scholarly journals Towards non-invasive heart rate monitoring in free-ranging cetaceans: a unipolar suction cup tag measured the heart rate of trained Risso's dolphins

2021 ◽  
Vol 376 (1831) ◽  
pp. 20200225 ◽  
Author(s):  
Kagari Aoki ◽  
Yurie Watanabe ◽  
Daiki Inamori ◽  
Noriko Funasaka ◽  
Kentaro Q. Sakamoto
Keyword(s):  
Heart Rate ◽  
Killer Whale ◽  
Heart Rate Monitoring ◽  
Sinus Arrhythmia ◽  
Diving Physiology ◽  
Measuring Device ◽  
Non Invasive ◽  
Suction Cup ◽  
False Killer Whale ◽  
Free Ranging

Heart rate monitoring in free-ranging cetaceans to understand their behavioural ecology and diving physiology is challenging. Here, we developed a simple, non-invasive method to monitor the heart rate of cetaceans in the field using an electrocardiogram-measuring device and a single suction cup equipped with an electrode. The unipolar suction cup was placed on the left lateral body surface behind the pectoral fin of Risso's dolphins ( Grampus griseus ) and a false killer whale ( Pseudorca crassidens ) in captivity; their heart rate was successfully monitored. We observed large heart rate oscillations corresponding to respiration in the motionless whales during surfacing (a false killer whale, mean 47 bpm, range 20–75 bpm; Risso's dolphins, mean ± s.d. 61 ± 15 bpm, range 28–120 bpm, n = 4 individuals), which was consistent with the sinus arrhythmia pattern (eupneic tachycardia and apneic bradycardia) observed in other cetaceans. Immediately after respiration, the heart rate rapidly increased to approximately twice that observed prior to the breath. Heart rate then gradually decreased at around 20–50 s and remained relatively constant until the next breath. Furthermore, we successfully monitored the heart rate of a free-swimming Risso's dolphin. The all-in-one suction cup device is feasible for field use without restraining animals and is helpful in further understanding the diving physiology of free-ranging cetaceans. This article is part of the theme issue ‘Measuring physiology in free-living animals (Part II)’.

scholarly journals Analysis of Non-invasive Video Based Heart Rate Monitoring System obtained from Various Distances and Different Facial Spot

2018 ◽  
Vol 1049 ◽  
pp. 012003 ◽  
Author(s):  
Norwahidah Ibrahim ◽  
Razali Tomari ◽  
Wan Nurshazwani Wan Zakaria ◽  
Nurmiza Othman
Keyword(s):  
Heart Rate ◽  
Monitoring System ◽  
Heart Rate Monitoring ◽  
Non Invasive

scholarly journals Pulse Rate Monitoring Embedded System during Indoor Exercises Using Microcontroller

2019 ◽  
Vol 8 (3) ◽  
pp. 2064-2066
Keyword(s):  
Heart Rate ◽  
Embedded System ◽  
Low Cost ◽  
Heart Beat ◽  
Measuring Device ◽  
Non Invasive ◽  
Beat Rate ◽  
Proposed Model ◽  
Measure Heart Rate ◽  
Heart Beat Rate

In the current paper we have described the design, testing and result data of a low cost heart beat measuring device. The proposed model works on the properties of optics. Our model is non-invasive in nature and able to measure heart rate of any individual during different physical activities. We have also developed a better algorithm for measuring heart beat rate at a fixed interval of 5 seconds. The heart beat is counted by a specific microcontroller that displays the heart rate data on an LCD continuously. We have also measured the heart beat rate of an individual running on the trademill at variable speed and compared the result with our model.

scholarly journals Real-Time Cardiac Beat Detection and Heart Rate Monitoring from Combined Seismocardiography and Gyrocardiography

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3472 ◽  
Author(s):  
D’Mello ◽  
Skoric ◽  
Xu ◽  
Roche ◽  
Lortie ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Time Algorithm ◽  
Cardiac Monitoring ◽  
Computational Time ◽  
Heart Rate Monitoring ◽  
Heart Rates ◽  
Non Invasive ◽  
Beat Detection ◽  
Speed Accuracy

Cardiography is an indispensable element of health care. However, the accessibility of at-home cardiac monitoring is limited by device complexity, accuracy, and cost. We have developed a real-time algorithm for heart rate monitoring and beat detection implemented in a custom-built, affordable system. These measurements were processed from seismocardiography (SCG) and gyrocardiography (GCG) signals recorded at the sternum, with concurrent electrocardiography (ECG) used as a reference. Our system demonstrated the feasibility of non-invasive electro-mechanical cardiac monitoring on supine, stationary subjects at a cost of $100, and with the SCG–GCG and ECG algorithms decoupled as standalone measurements. Testing was performed on 25 subjects in the supine position when relaxed, and when recovering from physical exercise, to record 23,984 cardiac cycles at heart rates in the range of 36–140 bpm. The correlation between the two measurements had r2 coefficients of 0.9783 and 0.9982 for normal (averaged) and instantaneous (beat identification) heart rates, respectively. At a sampling frequency of 250 Hz, the average computational time required was 0.088 s per measurement cycle, indicating the maximum refresh rate. A combined SCG and GCG measurement was found to improve accuracy due to fundamentally different noise rejection criteria in the mutually orthogonal signals. The speed, accuracy, and simplicity of our system validated its potential as a real-time, non-invasive, and affordable solution for outpatient cardiac monitoring in situations with negligible motion artifact.

