodologies are categorized into three modalities as wearable, attachable, and invisible. The preferred implementation by the dry and noncontact method is highlighted.

Analysis methods for ECG signal and heart rate data are broadly investigated on a short-term basis, that is, beat-to-beat analysis, sometimes in real-time processing mode, and a long-term basis, meaning various temporal scales such as daily, weekly, monthly, seasonal, and even yearly in batch processing mode. Heart rate variability (HRV) analysis methods in temporal, frequency, and nonlinear domains are extensively reviewed. In addition, heart rate turbulence (HRT) for risk stratification and prediction of acute myocardial infarction is briefly discussed.

The range of practical applications is constantly expanding with the discovery of innovative knowledge and understanding through deep mining of indiscernible information from ECG/HRV. The appropriate combination of such informative features in different analysis domains and the efficient presentation of analytical results play an important role in the visualization of physiological significance. Besides the conventional clinical application of ECG in the diagnosis of cardiovascular-related diseases, broader application in the daily healthcare domain, such as menstrual cycle estimation, health condition tracking, lifestyle change detection, biorhythm evaluation, sleep stage classification, and medicinal effect assessment, is also expected.

Future perspectives are viewed with optimism. Modalities for the acquisition of multiple physiological signals and nonphysiological data over a long-term period are becoming a reality to guarantee analytical outcomes that are more reliable and holistic with the maturation of big data infrastructure, platforms, and analytics, as well as the paradigm shift from clinical disease diagnosis to daily preventive healthcare.


ECG HRV HRT Biosignal acquisition Signal processing Data analysis Daily healthcare Monitoring Wearable Attachable Invisible Health condition Biorhythm 


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© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Biomedical Information Technology LaboratoryThe University of AizuTsuruga, Ikki-machi, Aizu-WakamatsuJapan

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