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Heart Rate

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Synthetic seismocardiogram generation using a transformer-based neural network.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To design and validate a novel deep generative model for seismocardiogram (SCG) dataset augmentation. SCG is a noninvasively acquired cardiomechanical signal used in a wide range of cardivascular monitoring tasks; however, these approaches...

Machine learning-based evaluation of application value of pulse wave parameter model in the diagnosis of hypertensive disorder in pregnancy.

Mathematical biosciences and engineering : MBE
Hypertensive disorder in pregnancy (HDP) remains a major health burden, and it is associated with systemic cardiovascular adaptation. The pulse wave is an important basis for evaluating the status of the human cardiovascular system. This research aim...

Development and validation of a deep learning model to diagnose COVID-19 using time-series heart rate values before the onset of symptoms.

Journal of medical virology
One of the effective ways to minimize the spread of COVID-19 infection is to diagnose it as early as possible before the onset of symptoms. In addition, if the infection can be simply diagnosed using a smartwatch, the effectiveness of preventing the ...

[Anesthesia Depth Monitoring Based on Anesthesia Monitor with the Help of Artificial Intelligence].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: To use the low-cost anesthesia monitor for realizing anesthesia depth monitoring, effectively assist anesthesiologists in diagnosis and reduce the cost of anesthesia operation.

A novel method for conformity assessment testing of patient monitors for post-market surveillance purposes.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Patient monitors are medical devices used to monitor vital parameters such as heart rate, respiratory rate, blood pressure, blood oxygen saturation, and body temperature during inpatient treatment. As such, patient monitors provide physic...

A two-step method for paroxysmal atrial fibrillation event detection based on machine learning.

Mathematical biosciences and engineering : MBE
Detection of atrial fibrillation (AF) events is significant for early clinical diagnosis and appropriate intervention. However, in existing detection algorithms for paroxysmal AF (AFp), the location of AF starting and ending points in AFp is not conc...

A U - Net Deep Learning Model for Infant Heart Rate Estimation from Ballistography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Ballistography(BSG) is a non-intrusive and low- cost alternative to electrocardiography (ECG) for heart rate (HR) monitoring in infants. Due to the inter-patient variance and susceptibility to noise, heartbeat detection in the BSG waveform remains a ...

Lightweight neural network based model for real-time precise HR monitoring during high intensity workout using consumer smartwatches.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Mobile and wearable electronics is one of the rapidly developing areas of high technologies, which regularly appear new devices that offer new features for monitoring our health, level of physical exertion and everyday activity. From the point of vie...

Deep learning based non-contact physiological monitoring in Neonatal Intensive Care Unit.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based mon...

Deep-Learning based Sleep Apnea Detection using SpO2 and Pulse Rate.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This work presents automated apnea event de-tection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≄40 years) from the sleep heart health study da...