Key result
Autocorrelation method reduces noise and improves threshold-based heartbeat detection in noisy ECG signals.
Why the study?
Heartbeat detection is more challenging in ambulatory conditions due to higher levels of noise and artefacts compared to hospital settings.
Does an autocorrelation-based noise reduction method improve heartbeat detection performance in noisy ECG signals?
Does an autocorrelation-based noise reduction method improve heartbeat detection performance in noisy ECG signals?
An autocorrelation-based noise reduction method improves the accuracy of threshold-based heartbeat detection in noisy ambulatory ECG signals.
May support noise-robust ECG algorithms in research settings; leaves open human translation and clinical validation.
Detecting heartbeats is more challenging in an ambulatory condition due to a higher level of noise and artefacts compared to heartbeats recorded in a hospital setting. To reduce false detection and improve the performance of threshold-based beat detection algorithm, an autocorrelation method for noise reduction within noisy electrocardiogram (ECG) signals is presented in this paper. The proposed method contained two components: autocorrelation and refining process. The proposed work used the autocorrelation method to generate periods of heartbeats to refine and identify the correct QRS complex and remove the noise in the candidate QRS complex, thus reducing incorrect detections. Results from the experiment showed that the proposed method was capable of reducing the noise and improving the beat detection performance in noisy signals.
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Apandi et al. (2020) studied this question. The autocorrelation method significantly reduced noise and improved threshold-based heartbeat detection performance in noisy ECG signals.
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