Noise Tolerant Heart Rate Extraction Algorithm Using Short-Term Autocorrelation for Wearable Healthcare Systems

Abstract

SUMMARYThis report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

“…Another important issue related to wearable sensors is high-accuracy design. Techniques can improve the signal level by improving noise tolerance characteristics [79]. A major noise that deteriorates the SNR of ECG or other signals is motion artifacts caused by skin stretching, pressure, and deformation.…”

“…Results show that the behavior classification system operated at about 9.8 μA current consumption. Figure 17 presents the inter-electrode distance dependence of the SNR [79]. The SNR improves as the distance between the electrodes increases.…”

“…The ECG waveform used for wearable sensors is often contaminated by noise from various sources such as baseline drift, hum noise, muscle noise, and motion artifacts, as portrayed in right portion of Fig. 18 [79]. Left portion of Fig.…”

“…Short-term autocorrelation (STAC) has been proposed for IHR extraction to remove muscle noise and motion artifacts ( Fig. 19) [79]. Autocorrelation [93], [94] and template matching [95] use similarity of QRS complex waveforms.…”

Combining Parallel Adaptive Filtering and Wavelet Threshold Denoising for Photoplethysmography-Based Pulse Rate Monitoring during Intensive Physical Exercise

“…Nowadays, there are mainly two methods used to monitor PR in wearable devices. One is the traditional method based on Electroocardiogram (ECG) signal [2], [3], it requires several electrodes to simultaneously collect physiological signal from different parts of human body, and then PR will be estimated according to the acquired ECG signal. This method is commonly used in clinical scenarios and can provide high measurement accuracy.…”