Embedded heart rate analysis based on sound sensing

2019 ◽  
Author(s):  
Maciej Rosol ◽  
Lukasz Wieckowski
Keyword(s):  
Heart Rate ◽  
Rate Analysis ◽  
Heart Rate Analysis

scholarly journals Fetal Heart Rate Analysis for Automatic Detection of Perinatal Hypoxia Using Normalized Compression Distance and Machine Learning

2017 ◽  
Vol 8 ◽  
Author(s):  
Óscar Barquero-Pérez ◽  
Ricardo Santiago-Mozos ◽  
José M. Lillo-Castellano ◽  
Beatriz García-Viruete ◽  
Rebeca Goya-Esteban ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Automatic Detection ◽  
Perinatal Hypoxia ◽  
Rate Analysis ◽  
Normalized Compression Distance ◽  
Heart Rate Analysis

scholarly journals Movement-induced heart rate changes in epileptic and non-epileptic seizures

2009 ◽  
Vol 67 (3b) ◽  
pp. 789-791 ◽  
Author(s):  
Gisele R. de Oliveira ◽  
Francisco de A.A. Gondim ◽  
Edward R. Hogan ◽  
Francisco H. Rola
Keyword(s):  
Heart Rate ◽  
Motor Activity ◽  
Rate Increase ◽  
Epileptic Seizures ◽  
Baseline Heart Rate ◽  
Heart Rate Increase ◽  
Event Duration ◽  
Rate Analysis ◽  
Heart Rate Analysis ◽  
Video Eeg

Heart rate changes are common in epileptic and non-epileptic seizures. Previous studies have not adequately assessed the contribution of motor activity on these changes nor have evaluated them during prolonged monitoring. We retrospectively evaluated 143 seizures and auras from 76 patients admitted for video EEG monitoring. The events were classified according to the degree of ictal motor activity (severe, moderate and mild/absent) in: severe epileptic (SE, N=17), severe non-epileptic (SNE, N=6), moderate epileptic (ME, N=28), moderate non-epileptic (MNE, N=11), mild epileptic (mE, N=35), mild non-epileptic (mNE, N=33) and mild aura (aura, N=13). Heart rate increased in the ictal period in severe epileptic, severe non-epileptic, moderate epileptic and mild epileptic events (p<0.05). Heart rate returned to baseline levels during the post ictal phase in severe non-epileptic seizures but not in severe epileptic patients. Aura events had a higher baseline heart rate. A cut-off of 20% heart rate increase may distinguish moderate epileptic and mild epileptic events lasting more than 30 seconds. In epileptic seizures with mild/absent motor activity, the magnitude of heart rate increase is proportional to the event duration. Heart rate analysis in seizures with different degrees of movement during the ictal phase can help to distinguish epileptic from non-epileptic events.

scholarly journals Analysis of Heart Rate Dynamics Before and During Meditation

2021 ◽  
Vol 17 (05) ◽  
pp. 100
Author(s):  
Mohammad Karimi Moridani ◽  
Tina Habikazemi ◽  
Nahid Khoramabadi
Keyword(s):  
Heart Rate ◽  
Vital Signs ◽  
Normal Subjects ◽  
Support Vector ◽  
Nonlinear Characteristics ◽  
Rate Analysis ◽  
High Tension ◽  
Heart Rate Dynamics ◽  
Heart Rate Analysis ◽  
Sensitivity Specificity

<p>Heart rate is one of the most important vital signs. People usually face high tension in routine life, and if we found an effective method to control the heart rate, it would be very desirable. One of the goals of this paper is to examine changes in heart rate before and during meditation. Another goal is that what impact could have meditation on the human heartbeat.</p><p>To heart rate analysis before and during meditation, available heart rate signals have been used for the Physionet database that contains 10 normal subjects and 8 subjects that meditation practice has been done on them. In this paper, first is paid to extract linear and nonlinear characteristics of heart rate and then is paid to the best combination of features to identify two intervals before and during meditation using MLP and SVM classifiers with the help of sensitivity, specificity and accuracy measurements.</p><p>The achieved results in this paper showed that choosing the best combination of a feature to make a meaningful difference between two intervals before and during meditation includes two-time features (Mean HR, SDNN), a frequency feature ( ), and three nonlinear characteristics   ( ). Also, using the support vector machine had better results than the MLP neural network. The sensitivity, specificity, and accuracy of the mean and standard deviation obtained respectively like 92.73  0.23, 89.05 0.67, 89.97 0.23 by using MLP and respectively like 95.96 0.09, 93.80 0.16, and 94.90 0.14 by using SVM.</p>As a result, using meditation can reduce the stress and anxiety of patients by effects on heart rate, and the treatment process speeds up and have an important role in improving the performance of the system.

scholarly journals Computerized fetal heart rate analysis in early preterm fetal growth restriction

2020 ◽  
Vol 56 (1) ◽  
pp. 51-60 ◽  
Author(s):  
H. Wolf ◽  
S. J. Gordijn ◽  
W. Onland ◽  
R. J. S. Vliegenthart ◽  
J. W. Ganzevoort
Keyword(s):  
Heart Rate ◽  
Fetal Growth ◽  
Fetal Growth Restriction ◽  
Fetal Heart Rate ◽  
Fetal Heart ◽  
Growth Restriction ◽  
Rate Analysis ◽  
Heart Rate Analysis

scholarly journals Heart rate analysis in normal subjects of various age groups

2004 ◽  
Vol 3 (1) ◽  
Author(s):  
Rajendra Acharya U ◽  
Kannathal N ◽  
Ong Wai Sing ◽  
Luk Yi Ping ◽  
TjiLeng Chua
Keyword(s):  
Heart Rate ◽  
Age Groups ◽  
Normal Subjects ◽  
Rate Analysis ◽  
Heart Rate Analysis
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