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Journal articles on the topic 'Heart rate detection'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Boudet, G., and A. Chamoux. "Heart Rate Monitors And Abnormal Heart Rhythm Detection." Archives of Physiology and Biochemistry 108, no. 4 (January 2000): 371–79. http://dx.doi.org/10.1076/apab.108.4.371.4304.

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2

PEARSON, MICHAEL, and OLIVER FAUST. "HEART-RATE BASED SLEEP APNEA DETECTION USING ARDUINO." Journal of Mechanics in Medicine and Biology 19, no. 01 (February 2019): 1940006. http://dx.doi.org/10.1142/s0219519419400062.

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The purpose of this study was to investigate the use of a cost-effective heart rate monitor sensor and Arduino Uno configuration to accurately detect simulated sleep apnea, through the use of the inter-beat interval (R-R interval). Three separate 30[Formula: see text]min heart rate recordings were taken, each with six simulated sleep apnea events ranging from 20 to 40[Formula: see text]s. The results were gathered and processed to identify the simulated sleep apnea events. In each of the recordings, the simulated sleep apnea events were visible and the key characteristics, surrounding the events, could be recognized. The heart rate monitor sensor and Arduino Uno configuration successfully detected the simulated sleep apnea events through the analysis and processing of the hearts R-R interval.

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3

., S. Thulasi Prasad. "HEART RATE DETECTION USING HILBERT TRANSFORM." International Journal of Research in Engineering and Technology 02, no. 11 (November 25, 2013): 508–13. http://dx.doi.org/10.15623/ijret.2013.0211076.

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4

Bulckaert, Arnoud, Vasileios Exadaktylos, Guido De Bruyne, Bart Haex, Elke De Valck, Johan Wuyts, Johan Verbraecken, and Daniel Berckmans. "Heart rate-based nighttime awakening detection." European Journal of Applied Physiology 109, no. 2 (January 23, 2010): 317–22. http://dx.doi.org/10.1007/s00421-010-1359-0.

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5

Vicente, José, Pablo Laguna, Ariadna Bartra, and Raquel Bailón. "Drowsiness detection using heart rate variability." Medical & Biological Engineering & Computing 54, no. 6 (January 16, 2016): 927–37. http://dx.doi.org/10.1007/s11517-015-1448-7.

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6

Piotrowski, Z., and K. Różanowski. "Robust Algorithm for Heart Rate (HR) Detection and Heart Rate Variability (HRV) Estimation." Acta Physica Polonica A 118, no. 1 (July 2010): 131–35. http://dx.doi.org/10.12693/aphyspola.118.131.

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7

Patial, Payal, and Kawaldeep Singh. "Heart Rate Variability Analysis and Pathological Detection." International Journal of Computer Applications 70, no. 6 (May 17, 2013): 42–49. http://dx.doi.org/10.5120/11970-7825.

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8

Zhao, Yudan, and Chaoyu Wang. "Heart Rate Detection Based on Facial Video." Journal of Information Hiding and Privacy Protection 3, no. 3 (2021): 121–30. http://dx.doi.org/10.32604/jihpp.2021.026380.

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9

Mitsukura, Yasue, Koichi Fukunaga, Masato Yasui, and Masaru Mimura. "Sleep stage detection using only heart rate." Health Informatics Journal 26, no. 1 (February 19, 2019): 376–87. http://dx.doi.org/10.1177/1460458219827349.

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Getting enough quality sleep plays a vital role in protecting our mental health, physical health, and quality of life. Sleep deprivation can make it difficult to concentrate on daily activities, and lower sleep quality is associated with hypertension, hyperglycemia, and hyperlipidemia. The amount of sleep we get is important, but in recent years, quality sleep has also been deemed significant. Polysomnography, which has been the gold standard in assessing sleep quality based on stages, requires that the subject be attached to electrodes, which can disrupt sleep. An easier method to objectively measure sleep is therefore needed. The aim of this study was to construct an easy and objective sleep stage monitoring method. A cross-sectional study for healthy subjects has been done in our research. A new easy model for monitoring the sleep stages is built on only heart rate calculated by the electrocardiogram. This enabled us to easily assess the sleep quality based on five stages. This experiment included a total of 50 subjects. The overall accuracy in determining the five sleep stages was 66.0 percent. Four stages for sleep are identified accurately compared with other conventional methods. Despite there are no five sleep stage separation method using only heart rate, our method achieved the five separation for sleep with a relatively good accuracy. This study represents a great contribution to the field of sleep science. Because sleep stages can be recognized by the heart rate alone, sleep can be noninvasively assessed with any heart rate meter. This method will make it easier to determine sleep stages and diagnose sleep disorders.

