Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

Jiao, Changzhe; Chen, Chao; Gou, Shuiping; Hai, Dong; Su, Bo-Yu; Skubic, Marjorie; Jiao, Licheng; Zare, Alina; Ho, K. C.

doi:10.1109/jbhi.2021.3077002

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…Jung et al used the Probability Density Function and fused multi-channel data to achieve a MAE of 1.78 bpm in the supine position, based on the data collected from eleven healthy subjects (Jung et al, 2021). Jiao et al reported a remarkable performance of the LSTM network with the lowest MAE of 0.68 (Jiao et al, 2021). However, it should be noted that Jiao's MAE of heart rate was computed and averaged every 15 s on the test data.…”

Section: Discussionmentioning

confidence: 99%

Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition

Feng,

Huang,

Jiang

et al. 2023

Front. Physiol.

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The Ballistocardiogram (BCG) is a vibration signal that is generated by the displacement of the entire body due to the injection of blood during each heartbeat. It has been extensively utilized to monitor heart rate. The morphological features of the BCG signal serve as effective indicators for the identification of atrial fibrillation and heart failure, holding great significance for BCG signal analysis. The IJK-complex identification allows for the estimation of inter-beat intervals (IBI) and enables a more detailed analysis of BCG amplitude and interval waves. This study presents a novel algorithm for identifying the IJK-complex in BCG signals, which is an improvement over most existing algorithms that only perform IBI estimation. The proposed algorithm employs a short-time Fourier transform and summation across frequencies to initially estimate the occurrence of the J wave using peak finding, followed by Ensemble Empirical Mode Decomposition and a regional search to precisely identify the J wave. The algorithm’s ability to detect the morphological features of BCG signals and estimate heart rates was validated through experiments conducted on 10 healthy subjects and 2 patients with coronary heart disease. In comparison to commonly used methods, the presented scheme ensures accurate heart rate estimation and exhibits superior capability in detecting BCG morphological features. This advancement holds significant value for future applications involving BCG signals.

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Section: Discussionmentioning

confidence: 99%

Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition

Feng,

Huang,

Jiang

et al. 2023

Front. Physiol.

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“…This resulted in numerous studies that focused on providing techniques to better understand the current health status of patients ( Odendaal et al, 2019 ; Diab et al, 2021 ). Such studies that focus on providing techniques for monitoring the health status of the heart include, but are not limited to, support vector machines to aid the auscultation procedure using computed tomography scan images () machine learning model based on activity tracker data to classify patient health status ( Meng et al, 2020 ), a bidirectional long short-term memory (bi-LSTM) regression network for noninvasive heart rate (HR) estimation from ballistocardiogram signals ( Jiao et al, 2021 ), a binary classification model for assessing the neonatal heart and lung sound quality for the heart, and breathing rate estimation for telehealth applications ( Grooby et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A deep learning framework for noninvasive fetal ECG signal extraction

Wahbah,

Zitouni,

Al Sakaji

et al. 2024

Front. Physiol.

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Introduction: The availability of proactive techniques for health monitoring is essential to reducing fetal mortality and avoiding complications in fetal wellbeing. In harsh circumstances such as pandemics, earthquakes, and low-resource settings, the incompetence of many healthcare systems worldwide in providing essential services, especially for pregnant women, is critical. Being able to continuously monitor the fetus in hospitals and homes in a direct and fast manner is very important in such conditions.Methods: Monitoring the health of the baby can potentially be accomplished through the computation of vital bio-signal measures using a clear fetal electrocardiogram (ECG) signal. The aim of this study is to develop a framework to detect and identify the R-peaks of the fetal ECG directly from a 12 channel abdominal composite signal. Thus, signals were recorded noninvasively from 70 pregnant (healthy and with health conditions) women with no records of fetal abnormalities. The proposed model employs a recurrent neural network architecture to robustly detect the fetal ECG R-peaks.Results: To test the proposed framework, we performed both subject-dependent (5-fold cross-validation) and independent (leave-one-subject-out) tests. The proposed framework achieved average accuracy values of 94.2% and 88.8%, respectively. More specifically, the leave-one-subject-out test accuracy was 86.7% during the challenging period of vernix caseosa layer formation. Furthermore, we computed the fetal heart rate from the detected R-peaks, and the demonstrated results highlight the robustness of the proposed framework.Discussion: This work has the potential to cater to the critical industry of maternal and fetal healthcare as well as advance related applications.

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“…V. CONCLUSIONES Las redes de tipo LSTM son capaces de representar relaciones en señales biológicas como lo mostrado en [9] y [10] donde se llevó a cabo un modelo de predicción de patrones depresivos y estimación del ritmo cardiaco a partir de señales de EEG y BCG respectivamente. La implementación de este estudio es capaz de representar la señal oculográfica incluyendo los movimientos sacádicos del ojo gracias a las señales de referencia de EOG, así como de un seguimiento en el tiempo dentro de un límite de 0.5 segundos.…”

Section: A Salida De Los Sensoresunclassified

Modelo no paramétrico por redes LSTM del reflejo vestíbulo-ocular en respuesta a aceleraciones inducidas

Macias-Hernandez¹

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The following work introduces a non-parametric, long-short term memory (LSTM) neural network model to represent the vestibulo-ocular reflex in response to induced accelerations. This study includes the development of a device to obtain the data required to train the model including inertial measurement units and electrocolugraph (EOG) sensors. The model seeks to mimic the biological response of the vestibular apparatus and nuclei through the obtention of accelerometer and gyroscope data while attempting to track a reference set by the EOG measurements in the training process of the LSTM identifier. The implementation of learning laws that guarantee the modeling error is converging the origin justifies the implementation of LSTMs as artificial models of the vestibulo-ocular reflexes. The modeling results show a short convergence time and a low mean squared error obtained after a cycle of measuring analysis, thus showing that the model can successfully track the proposed EOG reference and can be used to assess the response of people in induced acceleration conditions such as flying simulation, vestibular rehabilitation and some other.

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Non-Invasive Heart Rate Estimation From Ballistocardiograms Using Bidirectional LSTM Regression

Cited by 11 publications

References 34 publications

Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition

Non-invasive monitoring of cardiac function through Ballistocardiogram: an algorithm integrating short-time Fourier transform and ensemble empirical mode decomposition

A deep learning framework for noninvasive fetal ECG signal extraction

Modelo no paramétrico por redes LSTM del reflejo vestíbulo-ocular en respuesta a aceleraciones inducidas

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