scholarly journals Responses of Heart Beat during Naughton Protocol Test

1987 ◽  
Vol 2 (2) ◽  
pp. 125-128
Author(s):  
Tadashi OSHIGE
Keyword(s):  
Heart Beat ◽  
Protocol Test

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

Remote Heart Beat Monitoring System

2019 ◽  
Vol 7 (1) ◽  
pp. 5-10
Author(s):  
Saman Shahid ◽  
Saima Zafar ◽  
Mansoor Imam ◽  
Muhammad Usman Chishtee ◽  
Haris Ehsan
Keyword(s):  
Heart Diseases ◽  
Cost Effective ◽  
Heart Beat ◽  
The Internet ◽  
Android Application ◽  
Development Environment ◽  
Future Assessment ◽  
Pulse Sensor ◽  
Hospital Visits ◽  
Monitoring Module

There is an increased prevalence of heart diseases in developing countries and continuous monitoring of heart beats is very much important to reduce hospital visits, health costs and complications. The Internet of Things (IoT) equipped with microcontrollers and sensors can give an easy and cost-effective remote health monitoring. We developed a Heart Beat monitoring module based on an android application. The software involved was the Android Application developed using Android Studio, which is the Integrated Development Environment (IDE). This app retrieved the data from the open IoT platform thingspeak.com. A highly sensitive Pulse Sensor was used to measure the heartbeat of the patient automatically. An Arduino Uno microcontroller interfaced with a Wi-Fi module ESP8266 used to transmit pulse reading over the internet using Wi-Fi. The heartbeat was displayed on the LCD of the patient in run-time. The heartbeat in beats per minute (BPM) was plotted against time (minutes). A mounted pulse sensor to the patient had monitored the heartbeat and transmitted it in the form of voltage signal to the microcontroller, which converted it back into a mathematical value. The Arduino transmitted the data onto the thingspeak.com portal, where it was plotted on a graph and the values were stored for future assessment. The user of the app was given a things peak API and the channel number as an access code, through which physician or nurse can accessed the patient’s data. IoT based heartbeat module as an android application can provide a convenient, cost effective and continuous remote measurements for heart patients to help physicians and nurses update. This app can reduce the burden of hospital visits or admissions for elderly patients.

Survey on Safety Devices Using IoT

2020 ◽  
pp. 450-453
Keyword(s):  
Traffic Congestion ◽  
Detection System ◽  
Text Message ◽  
Emergency Situation ◽  
Heart Beat ◽  
Advanced Technology ◽  
Night Time ◽  
Remote Areas ◽  
Alert Message ◽  
Automatic Detection System

Traffic congestion is becoming a huge problem, which is arising due to vehicle failure or accidents. Transportation and use of advanced technology has great importance in society and that has made many of our lives much easier. By automatic accident detection and alerting GSM & GPS based technology can be used to overcome these problems. Where as in case of Child and Women there are very few efficient security and safety measures adopted. Now in India the safety for women has become a major issue while travelling. Nowadays women think twice before taking any steps out of their homes, especially in the night time. Hence, this is unfortunately, the sad reality of our country and also due to various crimes like child abuse, rape, dowry deaths, trafficking and many more. At the time of women facing unsecured situations, there is a need to ensure safety while travelling. Hence automatic detection system needs to be established where one can send alert message to the police station or the relatives which detects the current location of the required ones by use of such technologies the women and children can get protection. Mainly in remote areas children use bicycles as means of transport from several years and nowadays, despite due to the large vailability of new and faster means, the bicycle users is not decreased. Despites the cyclists find difficult to travel within them and other vehicles find difficult to find them during night time. In case of any emergency situation faced at unknown remote areas the cyclist can send their location to required ones to help them. In this paper, report the survey on the existing mechanism for detecting locations, and sending signals and to collect parameters such as temperature of the human body, heart beat etc. using sensors. With the help of GPS and GSM we can track the location of the child, women or vehicle. Hence, by these we can save the life of person’s being injured in various locations by sending a text message using IOT technologies

Exploring the effects of oily fish consumption on measures of acute and long-term stress in healthy 8-9-year-old children: The FiSK Junior randomized trial

2020 ◽  
pp. 1-26
Author(s):  
Marie N Teisen ◽  
Stine Vuholm ◽  
Jesper M Rantanen ◽  
Jeppe H Christensen ◽  
Camilla T Damsgaard ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Fish Consumption ◽  
Cold Pressor Test ◽  
Cold Pressor ◽  
Heart Beat ◽  
Autonomic Response ◽  
Hair Cortisol ◽  
Oily Fish ◽  
Fish Group

