Real-Time High-Level Acute Pain Detection Using a Smartphone and a Wrist-Worn Electrodermal Activity Sensor

The subjectiveness of pain can lead to inaccurate prescribing of pain medication, which can exacerbate drug addiction and overdose. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real-time. We implemented three time- and frequency-domain electrodermal activity (EDA) indices in our smartphone application that collects EDA signals using a wrist-worn device. We then evaluated our computational algorithms using thermal grill data from ten subjects. The thermal grill delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). Furthermore, we simulated the real-time processing of the smartphone application using a dataset pre-collected from another group of fifteen subjects who underwent pain stimulation using electrical pulses, which elicited a VAS pain score level 7 out of 10. All EDA features showed significant difference between painless and pain segments, termed for the 5-s segments before and after each pain stimulus. Random forest showed the highest accuracy in detecting pain, 81.5%, with 78.9% sensitivity and 84.2% specificity with leave-one-subject-out cross-validation approach. Our results show the potential of a smartphone application to provide near real-time objective pain detection.

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Real-time High-Level Pain Quantification using a Smartphone and a Wrist-worn Electrodermal Activity Sensor (Preprint)

10.2196/preprints.24479 ◽

2020 ◽

Author(s):

Youngsun Kong ◽

Hugo Posada-Quintero ◽

Ki Chon

Keyword(s):

Real Time ◽

Pain Perception ◽

Cross Validation ◽

Cold Water ◽

Tissue Injury ◽

Electrodermal Activity ◽

Communication Disorders ◽

Smartphone Application ◽

Heat Pain ◽

High Level

BACKGROUND The subjectiveness of pain leads to inaccurate pain management, which can exacerbate drug addiction and overdose. The consequence is tremendous cost to society and individuals as the opioid crisis grows. Given that pain is often experienced in patients’ homes, there is an urgent need for ambulatory devices that can quantify pain in real time. OBJECTIVE We developed a smartphone-based system for real-time objective pain measurement and assessment using a wrist-worn electrodermal activity (EDA) device. METHODS Our smartphone application collects EDA signals from a wrist-worn device and evaluates pain based on the computation of three pain-sensitive EDA indices: the time-varying index of EDA (TVSymp); modified TVSymp (MTVSymp), and the derivative of phasic EDA (dPhEDA). For testing of our computational algorithms that were embedded in a smartphone application, ten subjects underwent heat pain using a thermal grill, which delivered a level of pain that was calibrated for each subject to be 8 out of 10 on a visual analog scale (VAS). The thermal grill induces heat pain perception without tissue injury using the temperature difference between warm and cold water. All of the wearable-collected EDA signal processing was performed using a smartphone application. Furthermore, another group of fifteen subjects underwent pain stimulation using electrical pulses (EP), which elicited a VAS pain score level 7 out of 10. For EP data collection, EDA signals were collected using a non-wearable device but the same smartphone application was used to calculate the EDA-derived pain indices. We set 5-second segments before and after each pain stimulus to be painless and pain segments, respectively, and trained eight machine-learning classifiers to test the feasibility of our smartphone and EDA-based system to detect pain in real-time. Parameters of the classifiers were optimized using the grid search cross-validation technique. We trained and tested the classifiers on both datasets with leave-one-subject-out cross-validation approach to prevent over-fitting of the models. RESULTS We obtained up to 82.1% accuracy in detecting pain. We also trained using only one dataset at a time and tested with other datasets (and vice versa) and achieved up to 83.1% accuracy. CONCLUSIONS Our results show the potential of a smartphone application to provide near real-time objective pain detection. This approach can potentially enable pain quantification for both acute and chronic pain and it is especially suited for subjects with communication disorders as well as infants.

