scholarly journals Artificial Intelligence in education: Using heart rate variability (HRV) as a biomarker to assess emotions objectively

2021 ◽  
Vol 2 ◽  
pp. 100011
Author(s):  
Joanne Wai Yee Chung ◽  
Henry Chi Fuk So ◽  
Marcy Ming Tak Choi ◽  
Vincent Chun Man Yan ◽  
Thomas Kwok Shing Wong
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Artificial Intelligence In Education

Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study

2010 ◽  
Vol 121 (12) ◽  
pp. 2024-2034 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
D. Conforti ◽  
G. Dolce
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Brain Processing

The Development and Research Trends of Artificial Intelligence in Neuroscience: A Scientometric Analysis in CiteSpace

2013 ◽  
Vol 718-720 ◽  
pp. 2068-2073 ◽  
Author(s):  
Gan Ping Ma
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Research Trends ◽  
Scientometric Analysis ◽  
Research Fields ◽  
Emerging Trends ◽  
Neuroscience Research ◽  
Tight Connection ◽  
Research Frontiers

Artificial intelligence (AI) is an interdiscipline that aims to create and enhance the intelligence of machines and robots. Neuroscience has a tight connection with AI, which is also one of the earliest research fields that neuroscience attempted to carry out. This paper focused on the development and research trends of AI in neuroscience with the help of a latest scientometric tool, CiteSpace II. It allowed us to grasp the research frontiers and trends of AI in neuroscience through the analysis of data concerning AI and neuroscience between 1990 and 2012. We found that cluster #5 heart rate variability was most likely to be the emerging trends and some technologies will be more frequently used in neuroscience research.

scholarly journals Artificial-Intelligence-Based Prediction of Clinical Events among Hemodialysis Patients Using Non-Contact Sensor Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2833 ◽  
Author(s):  
Saurabh Singh Thakur ◽  
Shabbir Syed Abdul ◽  
Hsiao-Yean (Shannon) Chiu ◽  
Ram Babu Roy ◽  
Po-Yu Huang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Supervised Machine Learning ◽  
Sensor Data ◽  
Clinical Settings ◽  
Clinical Events ◽  
Contact Sensor ◽  
Contact Sensors ◽  
Vital Parameters

Non-contact sensors are gaining popularity in clinical settings to monitor the vital parameters of patients. In this study, we used a non-contact sensor device to monitor vital parameters like the heart rate, respiration rate, and heart rate variability of hemodialysis (HD) patients for a period of 23 weeks during their HD sessions. During these 23 weeks, a total number of 3237 HD sessions were observed. Out of 109 patients enrolled in the study, 78 patients reported clinical events such as muscle spasms, inpatient stays, emergency visits or even death during the study period. We analyzed the sensor data of these two groups of patients, namely an event and no-event group. We found a statistically significant difference in the heart rates, respiration rates, and some heart rate variability parameters among the two groups of patients when their means were compared using an independent sample t-test. We further developed a supervised machine-learning-based prediction model to predict event or no-event based on the sensor data and demographic information. A mean area under curve (ROC AUC) of 90.16% with 96.21% mean precision, and 88.47% mean recall was achieved. Our findings point towards the novel use of non-contact sensors in clinical settings to monitor the vital parameters of patients and the further development of early warning solutions using artificial intelligence (AI) for the prediction of clinical events. These models could assist healthcare professionals in taking decisions and designing better care plans for patients by early detecting changes to vital parameters.

scholarly journals Artificial intelligence for heart rate variability analyzing with arrhythmias

2019 ◽  
Vol 14 (15) ◽  
pp. 46-52
Author(s):  
Gianfranko Raimondi ◽  
◽  
Aleksander Martynenko ◽  
S. Ostropolets ◽  
N. Marchitto ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Heart Rate Variability

scholarly journals Explainable artificial intelligence for heart rate variability in ECG signal

2020 ◽  
Vol 7 (6) ◽  
pp. 146-154
Author(s):  
Sanjana K. ◽  
Sowmya V. ◽  
Gopalakrishnan E.A. ◽  
Soman K.P.
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Ecg Signal ◽  
Explainable Artificial Intelligence
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