Painometry: Wearable and objective quantification system for acute postoperative pain

Hoang Truong, Nam Bui, Zohreh Raghebi, Marta Ceko, Nhat Pham, Phuc Nguyen, Anh Nguyen, Taeho Kim, Katrina Siegfried, Evan Stene, Taylor Tvrdy, Logan Weinman, Thomas Payne, Devin Burke, Thang Dinh, Sidney D'Mello, Farnoush Banaei-Kashani, Tor Wager, Pavel Goldstein, Tam Vu

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء

ملخص

Over 50 million people undergo surgeries each year in the United States, with over 70% of them filling opioid prescriptions within one week of the surgery. Due to the highly addictive nature of these opiates, a post-surgical window is a crucial time for pain management to ensure accurate prescription of opioids. Drug prescription nowadays relies primarily on self-reported pain levels to determine the frequency and dosage of pain drug. Patient pain self-reports are, however, influenced by subjective pain tolerance, memories of past painful episodes, current context, and the patient's integrity in reporting their pain level. Therefore, objective measures of pain are needed to better inform pain management. This paper explores a wearable system, named Painometry, which objectively quantifies users' pain perception based-on multiple physiological signals and facial expressions of pain. We propose a sensing technique, called sweep impedance profiling (SIP), to capture the movement of the facial muscle corrugator supercilii, one of the important physiological expressions of pain. We deploy SIP together with other biosignals, including electroencephalography (EEG), photoplethysmogram (PPG), and galvanic skin response (GSR) for pain quantification. From the anatomical and physiological correlations of pain with these signals, we designed Painometry, a multimodality sensing system, which can accurately quantify different levels of pain safely. We prototyped Painometry by building a custom hardware, firmware, and associated software. Our evaluations use the prototype on 23 subjects, which corresponds to 8832 data points from 276 minutes of an IRB-approved experimental pain-inducing protocol. Using leave-one-out cross-validation to estimate performance on unseen data shows 89.5% and 76.7% accuracy of quantification under 3 and 4 pain states, respectively.

اللغة الأصليةإنجليزيّة أمريكيّة
عنوان منشور المضيفMobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services
الصفحات419-433
عدد الصفحات15
رقم المعيار الدولي للكتب (الإلكتروني)9781450379540
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 15 يونيو 2020
الحدث18th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2020 - Toronto, كندا
المدة: ١٥ يونيو ٢٠٢٠١٩ يونيو ٢٠٢٠

سلسلة المنشورات

الاسمMobiSys 2020 - Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services

!!Conference

!!Conference18th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2020
الدولة/الإقليمكندا
المدينةToronto
المدة١٥/٠٦/٢٠١٩/٠٦/٢٠

All Science Journal Classification (ASJC) codes

  • !!Computer Networks and Communications
  • !!Computer Science Applications

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