Heart Rate Variability Analysis: Application Overview

Londhe, Aboli N.; Atulkar, Mithilesh

doi:10.1109/icicct.2018.8473198

Cited by 6 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this paper, stress classification was performed mainly using HRV-derived features as studies have shown that HRV is impacted during changes in stress levels, given that it is highly controlled by the ANS [10]. Moreover, other investigations proved that RMSSD, AVNN and SDNN were evaluated as being the most reliable HRV metrics in distinguishing between stressful and non-stressful situations [28].…”

Section: Discussionmentioning

confidence: 99%

“…µ controls the speed of training and learning rate; this parameter was set to a range between 0 and 1 with intervals of 0.1. Finally, the size of the hidden layer corresponds to the number of layers and neurones in the hidden layer; the following hidden layer sizes were analysed (10, 30, 10), (4, 6, 3, 2), (20), (4,6,3), (10,20) and (100, 100, 400), where each value represent the number of neurons at its corresponding layer position. A configuration of h = 0.001, µ = 0.1 and three hidden layers of 100, 10 and 400 nodes, respectively, proved to be the optimal settings for the model following the grid search.…”

Section: Multilayer Perceptronmentioning

confidence: 99%

See 1 more Smart Citation

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

Dalmeida

Masala

2021

Sensors

View full text Add to dashboard Cite

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence, the study will be mainly focusing on heart rate variability (HRV). This study is aimed at investigating the role of HRV-derived features as stress markers. This is achieved by developing a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, by testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from heart rate measurements obtained from wearable devices. We demonstrate that HRV features constitute good markers for stress detection as the best machine learning model developed achieved a Recall of 80%. Furthermore, this study indicates that HRV metrics such as the Average of normal-to-normal (NN) intervals (AVNN), Standard deviation of the average NN intervals (SDNN) and the Root mean square differences of successive NN intervals (RMSSD) were important features for stress detection. The proposed method can be also used on all applications in which is important to monitor the stress levels in a non-invasive manner, e.g., in physical rehabilitation, anxiety relief or mental wellbeing.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Multilayer Perceptronmentioning

confidence: 99%

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

Dalmeida

Masala

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Сердечные изменения во вре менном интервале между двумя последовательны ми сердечными сокращениямиэто ВСР, также называемые колебаниями в интервалах между уда рами. Здоровое сердце ритмично и имеет сложную и колеблющуюся природу, которая обеспечивает внезапную и быструю корректировку физиологи ческих изменений гомеостаза [15].…”

Section: Discussionunclassified

Dynamics of Heart Rate Variability in Children Operated for Ventricular Septal Defect in Тне Process of Sanatorial Spa Treatment

Каладзе

Iushchenko

Lukyanenko³

et al. 2020

Herald of Physiotherapy and Health Resort Therapy

View full text Add to dashboard Cite

The purpose of the study is to study the effectiveness of medical rehabilitation in children operated on for a ventricular septal defect, based on the dynamics of heart rate variability indicators. Material and methods of research. Heart rate variability was evaluated in 75 children operated on for ventricular septal defect. The control group (CG) consisted of 30 healthy children. The effectiveness of medical rehabilitation was assessed by the dynamics of heart rate variability indicators. Children of the 1st group (20 people) took the cardiometabolic drug Elkar at a dose of 30 mg/kg/day for 1 month. With children of the 2nd group (20 people) for 3 months, Nordic walking classes were held (NW). In addition, children of the 2nd group received the drug Elkar for 1 month (30 mg/kg). Results. According to the time analysis of heart rate variability, operated children had significantly lower values of SDNN 82 [69;103] (p<0.001), SDANNi 50.5 [39;87] (p<0.05), SDNNi 69.5 [54;80] (p<0.05), rMSSD 50.5 [39;87] (p<0.05), pNN50 % 18.4 [6.7;28.3] (p<0.05) lower power in the high-frequency range of the spectrum (p<0.001) and a change in the vegetative balance towards the predominance of the sympathetic division of the autonomic nervous system (LF/HF=3.16, p=0.009) than in the control group. After the treatment, there was no reliable HRV dynamics in children who received Elcar. There was a significant difference between SDNN and rMSSD with KG. In the second group, there was a significant increase in HRV according to SDNN data by 37.7 % (p<0.01), SDANNi by 45.6 % (p<0.05), rMSSD by 6.5 % (p<0.05), pNN50 % by 32.7 % (p<0.05). The difference in indicators with CG is leveled. Conclusions. In children operated on for VSD, there was a decrease in the parameters of the time analysis of HRV in comparison with healthy peers (p<0.05). The combined use of Nordic walking and Elkar have a normalizing effect on the HRV, causing a decrease in the tension of the Central circuit of heart rate regulation, significantly increasing the parasympathetic effect on the heart rate.

show abstract

“…These are considered to be an accurate methodology for bio signal recording as they cannot be mask or conditioned by human voluntary actions. However, this study will be mainly focusing on HRV, which is controlled by PSN and SNS therefore, an imbalance in any functions regulated by these two nervous system branches will affect HRV [10]. HRV is the variation in interval between successive normal RR (or NN) intervals [11] it is derived from an ECG reading and it is measured by calculating the time interval between two consecutive peaks of the heartbeats [12].…”

Section: Introductionmentioning

confidence: 99%

Stress Classification of ECG-Derived HRV Features Extracted from Wearable Devices

Km¹,

Gl²

2021

Preprint

View full text Add to dashboard Cite

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence the study will be mainly focusing on the heart rate variability (HRV). This study is aimed to develop a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from HRV measurements obtained from wearable devices. We demonstrate that MLP was the ideal stress classifier by achieving a Recall of 80%. The proposed method can be also used on all applications in which is important to monitor the stress level e. g. in physical rehabilitation, anxiety relief or mental wellbeing.

show abstract

Heart Rate Variability Analysis: Application Overview

Cited by 6 publications

References 49 publications

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

HRV Features as Viable Physiological Markers for Stress Detection Using Wearable Devices

Dynamics of Heart Rate Variability in Children Operated for Ventricular Septal Defect in Тне Process of Sanatorial Spa Treatment

Stress Classification of ECG-Derived HRV Features Extracted from Wearable Devices

Contact Info

Product

Resources

About