An elastic manifold learning approach to beat-to-beat interval estimation with ballistocardiography signals

Shen, Gang; Ding, Ruidong; Yang, Mingqi; Han, Dongyang; Zhang, Biyong

doi:10.1016/j.aei.2020.101051

Cited by 14 publications

(2 citation statements)

References 30 publications

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“…Future work could study the relationship between the heartbeat and head movement. Alternatively, other methods for detecting instantaneous HR from a BCG signal that have been previously developed [ 33 ], including machine learning–based heartbeat detection algorithms [ 34 , 35 ], may be able to increase the accuracy of heartbeat detection when the participants are lying on their side.…”

Section: Discussionmentioning

confidence: 99%

A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population

Gardner

Randhawa

Malouf³

et al. 2021

JMIR Cardio

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Background Obstructive sleep apnea (OSA) is a condition in which a person’s airway is obstructed during sleep, thus disturbing their sleep. People with OSA are at a higher risk of developing heart problems. OSA is commonly treated with a positive airway pressure (PAP) therapy device, which is used during sleep. The PAP therapy setup provides a good opportunity to monitor the heart health of people with OSA, but no simple, low-cost method is available for the PAP therapy device to monitor heart rate (HR). Objective This study aims to develop a simple, low-cost device to monitor the HR of people with OSA during PAP therapy. This device was then tested on a small group of participants to investigate the feasibility of the device. Methods A low-cost and simple device to monitor HR was created by attaching a gyroscope to a PAP mask, thus integrating HR monitoring into PAP therapy. The gyroscope signals were then analyzed to detect heartbeats, and a Kalman filter was used to produce a more accurate and consistent HR signal. In this study, 19 participants wore the modified PAP mask while the mask was connected to a PAP device. Participants lay in 3 common sleeping positions and then underwent 2 different PAP therapy modes to determine if these affected the accuracy of the HR estimation. Results Before the PAP device was turned on, the median HR error was <5 beats per minute, although the HR estimation error increased when participants lay on their side compared with when participants lay on their back. Using the different PAP therapy modes did not significantly increase the HR error. Conclusions These results show that monitoring HR from gyroscope signals in a PAP mask is possible during PAP therapy for different sleeping positions and PAP therapy modes, suggesting that long-term HR monitoring of OSA during PAP therapy may be possible.

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Section: Discussionmentioning

confidence: 99%

A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population

Gardner

Randhawa

Malouf³

et al. 2021

JMIR Cardio

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“…Many unobstructive sensing methods observing vital signs, such as heartbeat and respiration, have been evaluated for integration into unobstructive vital sensors that are based on the detection principle of air pressure [6][7][8], water pressure [9], cameras [10,11], microphones [12], load cells [13][14][15], piezoresistive [16], capacitively-coupled electrodes [17], and piezoelectric films [1,[18][19][20][21][22][23][24][25][26][27][28]. Among these, the sheet-type vital sensor using polyvinylidene fluoride (PVDF) as the pressure-sensitive layer is one of the most promising vital 2 of 12 sensors from the perspective of sensitivity and adaptability to various measurement environments [1,18,[21][22][23][24][25][26][27][28]. When the PVDF pressure-sensitive layer is distorted by an external force, the direction of the dipole inside the PVDF layer changes, and an electric charge is induced in the electrode.…”

Section: Introductionmentioning

confidence: 99%

Unobstructive Heartbeat Monitoring of Sleeping Infants and Young Children Using Sheet-Type PVDF Sensors

Kumaki,

Motoshima,

Higuchi

et al. 2023

Sensors

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Techniques for noninvasively acquiring the vital information of infants and young children are considered very useful in the fields of healthcare and medical care. An unobstructive measurement method for sleeping infants and young children under the age of 6 years using a sheet-type vital sensor with a polyvinylidene fluoride (PVDF) pressure-sensitive layer is demonstrated. The signal filter conditions to obtain the ballistocardiogram (BCG) and phonocardiogram (PCG) are discussed from the waveform data of infants and young children. The difference in signal processing conditions was caused by the physique of the infants and young children. The peak-to-peak interval (PPI) extracted from the BCG or PCG during sleep showed an extremely high correlation with the R-to-R interval (RRI) extracted from the electrocardiogram (ECG). The vital changes until awakening in infants monitored using a sheet sensor were also investigated. In infants under one year of age that awakened spontaneously, the distinctive vital changes during awakening were observed. Understanding the changes in the heartbeat and respiration signs of infants and young children during sleep is essential for improving the accuracy of abnormality detection by unobstructive sensors.

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Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

Liao

Liu

et al. 2021

Appl Intell

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An elastic manifold learning approach to beat-to-beat interval estimation with ballistocardiography signals

Cited by 14 publications

References 30 publications

A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population

A Wearable Ballistocardiography Device for Estimating Heart Rate During Positive Airway Pressure Therapy: Investigational Study Among the General Population

Unobstructive Heartbeat Monitoring of Sleeping Infants and Young Children Using Sheet-Type PVDF Sensors

Recognizing diseases with multivariate physiological signals by a DeepCNN-LSTM network

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