Garmin Vivoactive HR for Rowing & Sculling

Vivoactive HR

Sculling and Rowing

I am a rower and sculler. I first cut my teeth in the sport over 30 years ago while at college rowing on the Charles River. I had been looking for the longest time for a device that I could use to track my heart, stroke rate, and also support GPS mapping of my workout while on the water. There are professional devices that track stroke rate and the like, such as Speed Coach GPSStroke Coach and Coxmate GPS. These are all excellent pieces of equipment, by the way. But, I am not in varsity rowing any more and I was looking for a piece of equipment that could support my rowing “habit” both for indoor and outdoor rowing (aside: I also possess a Concept 2 ergometer, which I love) while also serving the utilitarian purpose of being a good watch that can track heart rate full time.

When I row, however, I am really interested in being able to map the analytics to the motion. The Vivoactive HR enables me to do this as well as to post-process the data. I am into data. As a Chief Analytics Officer in the healthcare field for a medical device and real-time patient surveillance company, it is important to me to be able to access and understand the information collected during an activity. The connectivity and access to data provided by the Vivoactive HR are phenomenal.

Data view from Garmin Connect web site.

 

 

 

 

The figure above details an example analytics screen, which shows the map of the workout, heart rate, stroke rate, distance traveled at each measurement point, and allows tracking the entire workout with a cross-hair that is dynamic and interactive on the web screen. The unit supports many other types of workouts, including running, biking, pool, golf, walking, indoor rowing on ergometer, SUP rowing, XC skiing, indoor walking, indoor biking, and indoor running, and tracks sleep. The unit can be submerged in water and the battery life is amazing. I normally live with the unit on my wrist, and after 3 days of continuous use, battery is down to, perhaps 80%. I will take it off for an hour or so to charge, and it is good-to-go. I highly recommend this unit for the avid professional or veteran rower (like myself).

Update June 29th, 2017: Comparison among NK, Coxmate, Minimax

Robin Caroe of RowPerfect kindly left me a comment to this post last evening and provided an updated article on comparison among the NK, Coxmate GPS and Catapult Minimax which contains quite valuable data on performance related to these products. I have provided the hyperlink to the article above. Technological differences in sampling rate (e.g.: 5 Hz for NK versus 10 Hz for Coxmate) are important for accuracy. I must say that I was very close to purchasing the Coxmate GPS prior to investigating the Garmin. Upon reading the brochure for the Minimax S4, I am intrigued. The Minimax offers an update rate on the GPS that provides for precision in terms of location. In the Rowperfect article, of the key measures of performance identified, (1) heart rate & heart rate variability; (2) force and length of stroke; and, (3) GPS update rate are important measures for the elite athlete. In the case of the Minimax, GPS update on the order of 100 times per second (10 milliseconds) can reveal boat pitch, roll & yaw. Highly impressive. I would agree, though, that this level of accuracy and precision would be important for the competitive athlete. Yet, in my case (non-competitive, casual athlete), I still love my Garmin. I am able to see and track my position very accurately, monitor my stroke and heart rate, and in terms of heart rate variability, I can write an algorithm in R or Matlab to monitor that measure fairly directly.

As an added resource, Reviews.com has posted a comparison between best rowing machines for training and rowing experience. You can read that review at this link: The Best Rowing Machine: get a total-body workout on dry land.

Arterial Blood Pressure Signal Tracking

Filtering of Arterial Blood Pressure Signal Artifact using the Extended Kalman Filter

Arterial blood pressure signal (from MIMIC II Database) with measurements and tracking signal overlaid.

The figure above depicts several seconds of raw arterial blood pressure (ABP) data obtained from a patient within the MIMIC II physiologic waveform database. [1,2]

This figure shows a raw signal with a tracking signal based on the extended Kalman filter (EKF) overlaid. In this case, the signal error and the process noise are very small (signal noise 0.1 mmHg, process noise 0.5 mmHg). With these settings, the filter tracks the actual signal very closely, and makes it appear as if there is not difference between signal measurement and track.

The full analysis is available at the following link in PDF form:

ABP Tracking via EKF

[1] M. Saeed, M. Villarroel, A.T. Reisner, G. Clifford, L. Lehman, G.B. Moody, T. Heldt, T.H. Kyaw, B.E. Moody, R.G. Mark.Multiparameter intelligent monitoring in intensive care II (MIMIC-II): A public-access ICU database. Critical Care Medicine 39(5):952-960 (2011 May); doi: 10.1097/CCM.0b013e31820a92c6.

[2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).

 

Alarm Fatigue? What a Nuisance!

Alarm Fatigue

“Hospital staff are exposed to an average of 350 alarms per bed per day, based on a sample from an intensive care unit at the Johns Hopkins Hospital in Baltimore.”[1]

From the same survey, almost 9 in 10 hospitals indicated they would increase their use of patient monitoring, particularly of Capnography and pulse oximetry, if false alarms could be reduced. [2]

“Of those hospitals surveyed that monitor some or all patients with pulse oximetry or Capnography, more than 65 percent have experienced positive results in terms of either a reduction in overall adverse events or in reduction of costs.”[3]

Attenuating Alarm Signals

The problem with attenuating alarm data is achieving the balance between communicating the essential, patient-safety specific information that will provide proper notification to clinical staff while minimizing the excess, spurious and non-emergent events that are not indicative of a threat to patient safety. In the absence of contextual information, the option is usually to err on the side of excess because the risk of missing an emergent alarm or notification carries with it the potential for high cost (e.g.: patient harm or death).

