Long-term electrocardiogram (ECG) recordings while performing normal daily routines are often corrupted with motion artifacts, which in turn, can result in the incorrect calculation of heart rates. Heart rates are important clinical information, as they can be used for analysis of heart-rate variability and detection of cardiac arrhythmias. In this study, we present an algorithm for denoising ECG signals acquired with a wearable armband device. The armband was worn on the upper left arm by one male participant, and we simultaneously recorded three ECG channels for 24 h. We extracted 10-s sequences from armband recordings corrupted with added noise and motion artifacts. Denoising was performed using the redundant convolutional encoder–decoder (R-CED), a fully convolutional network. We measured the performance by detecting R-peaks in clean, noisy, and denoised sequences and by calculating signal quality indices: signal-to-noise ratio (SNR), ratio of power, and cross-correlation with respect to the clean sequences. The percent of correctly detected R-peaks in denoised sequences was higher than in sequences corrupted with either added noise (70–100% vs. 34–97%) or motion artifacts (91.86% vs. 61.16%). There was notable improvement in SNR values after denoising for signals with noise added (7–19 dB), and when sequences were corrupted with motion artifacts (0.39 dB). The ratio of power for noisy sequences was significantly lower when compared to both clean and denoised sequences. Similarly, cross-correlation between noisy and clean sequences was significantly lower than between denoised and clean sequences. Moreover, we tested our denoising algorithm on 60-s sequences extracted from recordings from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and obtained improvement in SNR values of 7.08 ± 0.25 dB (mean ± standard deviation (sd)). These results from a diverse set of data suggest that the proposed denoising algorithm improves the quality of the signal and can potentially be applied to most ECG measurement devices.

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Our study focuses on classifying as significant Electrocardiogram (ECG) data from home healthcare system. Generally, spectral analysis of RR Interval (RRI) time series is used to determine periodic component of Heart Rate Variability (HRV). It is well known, moreover, that Low Frequency (LF) component is associated with blood pressure regulation, and High Frequency (HF) component is referred to respiration as Respiration Sinus Arrhythmia (RSA) in the HRV power spectra. In many cases, however, LF and HF components may be entirely superimposed on each other and, therefore, the method by division of power spectra range can not be evaluated diagnostically. We propose another approach to interpret well better than before. The method which we suggest is that it finds high correlative data using frequency analysis comparison Heart Instantaneous Frequency (HIF) based on extracting the instantaneous fundamental frequency with EDR. The reason which we use HIF is that it is simpler and more powerful against noise than HRV. First of all, we show the EDR extraction process, and then prove that HIF signal is useful or not through comparison with HRV. Finally, we classify significant signal data through comparison High Frequency (HF) component obtained frequency analysis of HIF with that of EDR.