scholarly journals A standardized method of classifying pulsed sounds and its application to pulse rate measurement of blue whale southeast Pacific song units

2019 ◽  
Vol 146 (4) ◽  
pp. 2145-2154 ◽  
Author(s):  
Julie Patris ◽  
Franck Malige ◽  
Hervé Glotin ◽  
Mark Asch ◽  
Susannah J. Buchan
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Franck Malige ◽  
Julie Patris ◽  
Susannah J. Buchan ◽  
Kathleen M. Stafford ◽  
Fannie Shabangu ◽  
...  

2021 ◽  
Author(s):  
Brian L. Hill ◽  
Xin Liu ◽  
Daniel McDuff

2020 ◽  
Vol 12 (22) ◽  
pp. 9646
Author(s):  
Jian-Chiun Liou ◽  
Chih-Wei Peng ◽  
Philippe Basset ◽  
Zhen-Xi Chen

In this study, a medical grade pulse rate (PR) instrument was used to monitor hemodialysis patients, and the wearable product was applied for the 4 h observation. Electrocardiogram (ECG) and photoplethysmography (PPG) data were simultaneously collected to observe physiological phenomena in patients undergoing hemodialysis. The analyzed results of 38 patients undergoing the treatment (as sympathetic/parasympathetic balance indicators before-hemodialysis (HD), and after-HD) and autonomic nerve activation for the pulse rate (PR) measurement accompanied by squeezing a soft ball were also observed. The results prove the pulse rate measurement while squeezing the soft ball and analyze data, and we show that the analyzed results have a very concentrated normal distribution. This study presents oxygen saturation (SpO2) and continuous pulse rate distribution curves during the 4 h observation of the hemodialysis patients and we show that some patients undergoing kidney dialysis have sleep apnea. They become lethargic during dialysis and experience severe hypoxia due to intermittent respiratory arrest. Studies have confirmed that such monitoring and biofeedback designs can reduce the incidence of hypotension during dialysis.


Photoplethysmography measures vital signs through to extraction of signals from the body. The paper explains the technique for extraction of pulse rate from the videos for three color channels namely; red, green and blue. The DMIMS database is used for experimentation which consists of total 720 videos out of which 25 videos are used for analysis. The results presented in this paper depict that our algorithm works best for blue channel followed by green and then red channel. The main focus of paper is to extract pulse rate from the recorded video and compare the output for different channels and find the best channel for heart rate extraction.


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