HeartPy: A novel heart rate algorithm for the analysis of noisy signals

Gent, Paul van; Farah, Haneen; Nes, Nicole van; Arem, Bart van

doi:10.1016/j.trf.2019.09.015

Cited by 146 publications

(97 citation statements)

References 36 publications

(42 reference statements)

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“…Then, for every reliable five-minute signal, we calculated the root mean square of the successive differences (RMSSD) of NNIs, the standard deviation of all NNIs (SDNN), low-frequency (LF) power (0.04–0.15 Hz), high-frequency (HF) power (0.15–0.4 Hz) and the LF-to-HF ratio (LF/HF). The analysis was implemented using the HeartPy and SciPy libraries in Python [ 24 ]. The outliers were removed, and the average values of HRV parameters during sleep were obtained.…”

Section: Methodsmentioning

confidence: 99%

Pregnant women’s daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology

et al. 2021

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Background Technology enables the continuous monitoring of personal health parameter data during pregnancy regardless of the disruption of normal daily life patterns. Our research group has established a project investigating the usefulness of an Internet of Things–based system and smartwatch technology for monitoring women during pregnancy to explore variations in stress, physical activity and sleep. The aim of this study was to examine daily patterns of well-being in pregnant women before and during the national stay-at-home restrictions related to the COVID-19 pandemic in Finland. Methods A longitudinal cohort study design was used to monitor pregnant women in their everyday settings. Two cohorts of pregnant women were recruited. In the first wave in January-December 2019, pregnant women with histories of preterm births (gestational weeks 22–36) or late miscarriages (gestational weeks 12–21); and in the second wave between October 2019 and March 2020, pregnant women with histories of full-term births (gestational weeks 37–42) and no pregnancy losses were recruited. The final sample size for this study was 38 pregnant women. The participants continuously used the Samsung Gear Sport smartwatch and their heart rate variability, and physical activity and sleep data were collected. Subjective stress, activity and sleep reports were collected using a smartphone application developed for this study. Data between February 12 to April 8, 2020 were included to cover four-week periods before and during the national stay-at-home restrictions. Hierarchical linear mixed models were exploited to analyze the trends in the outcome variables. Results The pandemic-related restrictions were associated with changes in heart rate variability: the standard deviation of all normal inter-beat intervals (p = 0.034), low-frequency power (p = 0.040) and the low-frequency/high-frequency ratio (p = 0.013) increased compared with the weeks before the restrictions. Women’s subjectively evaluated stress levels also increased significantly. Physical activity decreased when the restrictions were set and as pregnancy proceeded. The total sleep time also decreased as pregnancy proceeded, but pandemic-related restrictions were not associated with sleep. Daily rhythms changed in that the participants overall started to sleep later and woke up later. Conclusions The findings showed that Finnish pregnant women coped well with the pandemic-related restrictions and lockdown environment in terms of stress, physical activity and sleep.

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Section: Methodsmentioning

confidence: 99%

Pregnant women’s daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology

et al. 2021

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“…NeuroKit2 aims at being reliable and trustworthy, including peer-reviewed processing pipelines and functions tested against established software such as BioSPPy (Carreiras et al, 2015), hrv under review, PySiology (Gabrieli, Azhari, & Esposito, 2019), HeartPy (Gent, Farah, Nes, & Arem, 2019), systole (Legrand & Allen, 2020) or nolds (Schölzel, 2019). The repository leverages a comprehensive test suite and continuous integration to ensure stability and prevent errors.…”

Section: Neurokit2: a Python Toolbox For Neurophysiological Signal Processingmentioning

confidence: 99%

NeuroKit2: A Python toolbox for neurophysiological signal processing

et al. 2021

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NeuroKit2 is an open-source, community-driven, and user-friendly Python package dedicated to neurophysiological signal processing with an initial focus on bodily signals (e.g., ECG, EDA, EMG, EOG, PPG etc.). Its design philosophy is centred on user-experience and accessibility to both novice and advanced users. The package provides a consistent set of high-level functions that enable data processing in a few lines of code using validated pipelines, which we illustrate in two examples covering the most typical scenarios, such as an event-related paradigm and an interval-related analysis. The package also includes tools dedicated to specific processing steps such as rate extraction and filtering methods, offering a trade-off between efficiency and fine-tuned control to the user.Rather than focusing on specific signals, NeuroKit2 was developed to provide a comprehensive means for a simultaneous processing of a wide range of signals. Its goal is to improve transparency and reproducibility in neurophysiological research, as well as foster exploration and innovation.

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“…Peak detection. To identify the correct peak locations, we slightly modified an existing algorithm 19 . Our current algorithm consists of: (i) compute the CMA of the processed signal and align it to its signal; (ii) locate the maximum of a specific region of the signal continuously above its moving average; (iii) label the set of maxima as partial peaks, repeating with the window width w i = (0.5, 1, 1.5, 2, 2.5, 3) * sf (with sf = average sampling frequency), and consider as possible maxima those points labelled 6 times as partial peaks; (iv) if two possible maxima are separated by less than 400ms, discard the smaller of the two.…”

Section: Ppg Preprocessingmentioning

confidence: 99%

Prediction of vascular aging based on smartphone acquired PPG signals

Dall’Olio

Curti

Remondini

et al. 2020

Sci Rep

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Photoplethysmography (PPG) measured by smartphone has the potential for a large scale, non-invasive, and easy-to-use screening tool. Vascular aging is linked to increased arterial stiffness, which can be measured by PPG. We investigate the feasibility of using PPG to predict healthy vascular aging (HVA) based on two approaches: machine learning (ML) and deep learning (DL). We performed data preprocessing, including detrending, demodulating, and denoising on the raw PPG signals. For ML, ridge penalized regression has been applied to 38 features extracted from PPG, whereas for DL several convolutional neural networks (CNNs) have been applied to the whole PPG signals as input. The analysis has been conducted using the crowd-sourced Heart for Heart data. The prediction performance of ML using two features (AUC of 94.7%) – the a wave of the second derivative PPG and tpr, including four covariates, sex, height, weight, and smoking – was similar to that of the best performing CNN, 12-layer ResNet (AUC of 95.3%). Without having the heavy computational cost of DL, ML might be advantageous in finding potential biomarkers for HVA prediction. The whole workflow of the procedure is clearly described, and open software has been made available to facilitate replication of the results.

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HeartPy: A novel heart rate algorithm for the analysis of noisy signals

Cited by 146 publications

References 36 publications

Pregnant women’s daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology

Pregnant women’s daily patterns of well-being before and during the COVID-19 pandemic in Finland: Longitudinal monitoring through smartwatch technology

NeuroKit2: A Python toolbox for neurophysiological signal processing

Prediction of vascular aging based on smartphone acquired PPG signals

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