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SoC-FPGA systems for the acquisition and processing of electroencephalographic signals

International Journal of Reconfigurable and Embedded Systems (IJRES) ◽

10.11591/ijres.v10.i3.pp237-248 ◽

2021 ◽

Vol 10 (3) ◽

pp. 237

Author(s):

Matias Javier Oliva ◽

Pablo Andrés García ◽

Enrique Mario Spinelli ◽

Alejandro Luis Veiga

Keyword(s):

Embedded System ◽

Real Time ◽

General Purpose ◽

System Response ◽

Single Chip ◽

Real Time Processing ◽

General Purpose Processor ◽

Time Operation ◽

Electroencephalographic Signals ◽

High Level

<span lang="EN-US">Real-time acquisition and processing of electroencephalographic signals have promising applications in the implementation of brain-computer interfaces. These devices allow the user to control a device without performing motor actions, and are usually made up of a biopotential acquisition stage and a personal computer (PC). This structure is very flexible and appropriate for research, but for final users it is necessary to migrate to an embedded system, eliminating the PC from the scheme. The strict real-time processing requirements of such systems justify the choice of a system on a chip field-programmable gate arrays (SoC-FPGA) for its implementation. This article proposes a platform for the acquisition and processing of electroencephalographic signals using this type of device, which combines the parallelism and speed capabilities of an FPGA with the simplicity of a general-purpose processor on a single chip. In this scheme, the FPGA is in charge of the real-time operation, acquiring and processing the signals, while the processor solves the high-level tasks, with the interconnection between processing elements solved by buses integrated into the chip. The proposed scheme was used to implement a brain-computer interface based on steady-state visual evoked potentials, which was used to command a speller. The first tests of the system show that a selection time of 5 seconds per command can be achieved. The time delay between the user’s selection and the system response has been estimated at 343 µs.</span>

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Sleeve Gastrectomy vs. Fundus Resection in Bariatric Surgery (Animal Study)

Shiraz E-Medical Journal ◽

10.5812/semj.95198 ◽

2020 ◽

Vol 21 (6) ◽

Author(s):

Alimohammad Bananzadeh ◽

Seyed Vahid Hosseini ◽

Hajar Khazraei ◽

Mohammad Mehdi Lashkarizadeh ◽

Leila Ghahramani ◽

...

Keyword(s):

Bariatric Surgery ◽

Weight Loss ◽

Sleeve Gastrectomy ◽

Body Weight Loss ◽

Test Results ◽

Significant Difference ◽

Before And After ◽

Total Ghrelin ◽

The Difference ◽

High Level

Background: Bariatric surgery has resulted in body weight loss, which claimed by surgery removal specific parts of the stomach with enzyme or sleeve gastrectomy. Objectives: The aim of this study is to determine weight loss and endocrine changes by 12-week fundus resection and sleeve gastrectomy in rabbits. Methods: Twenty-one rabbits, weighing 2.5 - 3.5 kg, were divided into three groups (n = 7): sleeve gastrectomy, experimental fundus resection, and sham group. The weight of rabbits and total ghrelin and leptin levels in the plasma before and after surgery were measured in 12 weeks. Statistical analyses were performed using the Kruskal-Walis test for comparison of the means between the groups, and the difference after months in one group was assayed by Friedman test. Results: The results showed sleeve gastrectomy had a significant weight loss after one month when compared to fundus resection and sham-operated controls (P = 0.008). There was no significant difference in the ghrelin levels after these surgeries, but leptin levels decreased significantly after the fundectomy (P = 0.025). Conclusions: Sleeve gastrectomy is more efficient than the fundus resection in weight loss. It could be suggested as a new option in metabolic disorders due to the high level of leptin.

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Bifrost: A Python/C++ Framework for High-Throughput Stream Processing in Astronomy

Journal of Astronomical Instrumentation ◽

10.1142/s2251171717500076 ◽

2017 ◽

Vol 06 (04) ◽

pp. 1750007 ◽

Cited By ~ 15

Author(s):

Miles D. Cranmer ◽

Benjamin R. Barsdell ◽

Danny C. Price ◽

Jayce Dowell ◽

Hugh Garsden ◽

...

