Combined Time-Frequency Calculation of pNN50 Metric From Noisy Heart Rate Measurements

2014 ◽  
Author(s):  
Payam Parsinejad ◽  
Yolanda Rodriguez-Vaqueiro ◽  
Jose Angel Martinez-Lorenzo ◽  
Rifat Sipahi
Keyword(s):  
Heart Rate ◽  
Mental Workload ◽  
Human Subjects ◽  
Detection Algorithm ◽  
Frequency Domain Analysis ◽  
Peak Detection ◽  
Time Frequency ◽  
Peak Detection Algorithm ◽  
Frequency Calculation ◽  
Noticeable Improvement

pNN50 is a metric derived from heart rate (HR) measurements, and it is known to correlate with mental-workload changes in human subjects. Conventionally, this metric is calculated based on the variability of successive time periods in peak-to-peak occurrences in HR data. In the case of noisy measurements of HR, however, peak-to-peak detection may not be reliable. Here, we present a combined time-frequency domain analysis, benefiting from Short Time Fourier Transform, by which we can more accurately extract pNN50 metric from noisy HR data. An experimental measurement with added noise is used as a benchmark problem to demonstrate the effectiveness of the approach with noticeable improvement over the conventional time domain peak-to-peak detection algorithm.

scholarly journals Peak Detection Algorithm for Vital Sign Detection Using Doppler Radar Sensors

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1575 ◽  
Author(s):  
Ju-Yeon Kim ◽  
Jae-Hyun Park ◽  
Se-Young Jang ◽  
Jong-Ryul Yang
Keyword(s):  
Heart Rate ◽  
Doppler Radar ◽  
Vital Signs ◽  
Accurate Method ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Operating Characteristics ◽  
Radar Sensor ◽  
Peak Detection Algorithm ◽  
Hrv Analysis

An accurate method for detecting vital signs obtained from a Doppler radar sensor is proposed. A Doppler radar sensor can remotely obtain vital signs such as heartbeat and respiration rate, but the vital signs obtained by using the sensor do not show clear peaks like in electrocardiography (ECG) because of the operating characteristics of the radar. The proposed peak detection algorithm extracts the vital signs from the raw data. The algorithm shows the mean accuracy of 96.78% compared to the peak count from the reference ECG sensor and a processing time approximately two times faster than the gradient-based algorithm. To verify whether heart rate variability (HRV) analysis similar to that with an ECG sensor is possible for a radar sensor when applying the proposed method, the continuous parameter variations of the HRV in the time domain are analyzed using data processed with the proposed peak detection algorithm. Experimental results with six subjects show that the proposed method can obtain the heart rate with high accuracy but cannot obtain the information for an HRV analysis because the proposed method cannot overcome the characteristics of the radar sensor itself.

Robust PPG Peak Detection Using Dilated Convolutional Neural Networks

2021 ◽  
Author(s):  
Kianoosh Kazemi ◽  
Juho Laitala ◽  
Iman Azimi ◽  
Pasi Liljeberg ◽  
Amir M. Rahmani
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Convolutional Neural Networks ◽  
Signal To Noise Ratio ◽  
Motion Artifact ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Detection Methods ◽  
Method Performance ◽  
Data Generator

<div>Accurate peak determination from noise-corrupted photoplethysmogram (PPG) signal is the basis for further analysis of physiological quantities such as heart rate and heart rate variability. In the past decades, many methods have been proposed to provide reliable peak detection. These peak detection methods include rule-based algorithms, adaptive thresholds, and signal processing techniques. However, they are designed for noise-free PPG signals and are insufficient for PPG signals with low signal-to-noise ratio (SNR). This paper focuses on enhancing PPG noise-resiliency and proposes a robust peak detection algorithm for noise and motion artifact corrupted PPG signals. Our algorithm is based on Convolutional Neural Networks (CNN) with dilated convolutions. Using dilated convolutions provides a large receptive field, making our CNN model robust at time series processing. In this study, we use a dataset collected from wearable devices in health monitoring under free-living conditions. In addition, a data generator is developed for producing noisy PPG data used for training the network. The method performance is compared against other state-of-the-art methods and tested in SNRs ranging from 0 to 45 dB. Our method obtains better accuracy in all the SNRs, compared with the existing adaptive threshold and transform-based methods. The proposed method shows an overall precision, recall, and F1-score 80%, 80%, and 80% in all the SNR ranges. However, these figures for the other methods are below 78%, 77%, and 77%, respectively. The proposed method proves to be accurate for detecting PPG peaks even in the presence of noise.</div>

Fiber Bragg Grating Spectrum Peak-Detection Algorithm Based on Difference of Gaussian

2021 ◽  
Vol 58 (7) ◽  
pp. 0706002
Author(s):  
蔺彦章 Lin Yanzhang ◽  
刘毅 Liu Yi ◽  
潘玉恒 Pan Yuheng ◽  
李国燕 Li Guoyan
Keyword(s):  
Fiber Bragg Grating ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Bragg Grating ◽  
Peak Detection Algorithm ◽  
Difference Of Gaussian

Research of Peak-Detection Algorithm Based on Absolute Wavelength Calibration of Excimer Laser

2018 ◽  
Vol 45 (7) ◽  
pp. 0701003
Author(s):  
袁靖超 Yuan Jingchao ◽  
赵江山 Zhao Jiangshan ◽  
李慧 Li Hui ◽  
刘广义 Liu Guangyi
Keyword(s):  
Excimer Laser ◽  
Detection Algorithm ◽  
Peak Detection ◽  
Wavelength Calibration ◽  
Peak Detection Algorithm
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