Kasidit Kokkhunthod¹, Khomdet Phapatanaburi², Wongsathon Pathonsuwan¹, Talit Jumphoo¹, Patikorn Anchuen³, Porntip Nimkuntod⁴, Monthippa Uthansakul¹, Peerapong Uthansakul^1,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1775-1794, 2024, DOI:10.32604/cmc.2024.049276 - 15 May 2024

Abstract Monitoring blood pressure is a critical aspect of safeguarding an individual’s health, as early detection of abnormal blood pressure levels facilitates timely medical intervention, ultimately leading to a reduction in mortality rates associated with cardiovascular diseases. Consequently, the development of a robust and continuous blood pressure monitoring system holds paramount significance. In the context of this research paper, we introduce an innovative deep learning regression model that harnesses phonocardiogram (PCG) data to achieve precise blood pressure estimation. Our novel approach incorporates a convolutional neural network (CNN)-based regression model, which not only enhances its adaptability to… More >

Pablo Saiz-Gonzalez^1,2, Daniel J. McDonough³, Wenxi Liu⁴, Zan Gao^1,*

International Journal of Mental Health Promotion, Vol.25, No.5, pp. 711-719, 2023, DOI:10.32604/ijmhp.2023.027530 - 28 April 2023

Abstract Virtual reality (VR) seems to have the potential to provide opportunities to promote physical activity (PA) in a fun way. This paper aimed to examine the acute effects of three different virtual reality-based exercise bikes on young adults’ blood pressure (BP) and feelings compared to a traditional exercise cycling session. Four exercise sessions (immersive VR cycling, two non-immersive VR cycling, and traditional cycling) were completed by 36 young adults (22 females; M_age = 23.6 years). BP was measured immediately before and after each session using a BP cuff and exercise-induced feelings were assessed via an established… More >

Shu-Yin Chiang^*, Dong-Ye Wu

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2509-2526, 2023, DOI:10.32604/cmc.2023.036466 - 31 March 2023

Abstract Physiological signals indicate a person’s physical and mental state at any given time. Accordingly, many studies extract physiological signals from the human body with non-contact methods, and most of them require facial feature points. However, under COVID-19, wearing a mask has become a must in many places, so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research. In this study, RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate, blood pressure, respiratory… More >

Hoon Ko¹, Chul Park², Wu Seong Kang³, Yunyoung Nam⁴, Dukyong Yoon⁵, Jinseok Lee^1,*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2369-2381, 2023, DOI:10.32604/csse.2023.035641 - 09 February 2023

Abstract Adequate oxygen in red blood cells carrying through the body to the heart and brain is important to maintain life. For those patients requiring blood, blood transfusion is a common procedure in which donated blood or blood components are given through an intravenous line. However, detecting the need for blood transfusion is time-consuming and sometimes not easily diagnosed, such as internal bleeding. This study considered physiological signals such as electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure, oxygen saturation (SpO2), and respiration, and proposed the machine learning model to detect the need for blood transfusion accurately. For More >

E. Afreen Banu^1,*, V. Rajamani²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2469-2487, 2023, DOI:10.32604/csse.2023.021332 - 01 August 2022

Abstract In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a… More >

V. Ravindra Krishna Chandar^1,*, M. Thangamani²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 165-181, 2022, DOI:10.32604/iasc.2022.022339 - 15 April 2022

Abstract

During patient monitoring, false alert in the Intensive Care Unit (ICU) becomes a major problem. In the category of alarms, pseudo alarms are regarded as having no clinical or therapeutic significance, and thus they result in fatigue alarms. Artifacts are misrepresentations of tissue structures produced by imaging techniques. These Artifacts can invalidate the Arterial Blood Pressure (ABP) signal. Therefore, it is very important to develop algorithms that can detect artifacts. However, ABP has algorithmic shortcomings and limitations of design. This study is aimed at developing a real-time enhancement of independent component analysis (EICA) and time-domain

… More >

Chih-Ta Yen^1,*, Cheng-Hong Liao²

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1973-1986, 2022, DOI:10.32604/cmc.2022.022679 - 03 November 2021

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 More >

Chih-Ta Yen^1,*, 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 - 27 September 2021

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… More >

Eva Kapravelou¹, Hugo Issa², Gordon Culham³, Martin Hosking¹, Sanjiv K. Gandhi², Shubhayan Sanatani^1,*

Congenital Heart Disease, Vol.16, No.6, pp. 675-680, 2021, DOI:10.32604/CHD.2021.016548 - 08 July 2021

Abstract A 4-month-old previously healthy baby was found to be in congestive heart failure with LV dysfunction and a right aortic arch with severe coarctation, undetectable by blood pressure measurements. A cardiac CT and central blood pressure led to the diagnosis of a unique anatomic variant of aortic coarctation. Once diagnosed the patient underwent surgery with an uncomplicated recovery. More >

Hongyan Ai^1,*, Yan Wang², Hongmei Gu²

Oncologie, Vol.23, No.2, pp. 185-193, 2021, DOI:10.32604/Oncologie.2021.016420 - 22 June 2021

Abstract Objective: To explore the effect of the cluster nursing mode combined with blood pressure regulation on the surgical tolerance of patients with esophageal cancer and hypertension. Methods: The clinical data of 86 patients with esophageal cancer and hypertension treated in our hospital (February 2016–February 2017) were retrospectively analyzed. The patients were randomly split into research group and reference group, with 43 cases in each group. The reference group received routine nursing, while the research group received cluster nursing combined with blood pressure regulation. The SBP, DBP and heart rates at D1 (at admission), D2 (2 h… More >