Measuring fine-grained heart-rate using a flexible wearable sensor in the presence of noise

Zhang, Lida; King, Zachary; Egilmez, Begum; Reeder, Jonathan T.; Ghaffari, Roozbeh; Li, Jinghua; Rosen, Kristen D.; Bass, Michael; Moskowitz, Judith T.; Tandon, Darius; Wakschlag, Lauren S.; Alshurafa, Nabil

doi:10.1109/bsn.2018.8329683

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Participants in the RCT are also instructed to take a break from sensor wear if they experience physical discomfort. To determine adherence, ECG and accelerometry signals determine signal quality and wear time (Zhang et al., 2018).…”

Section: Research Challenges/opportunities For Personalized Prenatal Intervention To Alter Neurodevelopmental Riskmentioning

confidence: 99%

Moving the dial on prenatal stress mechanisms of neurodevelopmental vulnerability to mental health problems: A personalized prevention proof of concept

Wakschlag

Tandon

Krogh-Jespersen

et al. 2020

Developmental Psychobiology

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Prenatal stress exposure increases vulnerability to virtually all forms of psychopathology. Based on this robust evidence base, we propose a “Mental Health, Earlier” paradigm shift for prenatal stress research, which moves from the documentation of stress‐related outcomes to their prevention, with a focus on infant neurodevelopmental indicators of vulnerability to subsequent mental health problems. Achieving this requires an expansive team science approach. As an exemplar, we introduce the Promoting Healthy Brain Project (PHBP), a randomized trial testing the impact of the Wellness‐4‐2 personalized prenatal stress‐reduction intervention on stress‐related alterations in infant neurodevelopmental trajectories in the first year of life. Wellness‐4‐2 utilizes bio‐integrated stress monitoring for just‐in‐time adaptive intervention. We highlight unique challenges and opportunities this novel team science approach presents in synergizing expertise across predictive analytics, bioengineering, health information technology, prevention science, maternal–fetal medicine, neonatology, pediatrics, and neurodevelopmental science. We discuss how innovations across many areas of study facilitate this personalized preventive approach, using developmentally sensitive brain and behavioral methods to investigate whether altering children's adverse gestational exposures, i.e., maternal stress in the womb, can improve their mental health outlooks. In so doing, we seek to propel developmental SEED research towards preventive applications with the potential to reduce the pernicious effect of prenatal stress on neurodevelopment, mental health, and wellbeing.

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Section: Research Challenges/opportunities For Personalized Prenatal Intervention To Alter Neurodevelopmental Riskmentioning

confidence: 99%

Moving the dial on prenatal stress mechanisms of neurodevelopmental vulnerability to mental health problems: A personalized prevention proof of concept

Wakschlag

Tandon

Krogh-Jespersen

et al. 2020

Developmental Psychobiology

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“…To remove noisy signals caused by sensor deformation because of skin stretching, we first segmented the cleaned ECG signal using a window size of 1 minute with 30 seconds of overlap. Noise was filtered using an ensemble support vector machine (SVM) and neural network noise model described by Zhang et al [ 40 ]. The model involves further segmenting of each 1-minute ECG signal into 0.6-second intervals, extracting 3 HRV-based features from the R peaks detected, running both pretrained SVM and neural network classifiers, and classifying each interval as clean or noisy based on agreement between both models.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Ng¹,

