Estimation of heart rate variability from finger photoplethysmography during rest, mild exercise and mild mental stress

Due to the possibilities in miniaturization and wearability, photoplethysmography (PPG) has recently gained a large interest not only for heart rate measurement, but also for estimating heart rate variability, which is derived from ECG by convention. The agreement between PPG and ECG-based HRV has been assessed in several studies, but the feasibility of PPG-based HRV estimation is still largely unknown for many conditions. In this study, we assess the feasibility of HRV estimation based on finger PPG during rest, mild physical exercise and mild mental stress. In addition, we compare different variants of signal processing methods including selection of fiducial point and outlier correction. Based on five minutes synchronous recordings of PPG and ECG from 15 healthy participants during each of these three conditions, the PPG-based HRV estimation was assessed for the SDNN and RMSSD parameters, calculated based on two different fiducial points (foot point and maximum slope), with and without outlier correction. The results show that HRV estimation based on finger PPG is feasible during rest and mild mental stress, but can give large errors during mild physical exercise. A good estimation is very dependent on outlier correction and fiducial point selection, and SDNN seems to be a more robust parameter compared to RMSSD for PPG-based HRV estimation.

Comparison of Seismocardiography Based Heart Rate Measurement Method

The article describes a new alternative method of detecting the Aorta Open fiducial point based on digital signal processing formulated from the average seismocardiogram cycle obtained from the 6-degree-of-freedom Micro Electro-Mechanical Systems Inertial Measurement Unit, enabling estimation of heartbeat during heart muscle contraction without reference to electrocardiogram time period. Using the seismocardiography data obtained from the Inertial Measurement Unit, the authors then process the data using two methods: 1) Empirical Mode Decomposition and 2) Jerk signal, which is extracted as a first derivative of the Inertial Measurement Unit signal. As an example, we compare the two proposed methods to the existing method. Our Method 2 allows us to detect Aorta Open-Aorta Open value between 400ms and 450ms using Berkeley Packet Filter 5-15 Hz with dynamic peak threshold from the Hilbert envelope. Thus, the evaluation of the new method’s effectiveness is confirmed by the estimation of the Aorta Open-Aorta Open fiducial point as closer to the reference. Therefore, the result of our research, especially using jerk signal, can be considered a more accurate alternative for estimating heart rate or heartbeat based on seismocardiogram.

A Study on User Recognition Using 2D ECG Image Based on Ensemble Networks for Intelligent Vehicles

IoT enabled smart car era is expected to begin in the near future as convergence between car and IT accelerates. Current smart cars can provide various information and services needed by the occupants via wearable devices or Vehicle to Everything (V2X) communication environment. In order to provide such services, a system to analyze wearable device information on the smart car platform needs to be designed. In this paper a real time user recognition method using 2D ECG (Electrocardiogram) images, a biometric signal that can be obtained from wearable devices, will be studied. ECG (Electrocardiogram) signal can be classified by fiducial point method using feature points detection or nonfiducial point method due to time change. In the proposed algorithm, a CNN based ensemble network was designed to improve performance by overcoming problems like overfitting which occur in a single network. Test results show that 2D ECG image based user recognition accuracy improved by 1%~1.7% for the fiducial point method and by 0.9%~2% for the nonfiducial point method. By showing 13% higher performance compared to the single network in which recognition rate reduction occurs because similar characteristics are shown between classes, capability for use in a smart vehicle platform based user recognition system that requires reliability was demonstrated by the proposed method.

Efficient Fiducial Point Detection of ECG QRS Complex Based on Polygonal Approximation

Electrocardiogram signal analysis is based on detecting a fiducial point consisting of the onset, offset, and peak of each waveform. The accurate diagnosis of arrhythmias depends on the accuracy of fiducial point detection. Detecting the onset and offset fiducial points is ambiguous because the feature values are similar to those of the surrounding sample. To improve the accuracy of this paper’s fiducial point detection, the signal is represented by a small number of vertices through a curvature-based vertex selection technique using polygonal approximation. The proposed method minimizes the number of candidate samples for fiducial point detection and emphasizes these sample’s feature values to enable reliable detection. It is also sensitive to the morphological changes of various QRS complexes by generating an accumulated signal of the amplitude change rate between vertices as an auxiliary signal. To verify the superiority of the proposed algorithm, error distribution is measured through comparison with the QT-DB annotation provided by Physionet. The mean and standard deviation of the onset and the offset were stable as − 4.02 ± 7.99 ms and − 5.45 ± 8.04 ms, respectively. The results show that proposed method using small number of vertices is acceptable in practical applications. We also confirmed that the proposed method is effective through the clustering of the QRS complex. Experiments on the arrhythmia data of MIT-BIH ADB confirmed reliable fiducial point detection results for various types of QRS complexes.