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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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 |