Abstract Long-chain n-3 PUFA (n-3 LCPUFA) have been shown to reduce blood pressure, heart rate and vagal tone, but potential stress-mitigating effects of n-3 LCPUFA are not well investigated. We aim to explore the effects of oily fish consumption on long-term stress and the stress response in schoolchildren. Healthy 8-9-year-old children were randomized to receive ~300 g/week of oily fish or poultry for 12 ± 2 weeks. At baseline and endpoint, we measured erythrocyte n-3 LCPUFA, hair cortisol and the response to a 1-min cold pressor test (CPT) on saliva cortisol, blood pressure, and continuous electrocardiogram recordings. Of the 199 randomized children, 197 completed the trial. Hair cortisol did not differ between the groups, but a sex-interaction was indicated (Psex*group = 0.074, difference between means -0.9 (95% CI: -2.9,1.0) ng/g and 0.7 (-0.2,1.6) ng/g in boys and girls, respectively). The children in the fish group tended to be less prone to terminate CPT prematurely (OR 0.20 [0.02,1.04]). The mean heart beat interval during CPT was 18.2 (0.3,36.6) ms longer and the high frequency power increased (159 (29,289) ms2) in the fish versus the poultry group. The cardiac autonomic response in the 10 min following CPT was characterized by a sympathetic peak followed by a parasympathetic peak, which was most pronounced in the fish group. This exploratory study does not support a strong effect of oily fish consumption on stress, but indicates that oily fish consumption may increase vagal cardiac tone during the physiological response to CPT. These results warrant further investigation.

Left ventricular hypertrophy detection system based on electrocardiogram signal

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
C.W Liu ◽  
R.H Pan ◽  
Y.L Hu
Keyword(s):  
Left Ventricular Hypertrophy ◽  
Decision Tree ◽  
Ventricular Hypertrophy ◽  
Detection System ◽  
Pearson Correlation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Left Ventricular ◽  
Heart Beat ◽  
Knowledge Engineer

Abstract Background Left ventricular hypertrophy (LVH) is associated with increased risks of cardiovascular diseases. Electrocardiography (ECG) is generally used to screen LVH in general population and electrocardiographic LVH is further confirmed by transthoracic echocardiography (Echo). Purpose We aimed to establish an ECG LVH detection system that was validated by echo LVH. Methods We collected the data of ECGs and Echo from the previous database. The voltage of R- and S-amplitude in each ECG lead were measured twice by a study assistance blinded to the study design, (artificially measured). Another knowledge engineer analyzed row signals of ECG (the algorithm). We firstly check the correlation of R- and S-amplitude between the artificially measured and the algorythm. ECG LVH is defined by the voltage criteria and Echo LVH is defined by LV mass index >115 g/m2 in men and >95 g/m2 in women. Then we use decision tree, k-means, and back propagation neural network (BPNN) with or without heart beat segmentation to establish a rapid and accurate LVH detection system. The ratio of training set to test set was 7:3. Results The study consisted of a sample size of 953 individuals (90% male) with 173 Echo LVH. The R- and S-amplitude were highly correlated between artificially measured and the algorithm R- and S-amplitude regarding that the Pearson correlation coefficient were >0.9 in each lead (the highest r of 0.997 in RV5 and the lowest r of 0.904 in aVR). Without heart beat segmentation, the accuracy of decision tree, k-means, and BPNN to predict echo LVH were 0.74, 0.73 and 0.51, respectively. With heart beat segmentation, the signal of Echo LVH expanded to 1466, and the accuracy to predict ECG LVH were obviously improved (0.92 for decision tree, 0.96 for k-means, and 0.59 for BPNN). Conclusions Our study showed that machine-learning model by BPNN had the highest accuracy than decision trees and k-means based on ECG R- and S-amplitude signal analyses. Figure 1. Three layers of the decision tree Funding Acknowledgement Type of funding source: None

scholarly journals On the Stability of a Stochastic Nonlinear Model of the Heart Beat Rate During a Treadmill Exercise

2020 ◽  
Vol 53 (2) ◽  
pp. 16457-16461
Author(s):  
Mohammad Mostafa Asheghan ◽  
Bahram Shafai ◽  
Joaquín Míguez
Keyword(s):  
Nonlinear Model ◽  
Treadmill Exercise ◽  
Heart Beat ◽  
Beat Rate ◽  
Heart Beat Rate ◽  
The Stability
Sign in / Sign up

Export Citation Format

Share Document