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10

Gonzalez-Landaeta, R., O. Casas, and R. Pallas-Areny. "Heart Rate Detection From Plantar Bioimpedance Measurements." IEEE Transactions on Biomedical Engineering 55, no. 3 (March 2008): 1163–67. http://dx.doi.org/10.1109/tbme.2007.906516.

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11

Solem, Kristian, Pablo Laguna, Juan Pablo MartÍnez, and Leif SÖrnmo. "Model-Based Detection of Heart Rate Turbulence." IEEE Transactions on Biomedical Engineering 55, no. 12 (December 2008): 2712–22. http://dx.doi.org/10.1109/tbme.2008.2002113.

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12

Boardman, A., F. S. Schlindwein, N. V. Thakor, T. Kimura, and R. G. Geocadin. "Detection of asphyxia using heart rate variability." Medical & Biological Engineering & Computing 40, no. 6 (November 2002): 618–24. http://dx.doi.org/10.1007/bf02345299.

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13

Yhuwana, Y. G. Yhun, R. Apsari, and M. Yasin. "Fiber optic sensor for heart rate detection." Optik 134 (April 2017): 28–32. http://dx.doi.org/10.1016/j.ijleo.2017.01.035.

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14

HPatil, Dipali. "Stress Detection by Measuring Heart Rate Variability." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 4 (2015): 2083–89. http://dx.doi.org/10.17762/ijritcc2321-8169.150470.

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15

S., Gowrishankar, Prachita M., and Arvind Prakash. "IoT based Heart Attack Detection, Heart Rate and Temperature Monitor." International Journal of Computer Applications 170, no. 5 (July 17, 2017): 26–30. http://dx.doi.org/10.5120/ijca2017914840.

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16

Gondane, Jyoti, and Meena S. Panse. "Development of an Optical System for Non-Contact Type Measurement of Heart Rate and Heart Rate Variability." Applied System Innovation 4, no. 3 (July 28, 2021): 48. http://dx.doi.org/10.3390/asi4030048.

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Self-mixing optical coherent detection is a non-contact measurement technique which provides accurate information about the vibration frequency of any test subject. In this research, novel designs of optical homodyne and heterodyne detection techniques are explained. Homodyne and heterodyne setups are used for measuring the frequency of the modulated optical signal. This technique works on the principle of the optical interferometer, which provides a coherent detection of two self-mixing beams. In the optical homodyne technique, one of the two beams receives direct modulation from the vibration frequency of the test subject. In the optical heterodyne detection technique, one of the two optical beams is subjected to modulation by an acousto-optics modulator before becoming further modulated by the vibration frequency of the test subject. These two optical signals form an interference pattern that contains the information of the vibration frequency. The measurement of cardiovascular signals, such as heart rate and heart rate variability, are performed with both homodyne and heterodyne techniques. The optical coherent detection technique provides a high accuracy for the measurement of heart period and heart rate variability. The vibrocardiogram output obtained from both techniques are compared for different heart rate values. Results obtained from both optical homodyne and heterodyne detection techniques are compared and found to be within 1% of deviation value. The results obtained from both the optical techniques have a deviation of less than 1 beat per minute from their corresponding ECG values.

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17

Cheng, Yong Bin, Li Qiang Zhu, and Da Cheng Bi. "Design and Implementation of a Heart Rate Detection Device Based on Ballistocardiogram." Applied Mechanics and Materials 401-403 (September 2013): 1234–38. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.1234.

