Authors:  Elisabeth Olbert, Yvonne Teuschl, Christoph Mahringer, Walter Struhal

Editor's Choice
Journal of the Neurological Sciences.  REVIEW ARTICLE|  VOLUME 465, 123170, October 15, 2024

DOI: https://doi.org/10.1016/j.jns.2024.123170

Power modulation spectrum – a promising approach for the indispensable quality control of electrocardiogram signals from monitoring units for the detection of autonomic dysfunction

Highlights

• Big-data collected during long term monitoring could be used for research.
• Artefacts corrupt the electrocardiogram for metrics of heart rate variability.
• Large data challenge the golden standard “human expert” for detecting artefacts.
• Automated Modulation spectrum-based quality indices provide reliable information.
• MS-QI and PDQI can be used to discriminate between different data-quality.

Electrocardiogram (ECG) is essential for evaluating the autonomic nervous system. Ensuring the quality of real-world ECG datasets is critical, but manual control of large datasets is impractical. Thus, automated quality control is necessary. This paper introduces a new quality index, the peak-distance quality index (PDQI), based on the modulation spectrum approach.

Real-life data from 1000 ECG recordings, each 600 s long, were collected at the stroke unit of the University Hospital Tulln. Each ECG was visually evaluated, including the duration of the signal, artefacts and noise, and the number of extrasystoles. The power-modulation spectrum, the percentage of ECG in each signal, and modulation spectrum-based quality index (MS-QI) and PDQI were calculated. The area under the curve (AUC) for the detection of high-quality ECGs was calculated for both quality indices, as well as the optimal threshold for each index.

The percentage of ECG signals in the recordings based on the modulation spectrum correlates with expert rating (r = 0.99, p < 0.001). The AUC for PDQI for the detection of extrasystoles is 0.96, and the AUC for MSQI for the detection of artefacts is 0.83. The optimal thresholds for PDQI and MSQI are 0.44 and 0.17, respectively

The power modulation spectrum can be applied to large amounts of data to detect ECG signals within biosignals and calculate quality indices. MSQI can be used for artefact detection and PDQI for extrasystole detection in ECG signals. A combined approach using both quality indices can provide a picture of the underlying data quality.