JEFFREY HAYZLETT ANNOUNCES JOHN ZALESKI AS AN AUTHOR IN NEW C-SUITE BOOK CLUB

[subscribe2] C-SUITE BOOK CLUB New York, NY, March 12th, 2015: Jeffrey Hayzlett, former Fortune 100 CMO, bestselling author and Bloomberg TV host, announced John R. Zaleski has been inducted into C-Suite Book Club, the premier source for the world’s leading busin... Read More

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Contributing Author–Dictionary of Computer Science, Engineering, and Technology

Zaleski, JR, (contributing Author), Dictionary of Computer Science, Engineering, and Technology, (CRC Press, Phil Laplante, Editor-in-Chief). ... Read More

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Book I–Integrating Device Data into the Electronic Medical Record

The seminal book on the medical device integration into health information technology systems. John Zaleski, Ph.D. A book on empirical practice of medical device interoperability, based on years of experience in the field. A Developer’s Guide to Design and a ... Read More

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Book II–Medical Device Data and Modeling for Clinical Decision Making

This work combines much of the experience learned in medical device interoperability and clinical informatics I have gained over the course of the past 20+ years. I have leveraged work from my  PhD and experience in product management of critical care. The devic... Read More

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John R. Zaleski Dissertation (1996): Weaning from Postoperative Mechanical Ventilation

[subscribe2] Dissertation Title: "Modeling Postoperative Respiratory State in Coronary Artery Bypass Graft Patients: A Method for Weaning Patients from Mechanical Ventilation" "Physicians, nurses, and other health care workers are facing a problem: provide affordab... Read More

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Heart Rate Variability (HRV) Analysis Using the Lomb-Scargle Periodogram—Simulated ECG Analysis — Part 2

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Heart Rate Variability (HRV) Analysis

The purpose of the attached white paper and the analysis is to study the use of signal processing analysis on the subject as a method for advanced analytics. The selection of the LSP was made because the ability of this algorithm to operate on data that contains gaps is quite important when considering physiologic and other real-world data.

The use of the Lomb-Scargle Periodogram (LSP) for the analysis of biological signal rhythms has been well-documented. [1,2]

“The analysis of time-series of biological data often require special statistical procedures to test for the presence or absence of rhythmic components in noisy data, and to determine the period length of rhythms.” [3]

“In the natural sciences, it is common to have incomplete or unevenly sampled time series for a given variable. Determining cycles in such series is not directly possible with methods such as Fast Fourier Transform (FFT) and may require some degree of interpolation to fill in gaps. An alternative is the Lomb-Scargle method (or least-squares spectral analysis, LSSA), which estimates a frequency spectrum based on a least squares fit of sinusoid.” [4]

[1] T. Ruf, “The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series.” Biological Rhythm Research, 1999, Vol. 30, No. 2, pp. 178-201.

[2] Jozef Púčik, “Heart Rate Variability Spectrum: Physiologic Aliasing and Nonstationarity Considerations.” Trends in Biomedical Engineering. Bratislava, September 16-18, 2009.

[3] T. Ruf, “The Lomb-Scargle Periodogram in Biological Rhythm Research: Analysis of Incomplete and Unequally Spaced Time-Series”. Biological Rhythm Research, 1999, Vol. 30, No. 2, pp. 178-201.

[4] Marc in the box, “Lomb-Scargle periodogram for unevenly sampled time series.” Link: http://www.r-bloggers.com/lomb-scargle-periodogram-for-unevenly-sampled-time-series/. Published January 10th, 2013. Accessed 20-April-2015.

This Paper’s Contribution

This paper focuses on the use of the Lomb-Scargle Periodogram to survey Heart Rate Variabililty (HRV). In a preceding analysis, our focus was on the use of signal processing methods, such as the Lomb-Scargle Periodogram, detect power spectral density versus frequency in time-domain signals, such as heart rate variability. The purpose of that analysis was to illustrate the identification of power spectral density associated with time domain signals using a signal processing method known as the Lomb-Scargle Periodogram (LSP). The LSP is deemed a better method for evaluating power spectral density in time-varying signals where there may be missing or data gaps, or irregular measurements. For this reason, it is deemed superior to the discrete Fourier transform for power spectral analysis related to signals processing involving unevenly sample data, which is frequently the case in biology and medicine.

Download the White Paper Here

A copy of the white paper on HRV analysis using Lomb-Scargle Periodogram can be downloaded here. The following figure depicts the power spectrum produced by the Lomb-Scargle Periodogram when applied to a time-varying signal containing several frequencies. The mathematics of the Lomb-Scarlge Periodogram are such that the method can be applied to time signals with gaps or missing data, thereby improving its utility in real-world settings where such gaps may be expected to occur.