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In this study, a non-load blanket device for heart rate detection was designed, based on the function of ballistocardiogram (BCG) to characterize each heart beat. A bed blanket with underlaid pressure-sensitive cables was employed to simultaneously collect the BCGs and record the amplitude as well as frequency of heart beat in real time. The noise jamming was reasonably eliminated according to the Mallat fast algorithm with characteristics of BCG retained in the process. The obtained results of ballistocar-waveforms with obvious characters and values of cardiac rate demonstrated that good effects could be obtained by our heart beat detection device. This study proves to be a significant attempt for the BCG application on physiological parameters detections.

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18

Garcia-Agundez, Augusto, Christian Reuter, Polona Caserman, Robert Konrad, and Stefan Göbel. "Identifying Cybersickness through Heart Rate Variability alterations." International Journal of Virtual Reality 19, no. 1 (January 1, 2019): 1–10. http://dx.doi.org/10.20870/ijvr.2019.19.1.2907.

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Numerous users of Head Mounted Displays develop symptoms similar to motion sickness. This phenomenon is known as cybersickness. The current standard method to evaluate it is the Simulator Sickness Questionnaire (SSQ). Additionally, detection by methods such as biosignals is possible, but it requires sophisticated equipment and expertise. In order to detect early cybersickness symptoms, the availability of real-time detection by using simple equipment, such as a 2-lead ECG, would be highly useful. This contribution aims to analyze the feasibility of detecting cybersickness with a simple 2 lead ECG. A total of 13 participants played the VR game QuakeVR wearing an Oculus Rift DK2 for 15 minutes, during which a 2-lead ECG was recorded. Addiionally, pre- and post-SSQ questionnaires were given. Four of 13 participants had to end the experiment prematurely due to cybersickness. The difference in SSQ scores is statistically significant (p<.01), as is the ECG (p=.02), in these participants. This study shows the utility of a simple 2-lead ECG to detect cybersickness. These findings raise the possibility of real-time monitoring and prediction of cybersickness with simple devices and open the question of whether photoplethysmography could be used with the same purpose.

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19

González-Landaeta, R., O. Casas, and R. Pallàs-Areny. "Heart rate detection from an electronic weighing scale." Physiological Measurement 29, no. 8 (July 18, 2008): 979–88. http://dx.doi.org/10.1088/0967-3334/29/8/009.

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20

Malarvili, M. B., and M. Mesbah. "Newborn Seizure Detection Based on Heart Rate Variability." IEEE Transactions on Biomedical Engineering 56, no. 11 (November 2009): 2594–603. http://dx.doi.org/10.1109/tbme.2009.2026908.

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21

Xie, Yuting, Jianqing Li, Tingting Zhu, and Chengyu Liu. "Continuous-Valued Annotations Aggregation for Heart Rate Detection." IEEE Access 7 (2019): 37664–71. http://dx.doi.org/10.1109/access.2019.2902619.

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22

Skotte, Jørgen H., and Jesper Kristiansen. "Heart rate variability analysis using robust period detection." BioMedical Engineering OnLine 13, no. 1 (2014): 138. http://dx.doi.org/10.1186/1475-925x-13-138.

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23

Kim, Dong-Jun. "Enhancement of Heart Rate Detection using Oscillometric Method." Journal of Korea Institute of Information, Electronics, and Communication Technology 7, no. 1 (March 31, 2014): 50–54. http://dx.doi.org/10.17661/jkiiect.2014.7.1.050.

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24

Das, Sangita, Saurabh Pal, and Madhuchhanda Mitra. "Arduino-based noise robust online heart-rate detection." Journal of Medical Engineering & Technology 41, no. 3 (January 12, 2017): 170–78. http://dx.doi.org/10.1080/03091902.2016.1271044.

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25

Landreani, Federica, and Enrico Gianluca Caiani. "Smartphone accelerometers for the detection of heart rate." Expert Review of Medical Devices 14, no. 12 (November 26, 2017): 935–48. http://dx.doi.org/10.1080/17434440.2017.1407647.

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26

Yang, Lei, Ming Liu, Liquan Dong, Yuejin Zhao, and Xiaohua Liu. "Motion-compensated non-contact detection of heart rate." Optics Communications 357 (December 2015): 161–68. http://dx.doi.org/10.1016/j.optcom.2015.08.017.

