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Heart Rate

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Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The classification of the heartbeat type is an essential function in the automatical electrocardiogram (ECG) analysis algorithm. The guideline of the ANSI/AAMI EC57 defined five types of heartbeat: non-ectopic or paced beat (N), supraventricular ecto...

Precise Heart Rate Measurement Using Non-contact Doppler Radar Assisted by Machine-Learning-Based Sleep Posture Estimation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Non-contact and continuous heart rate measurement using Doppler radar is important for various healthcare applications. In this paper, we propose a precise heart rate measurement method assisted by machine learning based sleep posture estimation. Mac...

[Automatic classification method of arrhythmia based on discriminative deep belief networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Existing arrhythmia classification methods usually use manual selection of electrocardiogram (ECG) signal features, so that the feature selection is subjective, and the feature extraction is complex, leaving the classification accuracy usually affect...

Obstructive sleep apnea syndrome detection based on ballistocardiogram via machine learning approach.

Mathematical biosciences and engineering : MBE
Obstructive sleep apnea (OSA) is a common sleep-related respiratory disease that affects people's health, especially in the elderly. In the traditional PSG-based OSA detection, people's sleep may be disturbed, meanwhile the electrode slices are easil...

Feasibility study: Towards Estimation of Fatigue Level in Robot-Assisted Exercise for Cardiac Rehabilitation.

IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Socially Assistive Robotics (SAR) has shown to be an important tool to assist patients in physical rehabilitation. SAR is used to provide feedback about patient's state and performance to users and health professionals, therefore, patients are monito...

[Deep residual convolutional neural network for recognition of electrocardiogram signal arrhythmias].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Electrocardiogram (ECG) signals are easily disturbed by internal and external noise, and its morphological characteristics show significant variations for different patients. Even for the same patient, its characteristics are variable under different...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Medicine
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratifi...

Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Electrical storm (ES) is a serious arrhythmic syndrome that is characterized by recurrent episodes of ventricular arrhythmias. Electrical storm is associated with increased mortality and morbidity despite the use of implantable cardioverter-def...

Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure.

Support vector machine-based assessment of the T-wave morphology improves long QT syndrome diagnosis.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology
AIMS: Diagnosing long QT syndrome (LQTS) is challenging due to a considerable overlap of the QTc-interval between LQTS patients and healthy controls. The aim of this study was to investigate the added value of T-wave morphology markers obtained from ...