Heart Rate Variability Estimation in Electrocardiogram Signals Interferences Based on Photoplethysmography Signals
In order to improve the accuracy and real-timelines of heart rate variability (HRV) estimation in electrocardiogram (ECG) signals interferences, a novel HRV estimation method based on photoplethysmography (PPG) signals is proposed. The short-time autocorr
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Abstract. In order to improve the accuracy and real-timelines of heart rate variability (HRV) estimation in electrocardiogram (ECG) signals interferences, a novel HRV estimation method based on photoplethysmography (PPG) signals is proposed. The short-time autocorrelation principle is used to detect interferences in ECG signals, then, the improving sliding window iterative Discrete Fourier Transform (DFT) is used to estimate HRV in ECG interferences from the synchronously acquisitioned PPG signals. The international commonly used MIT-BIT Arrhythmia Database/Challenge 2014 Training Set is used to verify the interferences detection algorithm and HRV estimation algorithm which are proposed. At the same time, the proposed algorithms are compared with recently existing representative interferences detection algorithm based on RR intervals and PRV directly replaced HRV algorithm, respectively. The results show that the proposed methods are more accurate and more real-time. Keywords: Interferences signals
Heart rate variability
Photoplethysmography
1 Introduction Heart rate variability (HRV) is produced in the periodical change of heart beat intervals, which is one of the important indices for reflecting the sympathetic nerve and vagus nerve activity’s balance in the autonomic nervous system. It can be used for many diseases’ prediction or diagnoses, such as sudden cardiac death, coronary heart disease, heart failure, hypertension, diabetes, Parkinsonʼs disease and apnea disease, etc. [1]. However, HRV is derived from ECG signals, ECG signals’ acquisition needs many electrodes and multifarious wires. At the same time, ECG signals acquisitioned by monitoring equipment often contain interferences caused by many factors, including human movement. In recent years, many scholars have carried out extensive research on interferences detection, such as W. Karlen proposed a method of repeatedly using Gaussian filter and cross-correlation for PPG signals quality evaluation which can realize the interferences detection in PPG signals further [2]. The method needs segment PPG signals earlier. But because of the noise and artifacts, the segmentation © Springer International Publishing Switzerland 2016 D.-S. Huang et al. (Eds.): ICIC 2016, Part III, LNAI 9773, pp. 149–159, 2016. DOI: 10.1007/978-3-319-42297-8_15
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accuracy is hard to guarantee, which leads the accuracy of algorithm is not high. C. Orphanidou proposed quality evaluation method based on RR intervals [3]. The method is achieved by adaptive template matching theory, but template generation process is very complicated, thus the algorithm is very complex and the real-timeliness becomes poor. Li Qiao proposed machine learning method to classify multichannel ECG signals based on signal quality indices (SQI) [4]. The SQI of each channel signal is extracted first, then using SVM to complete the classification. Although the accuracy of the algorithm is high, as it is aiming at off-line data, the real-timeliness of the algorithm is not so good. In addit
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