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Heart Rate

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Cost-Sensitive Learning for Anomaly Detection in Imbalanced ECG Data Using Convolutional Neural Networks.

Sensors (Basel, Switzerland)
Arrhythmia detection algorithms based on deep learning are attracting considerable interest due to their vital role in the diagnosis of cardiac abnormalities. Despite this interest, deep feature representation for ECG is still challenging and intrigu...

A Neuromorphic Model With Delay-Based Reservoir for Continuous Ventricular Heartbeat Detection.

IEEE transactions on bio-medical engineering
There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model ...

ANNet: A Lightweight Neural Network for ECG Anomaly Detection in IoT Edge Sensors.

IEEE transactions on biomedical circuits and systems
In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG) anomaly detection and system level power reduction of wearable Internet of Things (IoT) Edge sensors. The proposed network utilizes a novel hybrid architectu...

Research on recognition and classification of pulse signal features based on EPNCC.

Scientific reports
To rapidly obtain the complete characterization information of pulse signals and to verify the sensitivity and validity of pulse signals in the clinical diagnosis of related diseases. In this paper, an improved PNCC method is proposed as a supplement...

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

Sensors (Basel, Switzerland)
The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to com...

Multimodal driver state modeling through unsupervised learning.

Accident; analysis and prevention
Naturalistic driving data (NDD) can help understand drivers' reactions to each driving scenario and provide personalized context to driving behavior. However, NDD requires a high amount of manual labor to label certain driver's state and behavioral p...

Heart rate variability for medical decision support systems: A review.

Computers in biology and medicine
Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from...

Deep convolutional neural network-based signal quality assessment for photoplethysmogram.

Computers in biology and medicine
Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was t...

Robust PVC Identification by Fusing Expert System and Deep Learning.

Biosensors
Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early wa...

Intelligent monitoring of noxious stimulation during anaesthesia based on heart rate variability analysis.

Computers in biology and medicine
Research based on medical signals has received significant attention in recent years. If the patients' states can be accurately monitored based on medical signals, it greatly benefits both doctors and patients. This paper proposes a method to extract...