Heart Rate and Heart Rate Variability Measuring System by Using Smartphone

Khairuddin, Muhammad; Nakamoto, Keita; Nakamura, Hiroshi; Tanaka, Keita; Nakashima, Shota

doi:10.1109/acit-csii-bcd.2017.23

Cited by 7 publications

(1 citation statement)

References 4 publications

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“…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]. As explained in [11] the RR intervals are obtained by calculating the difference between two R waves in the QRS complex.…”

Section: Introductionmentioning

confidence: 99%

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

Km¹,

Gl²

2021

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%