Keyword(s):

Real Time ◽

High Throughput ◽

Radio Astronomy ◽

Scientific Productivity ◽

Data Transport ◽

Time Data ◽

Process Data ◽

Real Time Processing ◽

Real Time Data Processing ◽

High Level

Radio astronomy observatories with high throughput back end instruments require real-time data processing. While computing hardware continues to advance rapidly, development of real-time processing pipelines remains difficult and time-consuming, which can limit scientific productivity. Motivated by this, we have developed Bifrost: an open-source software framework for rapid pipeline development. (a) Bifrost combines a high-level Python interface with highly efficient reconfigurable data transport and a library of computing blocks for CPU and GPU processing. The framework is generalizable, but initially it emphasizes the needs of high-throughput radio astronomy pipelines, such as the ability to process data buffers as if they were continuous streams, the capacity to partition processing into distinct data sequences (e.g. separate observations), and the ability to extract specific intervals from buffered data. Computing blocks in the library are designed for applications such as interferometry, pulsar dedispersion and timing, and transient search pipelines. We describe the design and implementation of the Bifrost framework and demonstrate its use as the backbone in the correlation and beamforming back end of the Long Wavelength Array (LWA) station in the Sevilleta National Wildlife Refuge, NM.

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Real-Time Data Processing

Big Data in Bioeconomy ◽

10.1007/978-3-030-71069-9_11 ◽

2021 ◽

pp. 147-156

Author(s):

Fabiana Fournier ◽

Inna Skarbovsky

Keyword(s):

Decision Making ◽

Real Time ◽

Processing System ◽

Event Processing ◽

Time Data ◽

System Architectures ◽

Real Time Processing ◽

Operational Decision Making ◽

Real Time Data Processing ◽

High Level

AbstractTo remain competitive, organizations are increasingly taking advantage of the high volumes of data produced in real time for actionable insights and operational decision-making. In this chapter, we present basic concepts in real-time analytics, their importance in today’s organizations, and their applicability to the bioeconomy domains investigated in the DataBio project. We begin by introducing key terminology for event processing, and motivation for the growing use of event processing systems, followed by a market analysis synopsis. Thereafter, we provide a high-level overview of event processing system architectures, with its main characteristics and components, followed by a survey of some of the most prominent commercial and open source tools. We then describe how we applied this technology in two of the DataBio project domains: agriculture and fishery. The devised generic pipeline for IoT data real-time processing and decision-making was successfully applied to three pilots in the project from the agriculture and fishery domains. This event processing pipeline can be generalized to any use case in which data is collected from IoT sensors and analyzed in real-time to provide real-time alerts for operational decision-making.

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Effect of Aerobic Loading on Static Balance in Young Athletes

Journal of Education and Training Studies ◽

10.11114/jets.v5i3.2177 ◽

2017 ◽

Vol 5 (3) ◽

pp. 154

Author(s):

Öznur Akyüz

Keyword(s):

Motor Skills ◽

Point Of View ◽

Static Balance ◽

Young Athletes ◽

Positive Acceleration ◽

Significant Difference ◽

Before And After ◽

University Department ◽

High Level ◽

Determinant Factor

The fact that balances can also be a factor in performance distinction between athletes in athletic skills, and is considered to provide positive acceleration for physical development in which motor skills are exhibited. Human’s skill to ensure balance can be defined as a determinant factor in development of other motor skills. From this point of view, the purpose of the study is to investigate the effects of Aerobic Loading on Proprioception in Young Athletes. The research was composed of 18 young athletes registered in Ağrı Provincial Directorate of Youth and Sports Athletics Club. Tests of the participant athletes of the research were conducted in the Performance Laboratory of Ağrı ibrahim Çeçen University Department of Physical Training and Sports. After the data were collected, comparisons were made using percentage distributions, medians, and t-test. As a result of the research, it was seen that no effect at desired level occurred in static balances of athletes. It was detected that there was no significant difference between the static balance values taken prior to loading and the static balance values taken after loading (p> 0,05). In lactate measurements performed in order to detect fatigue conditions, differences at significant level (p≤0.05) were detected when the data taken before and after the study were compared. When the general characteristics of the participants are considered, the fact that they are elite athletes reminds that they may carry a high level of motor learning process along with them. This skill may be effective in the fact that static balance values remained unchanged following maximal loading.

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Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition

Sensors ◽

10.3390/s20072026 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2026 ◽

Cited By ~ 2

Author(s):

Antonio Affanni

Keyword(s):

Real Time ◽

Data Storage ◽

Wireless Sensors ◽

Electrodermal Activity ◽

Motion Artifacts ◽

Wireless Transmission ◽

Stress Detection ◽

High Level ◽

Electrocardiogram Ecg ◽

Time Plot

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.

