Different acquisition systems for heart rate variability analysis may lead to diverse outcomes

Júnior, Francisco Antônio Oliveira; Pereira, Reabias de Andrade; Silva, Alexandre; Alves, José Luiz de Brito; Costa‐Silva, João Henrique; Braga, Valdir de Andrade; Balarini, Camille M.

doi:10.1590/1414-431x2021e11720

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…Mobile devices like chest heart rate monitors and smart phones that have been synced with the transmitters are usually utilized for signal collection. The HRV analysis is done using external software [6]. Moreover, Google Fit, a Google smartphone program produced by Google, allows for the measurement of the number of heartbeats and respiration.…”

Section: Figure 1 Physiological-based Dms Componentsmentioning

confidence: 99%

“…[3,4], and mental health disorders such as anxiety and depression. Moreover, since the European Society of Cardiology and the North American Society of Electrophysiology [5] established recommendations for the use of HRV, this approach has become more widely used and is not only confined to neurology, surgery, exercise physiology, physical activity, and anesthesia [6,7]. HRV analysis in the time, frequency, and nonlinear domains has also been shown to be conceivable for the detection and identification of driver states [8,9] in driver status monitoring systems.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

Abdul Razak,

Sayed Ismail,

Yogarayan

et al. 2023

Civ Eng J

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Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains. Doi: 10.28991/CEJ-2023-09-09-013 Full Text: PDF

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Section: Figure 1 Physiological-based Dms Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

Abdul Razak,

Sayed Ismail,

Yogarayan

et al. 2023

Civ Eng J

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“…With lower sampling frequencies such as 50-100Hz, signal interpolation [4] is required as the signal is more sensitive to motion artifacts; while higher frequencies (e.g., above 500Hz) usually require better computational resources to evaluate the electrical activity of the heart. The key to calculating HRV metrics, however, is to accurately locate the QRS complexes [5] from ECG signals because the QRS complex in ECG indicates ventricular depolarizations with high amplitude changes in electrical potential. Calculating the change in duration between adjacent R-peaks gives an indication of HRV in the time domain.…”

Section: Introductionmentioning

confidence: 99%

“…The lower sampling frequency ECG recording affects frequency domain analysis in HRV. Different sample frequencies may have an unpredictable impact on the R-peak detection process, resulting in inconsistent HRV calculations [5].…”

Section: Introductionmentioning

confidence: 99%

Statistical Validation of An Automated Method for Calculating Time Domain Heart Rate Variability on The QT Dataset

Sun,

Guastella,

Boulton

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Heart rate variability measures calculated from electrocardiography recordings reflect social competence. Clinical assessments of social skills have found that reduced heart rate variability is related to differences in the development of social skills in children and increase the risk of mental disorders. Limited by widespread manual signal processing and R-peak detection in current clinical assessments, most literature reports only short-term baseline studies, with fewer studies reporting social interaction settings with prolonged recording. There is an urgent need for an automated physiological signal processing toolbox to detect R-peaks and perform heart rate variability measurements in social settings. This paper proposes a modified automated Neurokit2 toolbox with signal processing procedures similar to the MindWare software that requires manual inspection of R-peak locations. We calculate time domain heart rate variability metrics from the publicly available QT database by PhysioNet collected at resting states and under stress tests, mimicking social interaction stress scenarios. Statistical analysis conveys that heart rate variability metrics calculation applying both signal processing approaches using the Neurokit2 toolbox are statistically equivalent in comparison to the hand-labelled R-peaks from the QT database (n= 10 in the normal sinus rhythm group, and n= 6 in the ST Change group). Such validation results are crucial for the adoption of automated toolboxes for heart rate variability measures in social interaction assessments, where more movement and mood changes of participants are expected.Clinical Relevance-This contributes to the body of evidence of the reliability of the Neurokit2 toolbox for automatic cleaning of prolonged cardiac electrophysiological signals and calculation of heart rate variability in time-domain characterization in social interaction stress assessment.

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“…It has been proposed as a promising substitute indicator to estimate the LVEF levels and thus could be an alternative for the assessment of cardiac function. Moreover, HRV parameters can be derived from electrocardiography (ECG) recordings, which is the gold standard measurement of HRV (10) with non-invasiveness, a lower cost and an easier implementation procedure.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the left ventricular ejection fraction by machine learning algorithms based on heart rate variability parameters in patients with ischemic heart disease

Tian

Han

et al. 2022

Preprint

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Background The left ventricular ejection fraction (LVEF) plays a pivotal role in the diagnosis and prediction of ischemic heart disease (IHD). Current techniques to measure LVEF have led to some complications and are relatively expensive despite the high accuracy. Heart rate variability (HRV) is an alternative for the assessment of cardiac function and its related parameters are easily to be derived from electrocardiography. Objective This study aimed to investigate the corresponding relationship between LVEF and HRV in patients with IHD via prediction models developed by machine learning techniques and compare the accuracy of these models. Methods In this retrospective real-world study, patients with IHD admitted to Jinan 4th People’s hospital between January 2019 and December 2020 were randomly selected and divided into a derivation cohort and a validation cohort in a 7:3 ratio. LVEF were measured by color Doppler echocardiography while HRV-related parameters were derived from a 24h Holter electrocardiography recordings. Patient clinical features and HRV parameters were identified and used in the subsequent modelling process. Logistic regression, naïve Bayes, decision tree, gradient boosting, extreme gradient boosting (XGBoosting) and random forests were respectively applied to estimate the correlation between HRV and LVEF. Model performances were evaluated by the coefficient R2 (the closer the result is to 1, the better) and the accuracy (a precise prediction was defined as the accuracy ≥ 90%). Results A total of 179 patients were included in this study with 125 (mean age 67.03 ± 16.12, male 72 [53.7%]) in the derivation cohort and 54 (mean age 62.51 ± 12.97, male 19 [48.7%]) in the validation cohort. Six variables including sex, age, SDNN, SDANN, rMSSD and pNN50 were found to be correlated with LVEF. Gradient boosting and random forests outperformed other machine learning models, showing the best prediction performance (> 90%) in the prediction of both LVEF percentage (%) and LVEF stratifications (< 40%, 40–50%, > 50%). These six indicators showed different contributions in different algorithms with sex as the least important indicator. Conclusion Gradient boosting and random forests machine learning models based on HRV- and patient characteristics-related parameters provided a new method in predicting LVEF in patients with IHD, which showed a potential in the diagnosis of IHD.

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Different acquisition systems for heart rate variability analysis may lead to diverse outcomes

Cited by 5 publications

References 34 publications

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

Statistical Validation of An Automated Method for Calculating Time Domain Heart Rate Variability on The QT Dataset

Prediction of the left ventricular ejection fraction by machine learning algorithms based on heart rate variability parameters in patients with ischemic heart disease

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