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27

J, Gaurav. "Heart Rate detection using Photoplethysmography using Android Phone." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1009–11. http://dx.doi.org/10.17762/ijritcc2321-8169.150324.

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28

Dos, Vinícius, Edson D., and Talles M. "Heart Rate Variability in the Detection of Scares." International Journal of Computer Applications 143, no. 12 (June 17, 2016): 36–40. http://dx.doi.org/10.5120/ijca2016910184.

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29

Salai, Mario, István Vassányi, and István Kósa. "Stress Detection Using Low Cost Heart Rate Sensors." Journal of Healthcare Engineering 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/5136705.

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The automated detection of stress is a central problem for ambient assisted living solutions. The paper presents the concepts and results of two studies targeted at stress detection with a low cost heart rate sensor, a chest belt. In the device validation study (n=5), we compared heart rate data and other features from the belt to those measured by a gold standard device to assess the reliability of the sensor. With simple synchronization and data cleaning algorithm, we were able to select highly (>97%) correlated, low average error (2.2%) data segments of considerable length from the chest data for further processing. The protocol for the clinical study (n=46) included a relax phase followed by a phase with provoked mental stress, 10 minutes each. We developed a simple method for the detection of the stress using only three time-domain features of the heart rate signal. The method produced accuracy of 74.6%, sensitivity of 75.0%, and specificity of 74.2%, which is impressive compared to the performance of two state-of-the-art methods run on the same data. Since the proposed method uses only time-domain features, it can be efficiently implemented on mobile devices.

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30

Ye, Ning, Yu Ge Sun, and Dan Yang. "Noninvasive Heart Rate Variability Detection Device for Fatigue Driving Detection System." Applied Mechanics and Materials 246-247 (December 2012): 194–98. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.194.

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Monitor psychological parameters of driver to detect fatigue state is an effective approach to prevent traffic accident. Heart rate variability (HRV) has its particular advantage comparing with other methods, such as its accuracy and conveniences. ECG is a regular signal to obtain HRV, but during driving condition, electrodes and wires need to be placed on driver’s body and may disturb the driver’s normal driving behavior. Since ballistocardiogram (BCG) can reflect heart movement, so HRV can also be extracted from BCG. This paper gives a novel noninvasive method to detect driver’s BCG. Using PVDF sensor which is embedded in safety belt to get driver's BCG and designing hardware and software to amplify and convert it to digital signal for next processing. The result shows that the proposed device can obtain the driver’s BCG properly and the HRV of the driver can be calculated accurately and conveniently, so the design is reasonable.

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31

Knapp-Kline, Kelley, and John P. Kline. "Heart rate, heart rate variability, and heartbeat detection with the method of constant stimuli: slow and steady wins the race." Biological Psychology 69, no. 3 (July 2005): 387–96. http://dx.doi.org/10.1016/j.biopsycho.2004.09.002.

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32

TRIPATHY, R. K., MARIO R. ARRIETA PATERNINA, and P. PATTANAIK. "A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740001. http://dx.doi.org/10.1142/s0219519417400012.

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Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.

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33

Mohammed, Norazlin, and Junaidah Binti Idrus. "Study on Heart Rate Visualisation Using Combination of Real Time Heart Rate Detection and Augmented Reality." Indian Journal of Public Health Research & Development 10, no. 4 (2019): 1236. http://dx.doi.org/10.5958/0976-5506.2019.00881.7.

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34

T, Vyasaraj, and Veena N. Hegde. "Heart Rate Variability-A Review." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 26, 2021): 1241–46. http://dx.doi.org/10.51201/jusst/21/07296.

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Information about the health can be obtained by measuring various physiological parameters such as Heart Rate, Heart Rate Variability (HRV), Nerve conduction, brain activity, blood oxygen saturation level etc. The useful information resulted from the systematic analysis of these physical parameters are helpful for clinicians to make better decisions. HRV reflects the state of the Autonomic Nervous System (ANS) defined as the variance in the time between successive heartbeats expressed in milliseconds. The various factors that affects HRV are diet, nutrition, age, alcohol, gender, cardiac rhythm, sleep habits, genetics etc. The analysis of HRV is helpful in stress assessment and also in identifying the diseases at the early stage. This paper discusses the fundamentals of HRV, analysis of HRV, and the role of HRV in stress detection.

