An adaptive deep learning approach for PPG-based identification

Jindal, Vasu; Birjandtalab, Javad; Pouyan, Maziyar Baran; Nourani, Mehrdad

doi:10.1109/embc.2016.7592193

Cited by 67 publications

(46 citation statements)

References 13 publications

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“…Deep and machine learning and neural networks have also been used to classify the PPG signal. However, methods based on neural networks and fuzzy systems require training or self-tuning of adaptive parameters [127][128][129][130][131][132]. For example, a combined ECG/PPG signal within a nonlinear system, based on a reaction-diffusion mathematical model implemented using the cellular neural network (CNN) methodology, was employed to filter the PPG signal by assigning a recognition score to the waveforms in the time series [132].…”

Section: Motion Artefactsmentioning

confidence: 99%

Current progress of photoplethysmography and SPO2 for health monitoring

Tamura¹

2019

Biomed. Eng. Lett.

168

121

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A photoplethysmograph (PPG) is a simple medical device for monitoring blood flow and transportation of substances in the blood. It consists of a light source and a photodetector for measuring transmitted and reflected light signals. Clinically, PPGs are used to monitor the pulse rate, oxygen saturation, blood pressure, and blood vessel stiffness. Wearable unobtrusive PPG monitors are commercially available. Here, we review the principle issues and clinical applications of PPG for monitoring oxygen saturation.

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Section: Motion Artefactsmentioning

confidence: 99%

Current progress of photoplethysmography and SPO2 for health monitoring

Tamura¹

2019

Biomed. Eng. Lett.

168

121

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“…Thanks to the advances of training big capacity models, such as the typical DNNs, it is possible to leverage the big data for information extraction and representation learning. Recently, deep learning techniques start to be applied in various medical imaging tasks, including computed tomography (CT) images, 14 color fundus imaging, 15 magnetic resonance imaging, 16 and photoplethysmogram 17 . Notably, deep learning methods bring some exciting breakthroughs.…”

Section: Related Workmentioning

confidence: 99%

A multimodal deep architecture for traditional Chinese medicine diagnosis

Dai

Wang

Dai

et al. 2020

Concurrency and Computation

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Traditional Chinese medicine (TCM) is found on a long‐term medical practice in China. Rare human brains can fully grasp the deep TCM knowledge derived from a tremendous amount of experience. In this big data era, a big electronic brain might be competent via deep learning techniques. For this prospect, the electronic brain needs to process various heterogeneous data, such as images, texts, audio signals, and other sensory data. It used to be a challenge to analyze the heterogeneous data by the computer‐aided system until the advances of the powerful deep learning tools. We propose a multimodal deep learning framework to mimic a TCM practitioner to diagnose a patient on the basis of multimodal perceptions of see, listen, smell, ask, and touch. The framework learns common representations from various high‐dimensional sensory data, and fuse the information for final classification. We propose to use conceptual alignment deep neural networks to embed prior knowledge and obtain interpretable latent representations. We implement a multimodal deep architecture to process tongue image and description text data for TCM diagnosis. Experiments illustrate that the multimodal deep architecture can extract effective features from heterogeneous data, produce interpretable representations, and finally achieve a higher accuracy than either corresponding unimodal architectures.

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“…Contact pulse signals can also be identified using restricted Boltzmann machine and deep belief networks [62]. Deep recurrent neural network architectures, and in particular multilayerl ong short-term memory (LSTM), can be trained to predict arterial blood pressure from contact PPG and electrocardiogram signals [63].…”

Section: Machine Learningmentioning

confidence: 99%

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

2019

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Remote pulse rate measurement from facial video has gained particular attention over the last few years. Research exhibits significant advancements and demonstrates that common video cameras correspond to reliable devices that can be employed to measure a large set of biomedical parameters without any contact with the subject. A new framework for measuring and mapping pulse rate from video is presented in this pilot study. The method, which relies on convolutional 3D networks, is fully automatic and does not require any special image preprocessing. In addition, the network ensures concurrent mapping by producing a prediction for each local group of pixels. A particular training procedure that employs only synthetic data is proposed. Preliminary results demonstrate that this convolutional 3D network can effectively extract pulse rate from video without the need for any processing of frames. The trained model was compared with other state-of-the-art methods on public data. Results exhibit significant agreement between estimated and ground-truth measurements: the root mean square error computed from pulse rate values assessed with the convolutional 3D network is equal to 8.64 bpm, which is superior to 10 bpm for the other state-of-the-art methods. The robustness of the method to natural motion and increases in performance correspond to the two main avenues that will be considered in future works.

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An adaptive deep learning approach for PPG-based identification

Cited by 67 publications

References 13 publications

Current progress of photoplethysmography and SPO2 for health monitoring

Current progress of photoplethysmography and SPO2 for health monitoring

A multimodal deep architecture for traditional Chinese medicine diagnosis

3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video

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