Heart rate analysis by sparse representation for acute pain detection

Tejman‐Yarden, Shai; Levi, Ofer; Beizerov, Alex; Parmet, Yisrael; Nguyen, Tu; Saunders, Michael A.; Rudich, Zvia; Perry, James C.; Baker, Dewleen G.; Moeller-Bertram, Tobias

doi:10.1007/s11517-015-1350-3

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…One is using more dimensions of information in signal processing other than a simple statistical median. For example, the number of fluctuations, features of stationarity, entropy, similarity [ 15 , 35 , 37 ] and features in the frequency domain can be further applied. In the continuous monitoring manner, a time window with a fixed length can be added to streaming data.…”

Section: Resultsmentioning

confidence: 99%

Acute pain intensity monitoring with the classification of multiple physiological parameters

Jiang

Mieronkoski

Syrjälä

et al. 2018

J Clin Monit Comput

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Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.

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Section: Resultsmentioning

confidence: 99%

Acute pain intensity monitoring with the classification of multiple physiological parameters

Jiang

Mieronkoski

Syrjälä

et al. 2018

J Clin Monit Comput

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“…Heart rate can be used as a marker for reactivity to painful events and is commonly used in neonatal pain assessment and care 18,[42][43][44][45][46][47][48] . While heart rate is not a specific indicator of pain, it has emerged as a reliable non-invasive means to identify probable painful events, especially in contexts where self-reporting is challenging [49][50][51] . Heart rate also reflects broader reactivity, and encompasses states like depression, tension, anger, or fear -often linked to the release of stress hormones 18 .…”

Section: Case Presentationmentioning

confidence: 99%

Ethical and Practical Concerns of Heart Rate Monitoring: Enabling Communication for Patients with Severe Communication Difficulties

Kildal,

Hofmann,

Nærland

et al. 2024

Preprint

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BackgroundMonitoring technologies to aid communication in long-term patient care may enhance safety and well-being but may also infringe on privacy. While autonomy is a primary value in medical ethics, definitions of autonomy often take for granted a minimum level of communicative ability. For the ethical debate on autonomy to be extended to people with severe communication difficulties who cannot explicitly approve or disapprove of the use of monitoring technologies, we need to consider the ethical implications of using or not using such technologies for this patient group specifically. Main text This paper explores how contemporary philosophical arguments on monitoring physiological responses can be made applicable to people with severe communication difficulties. It further discusses autonomy concerning the ethical principles of beneficence and nonmaleficence when evaluating the use of heart rate monitoring for this patient group. While monitoring someone without their consent is ethically problematic, providing heart rate monitoring may be morally imperative as a necessary means of interaction, as this patient group is effectively isolated without much possibility to interact with their caregivers. Conclusion Although heart rate monitoring of people with severe communication difficulty can be considered a privacy infringement and a violation of the principle of respect for a person’s autonomy, it can also be autonomy-enabling and of potential beneficence. Balancing the principles and interests of a person with severe communication difficulties is challenging and demands careful and critical ethical reflection.

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“…In order to solve the aforementioned issue regarding subjective pain testing, we propose the development and design of an objective multi-modal pain classifier. This would eliminate the need for subjective scales and potential misdiagnosis from pain measurement by using Electroencephalography (EEG), Pupillary Unrest Under Ambient Light (PUAL), and Skin Conductance (SC), Electromyography (EMG), Respiration Rate (RR), Blood Volume Pulse (BVP), Skin Temperature (ST), Blood Pressure (BP), Facial Expression (FE) (Evans, Hodgkinson, & Berry, 2001;Neice, 2017;Tejman-Yarden et al, 2016). Then, sensor fusion algorithms will be performed to classify the pain levels based on the above.…”

Section: Objectivesmentioning

confidence: 99%

Objective Pain Measurement based on Physiological Signals

Lin

Wang

Xiao

et al. 2018

Proceedings of the International Symposium on Human Factors and

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In an attempt to understand human physiological signals when an individual is subjected to pain, we set up a tonic pain experiment in a laboratory setting. The subjects’ physiological signals were recorded, timestamped, and compared to an initial 30 second baseline measurement. Subjects were also asked to verbally state their level of pain based on a visual analog scale in order to compare reported pain levels with physiological signals. The physiological signals measured were: Electroencephalography (EEG), Pupillary Unrest Under Ambient Light (PUAL), Skin Conductance (SC), Electromyography (EMG), Respiration Rate (RR), Blood Volume Pulse (BVP), Skin Temperature (ST), Blood Pressure (BP), and Facial Expression (FE). ANOVA and frequency domain analyses were conducted on the data in order to determine whether there was a significant difference between the ‘pain’ and ‘no pain’ (baseline) states of an individual. Based on our results, skin conductance, PUAL, facial expression, and EEG signals were theorized to be good signals for the classification of tonic pain, or any pain applied directly to an individual.

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Heart rate analysis by sparse representation for acute pain detection

Cited by 11 publications

References 32 publications

Acute pain intensity monitoring with the classification of multiple physiological parameters

Acute pain intensity monitoring with the classification of multiple physiological parameters

Ethical and Practical Concerns of Heart Rate Monitoring: Enabling Communication for Patients with Severe Communication Difficulties

Objective Pain Measurement based on Physiological Signals

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