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Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing

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Feature Engineering and Computational Intelligence in ECG Monitoring

Abstract

Standard electrocardiogram (ECG) database is a key point for validating the algorithms of feature detection and disease diagnosis. Researchers usually use the ECG databases posted in the PhysioNet platform, which were basically collected from clinical environment with high signal quality. Performance of the developed algorithms from these databases suffers the poor robustness and weak generalization when implemented on the dynamic ECGs typically collected by wearable devices. Standard and accredited dynamic databases are absent. Six open-accessed ECG databases, including signal quality database, China physiological signal challenge (CPSC) 2018 and 2019 databases, arrhythmia database, atrial fibrillation (AF) database, and long-term ECG database, were therefore tidied up and published freely. All the valuable ECG feature information were carefully annotated by cardiologists. We hope these databases may benefit the ECG study on dynamic signal processing.

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Author information

Authors and Affiliations

  1. The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Hongxiang Gao, Chengyu Liu & Jianqing Li

  2. The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

    Qin Shen

  3. School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China

    Jianqing Li

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  1. Hongxiang Gao
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  2. Chengyu Liu
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  3. Qin Shen
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  4. Jianqing Li
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Correspondence to Chengyu Liu .

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Editors and Affiliations

  1. The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, China

    Chengyu Liu

  2. School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China

    Jianqing Li

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Gao, H., Liu, C., Shen, Q., Li, J. (2020). Representative Databases for Feature Engineering and Computational Intelligence in ECG Processing. In: Liu, C., Li, J. (eds) Feature Engineering and Computational Intelligence in ECG Monitoring. Springer, Singapore. https://doi.org/10.1007/978-981-15-3824-7_2

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