Pain Assessment
Pain is a major single reason for people seeking medical care and it is associated with many illnesses. Pain assessment is critical for optimizing pain management interventions. Uncontrolled pain places patients at risk for numerous adverse psychological and physiological consequences, some of which may be life-threatening.
Systematic assessment of pain is particularly difficult at times when the ability of the patient to communicate is limited, e.g. during critical illness, under sedation or anesthesia. Reliable pain assessment in poorly communicating patients would ease treatment, increase patient satisfaction and increase patient safety.
Sponsor:
People:
Publications
2019
Kasaeyan Naeini, Emad; Azimi, Iman; Rahmani, Amir M; Liljeberg, Pasi; Dutt, Nikil
A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things Journal Article
In: Procedia Computer Science, vol. 151, pp. 551 - 558, 2019, ISSN: 1877-0509, (The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019) / The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019) / Affiliated Workshops).
@article{NAEINI2019551,
title = {A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things},
author = {Emad Kasaeyan Naeini and Iman Azimi and Amir M Rahmani and Pasi Liljeberg and Nikil Dutt},
url = {http://www.sciencedirect.com/science/article/pii/S1877050919305368},
doi = {https://doi.org/10.1016/j.procs.2019.04.074},
issn = {1877-0509},
year = {2019},
date = {2019-05-01},
journal = {Procedia Computer Science},
volume = {151},
pages = {551 - 558},
abstract = {Photoplethysmography (PPG) as a non-invasive and low-cost technique plays a significant role in wearable Internet-of-Things based health monitoring systems, enabling continuous health and well-being data collection. As PPG monitoring is relatively simple, non-invasive, and convenient, it is widely used in a variety of wearable devices (e.g., smart bands, smart rings, smartphones) to acquire different vital signs such as heart rate and pulse rate variability. However, the accuracy of such vital signs highly depends on the quality of the signal and the presence of artifacts generated by other resources such as motion. This unreliable performance is unacceptable in health monitoring systems. To tackle this issue, different studies have proposed motion artifacts reduction and signal quality assessment methods. However, they merely focus on improvements in the results and signal quality. Therefore, they are unable to alleviate erroneous decision making due to invalid vital signs extracted from the unreliable PPG signals. In this paper, we propose a novel PPG quality assessment approach for IoT-based health monitoring systems, by which the reliability of the vital signs extracted from PPG quality is determined. Therefore, unreliable data can be discarded to prevent inaccurate decision making and false alarms. Exploiting a Convolutional Neural Networks (CNN) approach, a hypothesis function is created by comparing heart rate in the PPG with corresponding heart rate values extracted from ECG signal. We implement a proof-of-concept IoT-based system to evaluate the accuracy of the proposed approach.},
note = {The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019) / The 2nd International Conference on Emerging Data and Industry 4.0 (EDI40 2019) / Affiliated Workshops},
keywords = {},
pubstate = {published},
tppubtype = {article}
}