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35

De Cooman, Thomas, Carolina Varon, Anouk Van de Vel, Katrien Jansen, Berten Ceulemans, Lieven Lagae, and Sabine Van Huffel. "Adaptive nocturnal seizure detection using heart rate and low-complexity novelty detection." Seizure 59 (July 2018): 48–53. http://dx.doi.org/10.1016/j.seizure.2018.04.020.

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36

Wahyuni, Yuli, and Muhammad Abdulrohman Pany. "HEART RATE DETECTION OF STRESS LEVELS FOR PREGNANT WOMEN." Indonesian Journal of Applied Research (IJAR) 3, no. 1 (May 12, 2022): 46–55. http://dx.doi.org/10.30997/ijar.v3i1.182.

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During pregnancy, there are changes because the fetus begins to grow and develop in the pregnant woman's stomach. Physiology greatly influences its development, especially in hormonal and metabolic changes that affect the psychological stress level of pregnant women. This study aims to determine the initial action to detect heart rate by looking at stress levels in pregnant women. The results of stress measurements are not normal when the heart rate is less than 60 bpm and more than 100 bpm, while in conditions of more than 60 bpm and less than 100 bpm, the results obtained are normal conditions. This study uses a hardware programming approach with the stages: Project Planning, Research, Component Testing, Mechanical System Design, Functional Test, Functional Test, Overall System Functional Test, System Optimization. The results show that this tool has worked well in monitoring heart rate when stressed in real-time using a pulse sensor displayed via telegram notifications in the form of total data. Still, because it is only in the form of notifications, there is no storage in the form of a database.

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37

Oweis, Rami J., Hala As’ad, Amany Aldarawsheh, Rawan Al-Khdeirat, and Kaldoun Lwissy. "A PC-aided optical foetal heart rate detection system." Journal of Medical Engineering & Technology 38, no. 1 (November 7, 2013): 23–31. http://dx.doi.org/10.3109/03091902.2013.849299.

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38

Zeng, Wu, Yi Sheng, Qiuyu Hu, Zhanxiong Huo, Yingge Zhang, and Yuxuan Xie. "Heart Rate Detection Using SVM Based on Video Imagery." Intelligent Automation & Soft Computing 32, no. 1 (2022): 377–87. http://dx.doi.org/10.32604/iasc.2022.017748.

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39

Muttaqin, Hidayatul, Ita Arfyanti, and Wahyuni. "Android Based Heart Rate Detection Tools with Arduino Nano." TEPIAN 2, no. 1 (March 13, 2021): 1–6. http://dx.doi.org/10.51967/tepian.v2i1.337.

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Android-based Heart Rate Detector Using an Android-Based Fingerprint Using Arduino Nano at Midwife Dwi Inggrini's Maternity Clinic with the hope of helping and simplifying the medical team in checking the heart rate of pregnant women without having to carry devices that are not portable, improving services and errors due to blackouts PLN electricity. The software development method used is the prototype method which includes data collection, design, prototyping, the testing phase by conducting Black Box and White Box testing. To access this tool the user must first connect the bluetooth android device with bluetooth HC-05 on the Arduino device, after the two Bluetooth devices are connected.

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40

Moss, Travis J., Douglas E. Lake, and J. Randall Moorman. "Local dynamics of heart rate: detection and prognostic implications." Physiological Measurement 35, no. 10 (September 17, 2014): 1929–42. http://dx.doi.org/10.1088/0967-3334/35/10/1929.

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41

L.V, Rajani Kumari, Padma Sai Y, and Balaji N. "Arduino Based Abnormal Heart Rate Detection and Wireless Communication." International Journal on Cybernetics & Informatics 5, no. 4 (August 30, 2016): 47–53. http://dx.doi.org/10.5121/ijci.2016.5406.

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42

Hamilton, Emily F., Michael C. Glaude, and Maciej Macieszczak. "Accuracy of Computerized Detection of Fetal Heart Rate Patterns." Obstetrics & Gynecology 97, Supplement (April 2001): 4S. http://dx.doi.org/10.1097/00006250-200104001-00006.