Wei²,

Jain³

et al. 2022

JMIR Mhealth Uhealth

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Background Cognitive behavioral therapy–based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can help to inform and enable pre-emptive interventions for a likely physiologically and perceptibly stressful day. Machine learning models are useful tools that can be developed to predict next-day physiological and perceived stress by using data collected from the previous day. Such models can improve our understanding of the specific factors that predict physiological and perceived stress and allow researchers to develop systems that collect selected features for assessment in clinical trials to minimize the burden of data collection. Objective The aim of this study was to build and evaluate a machine-learned model that predicts next-day physiological and perceived stress by using sensor-based, ecological momentary assessment (EMA)–based, and intervention-based features and to explain the prediction results. Methods We enrolled pregnant women into a prospective proof-of-concept study and collected electrocardiography, EMA, and cognitive behavioral therapy intervention data over 12 weeks. We used the data to train and evaluate 6 machine learning models to predict next-day physiological and perceived stress. After selecting the best performing model, Shapley Additive Explanations were used to identify the feature importance and explainability of each feature. Results A total of 16 pregnant women enrolled in the study. Overall, 4157.18 hours of data were collected, and participants answered 2838 EMAs. After applying feature selection, 8 and 10 features were found to positively predict next-day physiological and perceived stress, respectively. A random forest classifier performed the best in predicting next-day physiological stress (F1 score of 0.84) and next-day perceived stress (F1 score of 0.74) by using all features. Although any subset of sensor-based, EMA-based, or intervention-based features could reliably predict next-day physiological stress, EMA-based features were necessary to predict next-day perceived stress. The analysis of explainability metrics showed that the prolonged duration of physiological stress was highly predictive of next-day physiological stress and that physiological stress and perceived stress were temporally divergent. Conclusions In this study, we were able to build interpretable machine learning models to predict next-day physiological and perceived stress, and we identified unique features that were highly predictive of next-day stress that can help to reduce the burden of data collection.

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“…Denoising biomedical recordings from IoMT devices is a vital preprocessing step toward ML-ready dataset. Adaptive filters [6] and wavelet-based signal transformation [7] , [8] remove embedded noise, but inevitably distort the signal morphologies [4] , [9] . To minimize signal transformation, signal quality index (SQI) was introduced to measure and reject noises based on defined quality thresholds [10] .…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, many ML for healthcare applications preprocessed pulsatile recordings for models by applying light frequency filtering to remove noise from implausible frequencies, and constructed complex ML architectures to handle noise by automatic feature extraction [6] , [7] , [8] , [9] , [16] . Despite success in improving performance, ML models trained with excessive noise often led to decreased generalizability and overfitting [1] , [4] , [10] , [11] .…”

Section: Introductionmentioning

confidence: 99%

Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications

Huang,

Jafari,

Mortazavi

2024

IEEE Open J. Eng. Med. Biol.

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Goal: To establish Pulse2AI as a reproducible data preprocessing framework for pulsatile signals that generate high-quality machine-learning-ready datasets from raw wearable recordings. Methods: We proposed an end-to-end data preprocessing framework that adapts multiple pulsatile signal modalities and generates machine-learning-ready datasets agnostic to downstream medical tasks. Results: a dataset preprocessed by Pulse2AI improved systolic blood pressure estimation by 29.58%, from 11.41 to 8.03 mmHg in root-mean-square-error (RMSE) and its diastolic counterpart by 26.01%, from 7.93 to 5.87 mmHg in RMSE. For respiration rate (RR) estimation, Pulse2AI boosted performance by 19.69%, from 1.47 to 1.18 breaths per minute (BrPM) in mean-absolute-error (MAE). Conclusion: Pulse2AI turns pulsatile signals into machine learning (ML) ready datasets for arbitrary remote health monitoring tasks. We tested Pulse2AI on multiple pulsatile modalities and demonstrated its efficacy in two medical applications. This work bridges valuable assets in remote sensing and internet of medical things to ML-ready datasets for medical modeling.

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Measuring fine-grained heart-rate using a flexible wearable sensor in the presence of noise

Cited by 9 publications

References 13 publications

Moving the dial on prenatal stress mechanisms of neurodevelopmental vulnerability to mental health problems: A personalized prevention proof of concept

Moving the dial on prenatal stress mechanisms of neurodevelopmental vulnerability to mental health problems: A personalized prevention proof of concept

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Pulse2AI: An Adaptive Framework to Standardize and Process Pulsatile Wearable Sensor Data for Clinical Applications

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