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High-End Storage

Integrated Information and Computing Systems for Natural, Spatial, and Social Sciences ◽

10.4018/978-1-4666-2190-9.ch007 ◽

2012 ◽

pp. 132-152

Author(s):

Isabel Schwerdtfeger

Keyword(s):

Real Time ◽

Data Storage ◽

High Performance ◽

Storage System ◽

Real Time Processing ◽

Storage Solutions ◽

Future Demands ◽

And Performance ◽

Extreme Scale ◽

High Level

This chapter discusses the challenges high-end storage solutions will have with future demands. Due to heavy end-user demands for real-time processing of data access, this need must be addressed by high-end storage solutions. But what type of high-end storage solutions address this need and are suitable to ensure high performance write and retrieval of data in real-time from high- end storage infrastructures, including read and write access from digital archives? For this reason, this chapter reviews a few disk and tape solutions as well as combined disk- and tape storage solutions. The review on the different storage solutions does not focus on compliance of data storage management, but on available commercial high-end systems, addressing scalability and performance requirements both for online storage and archives. High level requirements aid in identifying high-end storage system features and support Extreme Scale infrastructures for the amount of data that high-end storage systems will need to manage in future.

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Real-Time Monitoring of Passenger’s Psychological Stress

Future Internet ◽

10.3390/fi11050102 ◽

2019 ◽

Vol 11 (5) ◽

pp. 102

Author(s):

Gaël Vila ◽

Christelle Godin ◽

Oumayma Sakri ◽

Etienne Labyt ◽

Audrey Vidal ◽

...

Keyword(s):

Real Time ◽

Electrodermal Activity ◽

High Stress ◽

Ambulatory Monitoring ◽

Real Life ◽

Smartphone Application ◽

Physiological Signals ◽

Sensor Data ◽

Moderate Activity ◽

Stress Levels

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.

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Machine Learning Aided Photonic Diagnostic System for Minimally Invasive Optically Guided Surgery in the Hepatoduodenal Area

Diagnostics ◽

10.3390/diagnostics10110873 ◽

2020 ◽

Vol 10 (11) ◽

pp. 873

Author(s):

Evgeny Zherebtsov ◽

Marina Zajnulina ◽

Ksenia Kandurova ◽

Elena Potapova ◽

Viktor Dremin ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Diagnostic System ◽

Blood Perfusion ◽

Tissue Type ◽

Fibre Optic ◽

Doppler Flowmetry ◽

The Real ◽

Significant Difference ◽

High Level

Abdominal cancer is a widely prevalent group of tumours with a high level of mortality if diagnosed at a late stage. Although the cancer death rates have in general declined over the past few decades, the mortality from tumours in the hepatoduodenal area has significantly increased in recent years. The broader use of minimal access surgery (MAS) for diagnostics and treatment can significantly improve the survival rate and quality of life of patients after surgery. This work aims to develop and characterise an appropriate technical implementation for tissue endogenous fluorescence (TEF) and assess the efficiency of machine learning methods for the real-time diagnosis of tumours in the hepatoduodenal area. In this paper, we present the results of the machine learning approach applied to the optically guided MAS. We have elaborated tissue fluorescence approach with a fibre-optic probe to record the TEF and blood perfusion parameters during MAS in patients with cancers in the hepatoduodenal area. The measurements from the laser Doppler flowmetry (LDF) channel were used as a sensor of the tissue vitality to reduce variability in TEF data. Also, we evaluated how the blood perfusion oscillations are changed in the tumour tissue. The evaluated amplitudes of the cardiac (0.6–1.6 Hz) and respiratory (0.2–0.6 Hz) oscillations was significantly higher in intact tissues (p < 0.001) compared to the cancerous ones, while the myogenic (0.2–0.06 Hz) oscillation did not demonstrate any statistically significant difference. Our results demonstrate that a fibre-optic TEF probe accompanied with ML algorithms such as k-Nearest Neighbours or AdaBoost is highly promising for the real-time in situ differentiation between cancerous and healthy tissues by detecting the information about the tissue type that is encoded in the fluorescence spectrum. Also, we show that the detection can be supplemented and enhanced by parallel collection and classification of blood perfusion oscillations.

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