A Simple Non-Invasive Automated Heart Rate Monitoring System Using Facial Images

2016 ◽  
pp. 100-122 ◽  
Author(s):  
Humaira Nisar ◽  
Zhen Yao Lim ◽  
Kim Ho Yeap
Keyword(s):  
Heart Rate ◽  
Power Spectrum ◽  
Color Model ◽  
Heart Rate Monitoring ◽  
Monitoring Method ◽  
Non Invasive ◽  
Average Accuracy ◽  
Contact Devices ◽  
The Face ◽  
Cie Lab

In this chapter we will discuss a simple non invasive automated heart rate monitoring method. Commonly heart rate is measured by using heart rate monitor devices. Many patients do not feel comfortable when they use contact devices for diagnostic purposes. Our algorithm gives a non-invasive way of heart rate measurement. The first step is to record a video. After 5 frames of the video are captured, the face is detected. A total of 300 frames will be used for further processing. At this stage, ROI (part of forehead) will be cropped out automatically. All image frames are in RGB color model, so these will be separated into 3 channels. For analysis, graph normalization is applied, which uses mean and standard deviation. Fast Fourier transform is used to plot the power spectrum of the traces. This power spectrum will have a peak if the heart rate is detected. We used RGB, HSI, YCbCr, YIQ, and CIE LAB color models for analysis. The best result is achieved with RGB color model followed by CIELab. The average accuracy is 95.32%.

scholarly journals Design and Performance Analysis of an Infra-Red Based Heart Rate Monitoring System

2019 ◽  
Vol 8 (6) ◽  
pp. 534-540
Keyword(s):  
Heart Rate ◽  
Performance Analysis ◽  
Error Rate ◽  
Processing Unit ◽  
Heart Rate Monitoring ◽  
Measuring Device ◽  
Sensor Unit ◽  
Ir Sensor ◽  
Infra Red ◽  
And Performance

The rate of the human heartbeat is an indication of the health status of the heart and circulatory system and many humans are potential candidates of stress-related health conditions due to the non-availability of a heart rate measuring device that is both affordable and easy to operate. This paper presents a study on the design and performance analysis of an efficient infrared based heart rate monitoring (HRM) system that is economical and portable. The heart rate is measured by placing the finger on an Infra-Red (IR) sensor unit composed of the IR Light Emitting Diode (LED) and a photo-diode. The heart rate is detected from the blood flow through the finger and the pulse signals pass through some filtering and amplification to be detected by the microcontroller. The HRM system employs a microcontroller, PIC16F628A which serves as a central processing unit (CPU) to process and analyze the heart beats detected as electrical signals from the IR sensor unit and converts the measured heart rate to a numerical value which is displayed via a 3-digit seven segment display. The designed HRM device is portable, durable and cost-effective. It can be used to monitor the human heart rate in both clinical and non-clinical environments with the advantage of being operated by non-professionals. The HRM system was tested in the laboratory and 50 samples of heart rates was analyzed to determine its performance level. The accuracy test results of the HRM device showed that the average error rate is 1.85%, when compared with a Blood Pressure Monitor. Hence, the performance of the HRM device is high with negligible error rate.

An Effective Algorithm for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms

2020 ◽  
Vol 10 (3) ◽  
pp. 633-640 ◽  
Author(s):  
Jingxian Liang ◽  
Jialin Huang ◽  
Liwei Mu ◽  
Baoxian Yu ◽  
Pengbin Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Cross Correlation ◽  
Effective Algorithm ◽  
Sleep Staging ◽  
Heart Rate Monitoring ◽  
Non Invasive ◽  
Step Procedure ◽  
The Mean ◽  
Beat Detection ◽  
Beat Heart

Ballistocardiograms (BCG) is an essential signal for vital sign monitoring. Obtaining the beat-to-beat intervals from BCG signal is of great significance for home-care applications, such as sleep staging, heart disease alerting, etc. The current approaches of detecting beat-to-beat intervals from BCG signals are complex. In this paper, we develop a non-invasive BCG monitoring system, and propose an effective and accurate algorithm for beat-to-beat detection. Firstly, a heartbeat shape is adaptively modeled based on a two-step procedure by taking advantage of the J-peak and the K-valley of BCG signals. Then, forward and backward detections with the criteria of both the morphological distance and the cross-correlation are jointly employed to find the position of each BCG signal, and in turn, to determine the beat-to-beat intervals of BCG signals. The proposed method was validated in at least 90 minutes recording from 10 subjects in various setups. The mean absolute beat-to-beat intervals error was 10.72 ms and on an average 97.93% of the beat-to-beat intervals were detected.

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