Publication detail

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

SMITAL, L. HAIDER, C. VÍTEK, M. LEINVEBER, P. JURÁK, P. NĚMCOVÁ, A. SMÍŠEK, R. MARŠÁNOVÁ, L. PROVAZNÍK, I. FELTON, C. GILBERT, B. HOLMES, D.

Original Title

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

English Title

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions

Type

journal article in Web of Science

Language

en

Original Abstract

Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. Methods: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. Results: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. Conclusion: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. Significance: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

English abstract

Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. Methods: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. Results: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. Conclusion: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. Significance: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

Keywords

Electrocardiography; Signal to noise ratio; Estimation; Biomedical monitoring; Performance evaluation; Real-time systems; ECG delineation; ECG signal; QRS detection; signal quality; signal segmentation; SNR estimation

Released

27.01.2020

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Location

PISCATAWAY

ISBN

1558-2531

Periodical

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Year of study

67

Number

10

State

US

Pages from

2721

Pages to

2734

Pages count

14

URL

Documents

BibTex


@article{BUT165449,
  author="Lukáš {Smital} and Clifton {Haider} and Martin {Vítek} and Pavel {Leinveber} and Pavel {Jurák} and Andrea {Němcová} and Radovan {Smíšek} and Lucie {Maršánová} and Ivo {Provazník} and Christopher L. {Felton} and Barry {Gilbert} and David {Holmes}",
  title="Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions",
  annote="Objective: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. Methods: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. Results: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. Conclusion: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. Significance: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.",
  address="IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
  chapter="165449",
  doi="10.1109/TBME.2020.2969719",
  howpublished="online",
  institution="IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
  number="10",
  volume="67",
  year="2020",
  month="january",
  pages="2721--2734",
  publisher="IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
  type="journal article in Web of Science"
}