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Angarita GA, Pittman B, Nararajan A, Mayerson TF, Parate A, Marlin B, Gueorguieva RR, Potenza MN, Ganesan D, Malison RT. Discriminating cocaine use from other sympathomimetics using wearable electrocardiographic (ECG) sensors. Drug Alcohol Depend 2023; 250:110898. [PMID: 37523916 PMCID: PMC10905422 DOI: 10.1016/j.drugalcdep.2023.110898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 06/05/2023] [Accepted: 07/09/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Our group has established the feasibility of using on-body electrocardiographic (ECG) sensors to detect cocaine use in the human laboratory. The purpose of the current study was to test whether ECG sensors and features are capable of discriminating cocaine use from other non-cocaine sympathomimetics. METHODS Eleven subjects with cocaine use disorder wore the Zephyr BioHarness™ 3 chest band under six experimental (drug and non-drug) conditions, including 1) laboratory, intravenous cocaine self-administration, 2) after a single oral dose of methylphenidate, 3) during aerobic exercise, 4) during tobacco use (N=7 who smoked tobacco), and 5) during routine activities of daily inpatient living (unit activity). Three ECG-derived feature sets served as primary outcome measures, including 1) the RR interval (i.e., heart rate), 2) a group of ECG interval proxies (i.e., PR, QS, QT and QTc intervals), and 3) the full ECG waveform. Discriminatory power between cocaine and non-cocaine conditions for each of the three outcomes measures was expressed as the area under the receiver operating characteristics (AUROC) curve. RESULTS All three outcomes successfully discriminated cocaine use from unit activity, exercise, tobacco, and methylphenidate conditions with a mean AUROC values ranging from 0.66 to 0.99 and with least squares means values all statistically different/higher than 0.5 among all subjects [F(3, 99) = 3.38, p =0.02] and among those with tobacco use [F(4, 84) = 5.39, p = 0.0007]. CONCLUSIONS These preliminary results support discriminatory power of wearable ECG sensors for detecting cocaine use.
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Affiliation(s)
- Gustavo A Angarita
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA.
| | - Brian Pittman
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Annamalai Nararajan
- Philips Research North America, Cambridge, MA02141, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Talia F Mayerson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Abhinav Parate
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Lumme Health Inc, Boston, MA02210, USA
| | - Benjamin Marlin
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Ralitza R Gueorguieva
- Department of Biostatistics, Yale University School of Public Health, New Haven, CT06510, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Connecticut Mental Health Center, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Child Study Center, Yale University School of Medicine, New Haven, CT06510, USA; Department of Neuroscience, Yale University, New Haven, CT06510, USA; Connecticut Council on Problem Gambling, Wethersfield, CT06109, USA; Wu Tsai Institute, New Haven, CT06510, USA
| | - Deepak Ganesan
- Manning College of Information and Computer Science, University of Massachusetts, Amherst, MA01003, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA
| | - Robert T Malison
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT06519, USA; Clinical Neuroscience Research Unit, Connecticut Mental Health Center, 34 Park Street, New Haven, CT06519, USA; Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT06510, USA; Department of Neuroscience, Yale University, New Haven, CT06510, USA
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Adams RJ, Saleheen N, Thomaz E, Parate A, Kumar S, Marlin BM. Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams. JMLR Workshop Conf Proc 2016; 48:334-343. [PMID: 28090606 PMCID: PMC5235325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The field of mobile health (mHealth) has the potential to yield new insights into health and behavior through the analysis of continuously recorded data from wearable health and activity sensors. In this paper, we present a hierarchical span-based conditional random field model for the key problem of jointly detecting discrete events in such sensor data streams and segmenting these events into high-level activity sessions. Our model includes higher-order cardinality factors and inter-event duration factors to capture domain-specific structure in the label space. We show that our model supports exact MAP inference in quadratic time via dynamic programming, which we leverage to perform learning in the structured support vector machine framework. We apply the model to the problems of smoking and eating detection using four real data sets. Our results show statistically significant improvements in segmentation performance relative to a hierarchical pairwise CRF.
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Affiliation(s)
- Roy J Adams
- University of Massachusetts Amherst, 140 Governors Drv, Amherst, MA 01003,
| | - Nazir Saleheen
- University of Memphis, 375 Dunn Hall, Memphis, TN 38152,
| | - Edison Thomaz
- The University of Texas at Austin, 110 Inner Campus Drive, Austin, TX 78705,
| | | | - Santosh Kumar
- University of Memphis, 375 Dunn Hall, Memphis, TN 38152,
| | - Benjamin M Marlin
- University of Massachusetts Amherst, 140 Governors Drv, Amherst, MA 01003,
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Sabir H, Kumbhare S, Parate A, Gupta S, Tale R. A 65-year old female with synchronous HIV and Extramedullary Plasmacytoma of Maxillary sinus. Gulf J Oncolog 2015; 1:18-23. [PMID: 26499825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2015] [Indexed: 06/05/2023]
Abstract
Extramedullary plasmacytoma in the head and neck region is a rare malignant neoplasm comprising approximately 3% of all the plasma cell neoplasms and less than 1% of head and neck tumors. This extraskeletal lesion is a unifocal, monoclonal, neoplastic proliferation of plasma cells. Some investigators believe that this lesion represents the least aggressive part of the spectrum of plasma cell neoplasms which extends to multiple myeloma. Therefore, plasmacytoma is believed to have clinical importance. We report a case of extramedullary plasmacytoma in the right maxillary sinus of a 65-year-old HIV positive female as a clinical rarity with review of the relevant literature.
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Affiliation(s)
- H Sabir
- Raj Multispeciality Dental Clinic, Indore (M.P), India
| | - S Kumbhare
- Government Dental College and Hospital, Nagpur (M.S), India
| | - A Parate
- Government Dental College and Hospital, Nagpur (M.S), India
| | - S Gupta
- College of Dentistry, Indore (M.P), India
| | - R Tale
- Dr Hedgewar Smruti Rugna Seva Mandal's Dental College and Hospital, Hingoli (M.S), India
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Parate A, Chiu MC, Chadowitz C, Ganesan D, Kalogerakis E. RisQ: Recognizing Smoking Gestures with Inertial Sensors on a Wristband. MobiSys 2014; 2014:149-161. [PMID: 26688835 DOI: 10.1145/2594368.2594379] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Smoking-induced diseases are known to be the leading cause of death in the United States. In this work, we design RisQ, a mobile solution that leverages a wristband containing a 9-axis inertial measurement unit to capture changes in the orientation of a person's arm, and a machine learning pipeline that processes this data to accurately detect smoking gestures and sessions in real-time. Our key innovations are fourfold: a) an arm trajectory-based method that extracts candidate hand-to-mouth gestures, b) a set of trajectory-based features to distinguish smoking gestures from confounding gestures including eating and drinking, c) a probabilistic model that analyzes sequences of hand-to-mouth gestures and infers which gestures are part of individual smoking sessions, and d) a method that leverages multiple IMUs placed on a person's body together with 3D animation of a person's arm to reduce burden of self-reports for labeled data collection. Our experiments show that our gesture recognition algorithm can detect smoking gestures with high accuracy (95.7%), precision (91%) and recall (81%). We also report a user study that demonstrates that we can accurately detect the number of smoking sessions with very few false positives over the period of a day, and that we can reliably extract the beginning and end of smoking session periods.
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