Using the Redundant Convolutional Encoder-Decoder to Denoise QRS Complexes in ECG Signals Recorded with an Armband Wearable Device.

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.

An improved algorithm for respiration signal extraction from electrocardiogram measured by conductive textile electrodes using instantaneous frequency estimation.

In this paper, an improved algorithm for the extraction of respiration signal from the electrocardiogram (ECG) in home healthcare is proposed. The whole system consists of two-lead electrocardiogram acquisition using conductive textile electrodes located in bed, baseline fluctuation elimination, R-wave detection, adjustment of sudden change in R-wave area using moving average, and optimal lead selection. In order to solve the problems of previous algorithms for the ECG-derived respiration (EDR) signal acquisition, we are proposing a method for the optimal lead selection.

A novel approach to classify significant ECG data based on heart instantaneous frequency and ECG-derived respiration using conductive textiles.

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.