Sample Lomb-Scargle Periodogram associated with a signal

Sample Lomb-Scargle Periodogram associated with a signal

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John Zaleski on Medical Device Integration: HIMSS Media Interview on Connected Medical Devices

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HIMSS Media Announces New Book on Medical Device Integration: Connected Medical Devices

John Zaleski is interviewed by HIMSS Media on his new book on the topic of medical device integration. In this book, titled Connected Medical Devices, he describes best practices for medical device integration. This book is intended for the healthcare enterprise that is beginning the process of integrating medical device data into their electronic health record systems. A link to Connected Medical Devices interview is included here:

John R. Zaleski, Ph.D., CPHIMS–HIMSS15 Interview on Connected Medical Devices

Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems

Within a healthcare enterprise, patient vital signs and other automated measurements are communicated from connected medical devices to end-point systems, such as electronic health records, data warehouses and standalone clinical information systems. Connected Medical Devices: Integrating Patient Care Data in Healthcare Systems explores how medical device integration (MDI) supports quality patient care and better clinical outcomes by reducing clinical documentation transcription errors, improving data accuracy and density within clinical records and ensuring the complete capture of medical device information on patients.  The book begins with a comprehensive overview of the types of medical devices in use today and the ways in which those devices interact, before examining factors such as interoperability standards, patient identification, clinical alerts and regulatory and security considerations. Offering lessons learned from his own experiences managing MDI rollouts in both operating room and intensive care unit settings, the author provides practical guidance for healthcare stakeholders charged with leading an MDI rollout. Topics include working with MDI solution providers, assembling an implementation team and transitioning to go-live. Special features in the book include a glossary of acronyms used throughout the book and sample medical device planning and testing tools.

About the Author

John Zaleski, PhD, CPHIMS, brings more than 25 years of experience in researching and ushering to market devices and products to improve healthcare. Dr. Zaleski received his PhD from the University of Pennsylvania, with a dissertation that describes a novel approach for modeling and prediction of post-operative respiratory behavior in post-surgical cardiac patients. He has a particular expertise in designing, developing, and implementing clinical and non-clinical point-of-care applications for hospital enterprises. Dr. Zaleski is the named inventor or co-inventor on seven issued patents related to medical device interoperability. He is the author of numerous peer-reviewed articles on clinical use of medical device data, information technology and medical devices and wrote two seminal books on medical device integration into electronic health records and the use of medical device data for clinical decision making.

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Using Radiofrequency Identification (RFID) to promote improved patient identification in telemonitoring

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What does RFID and Positive Patient Identification have to do with Telemonitoring, anway?

Chronic ailments, such as congestive heart failure (CHF), diabetes, chronic obstructive pulmonary order, stroke, arthritis and others usually afflict us as we get older. More often than not, we experience more than one of these ailments simultaneously. To facilitate the monitoring and management of these ailments, various types of telemonitoring are being employed to collect vital signs information such as blood pressure, glucose and weight. But, in addition, telemonitoring is being used as a remote method to communicate visually and audibly with nurses and other practitioners, particularly for those patients who are home-bound and infirm. When monitoring vital signs remotely, methods need to be brought to bear to ensure that data are collected from the correct patient, and this is where positive patient identification methods such as barcode or radiofrequency identification (RFID) can come into play.

The original paper, which I wrote a number of years ago, appeared in Practical Patient Care. Subsequent to that time, I opted to create this white paper to highlight key aspects of the subject matter, and to put forth a thought on the use in the home.

RFID for use in medication administration and positive patient identification

The attached paper is an abridgment of an article that originally appeared in Practical Patient Care a number of years ago on the use of RFID technologies in the application of medication administration. The application of barcode and RFID technologies in the areas of medication administration reporting and safety checking suggest there is an expanding role in the use of these technologies at the point of care, regardless of where that point of care might be. In particular, the use of RFID in validating and verifying patient, drug, and order identity as part of the “5 Rights” for medication administration checking and infusion pump drug validation via the use of universal product code (UPC) symbol or RFID-embedded patient bracelets is a key step in nursing workflow within acute care wards. Verifying patient identity is often accompanied by clinician requests to orally verify patient identity via independent checks for patient name, dates of birth, and social security numbers. Patients at home could also employ a similar approach to placing their identification on any vital signs information transmitted from there to a remote telemonitoring facility. This would improve the association and linkage between raw numeric vital signs and related observations to a particular identity.

White paper on telemonitoring and positive patient identification

The attached white paper captures my thoughts on the topic and on other technologies, such as telemonitoring.

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