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43

Doyle, O. M., A. Temko, W. Marnane, G. Lightbody, and G. B. Boylan. "Heart rate based automatic seizure detection in the newborn." Medical Engineering & Physics 32, no. 8 (October 2010): 829–39. http://dx.doi.org/10.1016/j.medengphy.2010.05.010.

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44

Hamilton, E. "Accuracy of computerized detection of fetal heart rate patterns." Obstetrics & Gynecology 97, no. 5 (May 2001): S4. http://dx.doi.org/10.1016/s0029-7844(01)01137-1.

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45

Zhou, Liqian, Ming Yin, Xi Xu, Xinpan Yuan, and Xiaojun Liu. "Non-contact detection of human heart rate with Kinect." Cluster Computing 22, S4 (January 19, 2018): 8199–206. http://dx.doi.org/10.1007/s10586-018-1716-z.

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46

Cho, Hui-Sup, Young-Jin Park, Hong-Kun Lyu, and Jin-Ho Cho. "Novel Heart Rate Detection Method Using UWB Impulse Radar." Journal of Signal Processing Systems 87, no. 2 (September 3, 2016): 229–39. http://dx.doi.org/10.1007/s11265-016-1177-7.

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47

Linhares, Raquel Romes. "Arrhythmia detection from heart rate variability by SDFA method." International Journal of Cardiology 224 (December 2016): 27–32. http://dx.doi.org/10.1016/j.ijcard.2016.08.286.

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48

Cho, Hui-Sup, and Young-Jin Park. "Detection of Heart Rate through a Wall Using UWB Impulse Radar." Journal of Healthcare Engineering 2018 (2018): 1–7. http://dx.doi.org/10.1155/2018/4832605.

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Measuring the physiological functions of the human body in a noncontact manner through walls is useful for healthcare, security, and surveillance. And radar technology can be used for this purpose. In this paper, a new method for detecting the human heartbeat using ultra wideband (UWB) impulse radar, which has advantages of low power consumption and harmlessness to human body, is proposed. The heart rate is extracted by processing the radar signal in the time domain and then using a principal component analysis of the time series data to indicate the phase variations that are caused by heartbeats. The experimental results show that a highly accurate detection of heart rate is possible with this method.

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49

Cepeda, Eduardo, Diego H. Peluffo-Ordóñez, Paúl Rosero-Montalvo, Miguel A. Becerra, Ana C. Umaquinga-Criollo, and Lenin Ramírez. "Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results." Bionatura 7, no. 3 (September 15, 2022): 1–8. http://dx.doi.org/10.21931/rb/2022.07.03.30.

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Real-time vital signs monitoring, particularly heart rate, is essential in today's medical practice and research. Heart rate detection allows the doctor to monitor the patient's health status to provide immediate action against possible cardiovascular diseases. We present a possible alternative to traditional heart rate signal monitoring systems, a cardiac pulse system using low-cost piezoelectric signal identification. This system could benefit health care and develop continuous pulse waveform monitoring systems. This paper introduces a heartbeat per minute (BPM) cardiac pulse detection system based on a low-cost piezoelectric ceramic sensor (PCS). The PCS is placed under the wrist and adjusted with a silicone wristband to measure the pressure exerted by the radial artery on the sensor and thus obtain the patient's BPM. We propose a signal conditioning stage to reduce the sensor's noise when acquiring the data and make it suitable for real-time BPM visualization. As a comparison, we performed a statistical test to compare the low-cost PCS with types of traditional sensors, along with the help of 21 volunteers. Experimental results show that the data collected by the PCS, when used for heart rate detection, is highly accurate and close to traditional sensor measurements. Therefore, we conclude that the system efficiently monitors the cardiac pulse signal in BPM. Keywords: Heart rate; Piezoelectric, BPM; Pulse Detection.

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50

Rashtian, Hootan, Solmaz Shariat Torbaghan, Salar Rahili, Michael Snyder, and Nima Aghaeepour. "Heart Rate and CGM Feature Representation Diabetes Detection From Heart Rate: Learning Joint Features of Heart Rate and Continuous Glucose Monitors Yields Better Representations." IEEE Access 9 (2021): 83234–40. http://dx.doi.org/10.1109/access.2021.3085544.

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