Curriculum Vitae

My primary focus is on building products, so I don't track these as closely as I should. It's possble I have overlooked one or two publications or patents in the following lists.

Publications

  1. Derakhshan, A.; Yavin, H.; Omotoye, S.; Dresing, T. J.; Dawoud, F.; McSpadden, L. C.; Rhude, J. L.; Davis, K. J.; Wilkoff, B. L.; Tanaka-Esposito, C.. Po02-087 Novel Device-Based Discriminators Improve Differentiation of Polymorphic Vt and Vf from Monomorphic Vt in Implantable Cardiac Defibrillators. Heart Rhythm (2023) 20(5):S379. doi: 10.1016/j.hrthm.2023.03.865
  2. Betts, T. R.; Gardner, R. S.; Quartieri, F.; Goil, A.; Davis, K. J.; Qu, F.; Sabet, L.; McSpadden, L. C.; Ryu, P.; Singh, J. P.. Po-678-08 Neural Network Model for Automatic Discrimination of Atrial Fibrillation Episodes Detected by an Insertable Cardiac Monitor. Heart Rhythm (2022) 19(5):S352–S353. doi: 10.1016/j.hrthm.2022.03.483
  3. Gopinathannair, R.; Lakkireddy, D.; Afzal, M. R.; Piorkowski, C.; Qu, F.; Dawoud, F.; Davis, K.; Ryu, K.; Ip, J.. Effectiveness of SharpSense™ algorithms in reducing bradycardia and pause detection: real-world performance in Confirm Rx™ insertable cardiac monitor. Journal of Interventional Cardiac Electrophysiology (2022) 63(3):661–668. doi: 10.1007/s10840-021-01099-4
  4. Gardner, R. S.; Quartieri, F.; Betts, T. R.; Afzal, M. R.; Manyam, H.; Badie, N.; Dawoud, F.; Sabet, L.; Davis, K.; Qu, F.; Ryu, K.; Ip, J.. Reducing the electrogram review burden imposed by insertable cardiac monitors. Journal of Cardiovascular Electrophysiology (2022) 33(4):741–750. doi: 10.1111/jce.15397
  5. Lashgari, E.; Nair, D. G.; Gopinathannair, R.; Exner, D. V.; Qu, F.; Dawoud, F.; Goil, A.; Davis, K.; Ryu, P.; Yoo, D.; Manyam, H.; Singh, J. P.. A Convolutional Neural Network for Automatic Discrimination of Pause Episodes Detected by an Insertable Cardiac Monitor. Cardiovascular Digital Health Journal (2022) 3(4). doi: 10.1016/j.cvdhj.2022.07.007
  6. Wilkoff, B. L.; Sterns, L. D.; Katcher, M. S.; Upadhyay, G.; Seizer, P.; Kang, C.; Rhude, J.; Davis, K. J.; Fischer, A.. Novel ventricular tachyarrhythmia detection enhancement detects undertreated life-threatening arrhythmias. Heart Rhythm O2 (2022) 3(1):70–78. doi: 10.1016/j.hroo.2021.11.009
  7. Safabakhsh, S.; Zhao, R.; Parker, J.; Liew, J.; Du, D.; Chakrabarti, S.; Ong, K.; Ryu, K.; Davis, K.; Laksman, Z.. Machine Learning Driven Improvement of Signal Detection by Implantable Cardiac Monitors. JACC: Advances (2022) 1(3). doi: 10.1016/j.jacadv.2022.100054
  8. Ip, J.; Quartieri, F.; Betts, T.; Afzal, M.; Manyam, H.; Badie, N.; Dawoud, F.; Sabet, L.; Davis, K. J.; Qu, F.; Ryu, K.; Gardner, R. S.. B-Po05-042 Reducing Clinical Review Burden of Insertable Cardiac Monitors in Patients with Frequent Arrhythmia Detections. Heart Rhythm (2021) 18(8):S388. doi: 10.1016/j.hrthm.2021.06.962
  9. Cantillon, D. J.; Dukkipati, S. R.; Ip, J. H.; Exner, D. V.; Niazi, I. K.; Banker, R. S.; Rashtian, M.; Plunkitt, K.; Tomassoni, G. F.; Nabutovsky, Y.; Davis, K. J.; Reddy, V. Y.. Comparative study of acute and mid-term complications with leadless and transvenous cardiac pacemakers. Heart Rhythm (2018) 15(7):1023–1030. doi: 10.1016/j.hrthm.2018.04.022
  10. Desai, A. S.; Bhimaraj, A.; Bharmi, R.; Jermyn, R.; Bhatt, K.; Shavelle, D.; Redfield, M. M.; Hull, R.; Pelzel, J.; Davis, K.; Dalal, N.; Adamson, P. B.; Heywood, J. T.. Ambulatory Hemodynamic Monitoring Reduces Heart Failure Hospitalizations in “Real-World” Clinical Practice. Journal of the American College of Cardiology (2017) 69(19):2357–2365. doi: 10.1016/j.jacc.2017.03.009
  11. Cantillon, D. J.; Exner, D. V.; Badie, N.; Davis, K.; Gu, N. Y.; Nabutovsky, Y.; Doshi, R.. Complications and Health Care Costs Associated with Transvenous Cardiac Pacemakers in a Nationwide Assessment. JACC: Clinical Electrophysiology (2017) 3(11):1296–1305. doi: 10.1016/j.jacep.2017.05.007

Patents (U.S. Only)

Number Description
US12257060B2 (granted) Methods and systems for predicting arrhythmia risk utilizing machine learning models
US12186100B2 (granted) Methods and systems for arrhythmia episode prioritization and improving arrhythmia detection and classification to reduce clinical review burden
US11874334B2 (granted) Method and device for detecting abnormal battery consumption due to extra-battery mechanisms
US20250235145A1 (pending) Methods and systems to confirm device classified arrhythmias utilizing machine learning models
US20240189603A1 (pending) Method and device for discriminating monomorphic tachycardia and oversensing using similarity and characteristics of ecg rhythms
US20240065637A1 (pending) Implantable medical device data and diagnostics management system method using machine-learning architecture
US20230263480A1 (pending) System for verifying a pathologic episode using an accelerometer
US20220354410A1 (pending) Device and method for detecting ventricular arrhythmias based on duty cycle characteristics
US20220167903A1 (pending) Methods and systems to manage presentation of representative cardiac activity segments for clusters of such segments