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  • Open Access

    ARTICLE

    Heartbeat and Respiration Rate Prediction Using Combined Photoplethysmography and Ballisto Cardiography

    Valarmathi Ramasamy1,*, Dhandapani Samiappan2, R. Ramesh3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1365-1380, 2023, DOI:10.32604/iasc.2023.032155

    Abstract Owing to the recent trends in remote health monitoring, real-time applications for measuring Heartbeat Rate and Respiration Rate (HARR) from video signals are growing rapidly. Photo Plethysmo Graphy (PPG) is a method that is operated by estimating the infinitesimal change in color of the human face, rigid motion of facial skin and head parts, etc. Ballisto Cardiography (BCG) is a nonsurgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses. The resilience against motion artifacts induced by luminance fluctuation and the patient’s mobility variation is the major difficulty faced… More >

  • Open Access

    ARTICLE

    An Improved Approach to the Performance of Remote Photoplethysmography

    Yi Sheng1, Wu Zeng1,*, Qiuyu Hu1, Weihua Ou2, Yuxuan Xie3, Jie Li1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2773-2783, 2022, DOI:10.32604/cmc.2022.027985

    Abstract Heart rate is an important metric for determining physical and mental health. In recent years, remote photoplethysmography (rPPG) has been widely used in characterizing physiological signals in human subjects. Currently, research on non-contact detection of heart rate mainly focuses on the capture and separation of spectral signals from video imagery. However, this method is very sensitive to the movement of the test subject and light intensity variation, and this results in motion artifacts which presents challenges in extracting accurate physiological signals such as heart rate. In this paper, an improved method for rPPG signal preprocessing is proposed. Based on the… More >

  • Open Access

    ARTICLE

    Blood Pressure and Heart Rate Measurements Using Photoplethysmography with Modified LRCN

    Chih-Ta Yen1,*, Cheng-Hong Liao2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1973-1986, 2022, DOI:10.32604/cmc.2022.022679

    Abstract In this study, single-channel photoplethysmography (PPG) signals were used to estimate the heart rate (HR), diastolic blood pressure (DBP), and systolic blood pressure (SBP). A deep learning model was proposed using a long-term recurrent convolutional network (LRCN) modified from a deep learning algorithm, the convolutional neural network model of the modified inception deep learning module, and a long short-term memory network (LSTM) to improve the model's accuracy of BP and HR measurements. The PPG data of 1,551 patients were obtained from the University of California Irvine Machine Learning Repository. How to design a filter of PPG signals and how to… More >

  • Open Access

    ARTICLE

    A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

    Chih-Ta Yen1,*, Sheng-Nan Chang2, Liao Jia-Xian3, Yi-Kai Huang3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2937-2952, 2022, DOI:10.32604/cmc.2022.020493

    Abstract This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital… More >

  • Open Access

    ARTICLE

    Non-Contact Real-Time Heart Rate Measurement Algorithm Based on PPG-Standard Deviation

    Jiancheng Zou1,*, Tianshu Chen1, Xin Yang2

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1029-1040, 2019, DOI:10.32604/cmc.2019.05793

    Abstract Heart rate is an important physiological parameter for clinical diagnosis, it can infer the health of the human body. Thus, efficient and accurate heart rate measurement is important for disease diagnosis and health monitoring. There are two ways to measure heart rate. One is contact type and the other is non-contact. Contact measurement methods include pulse cutting, electrocardiogram, etc. Because of the inconvenience of this method, a non-contact heart rate method has been proposed. Traditional non-contact measurement method based on image is collecting RGB three-channel signals in continuous video and selecting the average value of the green channel pixels as… More >

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