Analysis

The purpose of this study is to look at the and some of the Mathematical Techniques for Mitigating Alarm Fatigue: techniques and options available for evaluating real-time data. The objective is to suggest a dialog for further research and investigation into the use of such techniques as appropriate. Clearly, patient safety, regulatory, staff fatigue and other factors must be taken into account in terms of aligning on a best approach or practice (if one can even be identified). These aspects of alarm fatigue are intentionally omitted from the discussion at this point (to be taken up at another time) so that a pure study of the physics of the parameter data and techniques for analyzing can be explored.

References

[1] Ilene MacDonald, “Hospitals rank alarm fatigue as top patient safety concern”, Fierce Healthcare. January 22, 2014.

[2] Wong, Michael; Mabuyi, Anuj; Gonzalez, Beverly; “First National Survey of Patient-Controlled Analgesia Practices.” March-April 2013, A Promise to Amanda Foundation and the Physician-Patient Alliance for Health & Safety.

[3] Ibid.

 

Opioid-induced respiratory depression and monitoring patients diagnosed with obstructive sleep apnea

HIMSS Future Care posting:

Managing patients on the general care floor (GCF) who are either at risk or “diagnosed with obstructive sleep apnea (OSA), and those in particular who meet the requirements of the STOP-BANG criteria for OSA, can be quite challenging. The ECRI Institute, a federally-certified patient safety and research organization, has identified in its 2017 list of Top 10 Health Technology Hazards “Undetected Opioid-Induced Respiratory Depression” as Number 4 [1]. Opioids used for treatment of acute postoperative pain is rather commonplace, and patients at-risk for OSA, if left unattended, can experience anoxic brain injury or death.

Medical Device Data and Modeling for Clinical Decision Making

Medical Device Data, Modeling & Simulation

This cutting-edge volume is the first book that provides practical guidance on the use of medical device data for bioinformatics modeling purposes. Professionals learn how to develop original methods for communicating with medical devices within healthcare enterprises and assisting with bedside clinical decision making. The book guides in the implementation and use of clinical decision support methods within the context of electronic health records in the hospital environment. Supported with over 100 illustrations, this all-in-one resource discusses key concepts in detail and then presents clear implementation examples to give professionals a complete understanding of how to use this knowledge in the field.

“Medical Device Data and Modeling for Clinical Decision Making” Content Overview:

  • Introduction to Physiological Modeling in Medicine: A Survey of Existing Methods, Approaches and Trends.
  • Simulation and Modeling Techniques.
  • Introduction to Automatic Control Systems Theory and Applications.
  • Physical System Modeling and State Representation.
  • Medical Device Data Measurement, Interoperability, Interfacing and Analysis.
  • Systems Modeling Example Applications.
  • Modeling Benefits, Cautions, and Future Work.

 

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

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 affordable, quality health care to patients while at the same time satisfy cost constraints imposed on them by insurance companies and government agencies. Cost-cutting measures in many industries, including health care, have resulted in down-sizing “solutions” which achieve their goal of reducing costs by eliminating personnel. This approach, however, can take a physical and psychological toll on those remaining care-providers involved in the daily activity of saving lives. Technology has made an attempt to come to the rescue of these individuals by enabling easy-access to data on patients within their care.”

“Much of this information, though, is in a a raw and unprocessed form, and is generally large in quantity. For the weary health-care provider, the effort involved in viewing and processing this information in real-time can be a deterrent to its use. Few places bombard the health-care provide with more real-time data than the Surgical Intensive Care Unit, or SICU. Patients arrive in the SICU from surgery, their lives dependent on the talents of the critical care staff and the proximity of life-sustaining technologies for survival. While it is important to maintain all of the patient’s physiological functions during the critical 24 hour period following surgery, two of the most vital are heart function and breathing. Whereas heart function is maintained through the careful administration of drugs to reduce the strain of pumping blood through the body, breathing is accomplished directly through the use of a mechanical ventilator which breathes for the patient until spontaneous respiratory function is regained.”

This Ph.D. dissertation documents the research and development of a real-time predictor of patient recovery and viability for weaning from postoperative mechanical ventilation.

Weaning from postoperative mechanical ventilation isa key process in surgical intensive care. According to the SCCM (Source: Society of Critical Care Medicine, 2006), ICU patients occupy only 10% of the inpatient beds, but account for almost 30% of the acute care hospital costs. A key aspect of care in ICUs relates to weaning from postoperative mechanical ventilation.

Predicting Sepsis with Early Warning Shock Index

Sepsis hospitalizations increased between 2000 & 2008

Between 2000 and 2008, annual hospitalizations for septicimia increased from 326,000 to 727,000, in which 46% of hospitalized septic patients (patients diagnosed with sepsis or septicimia–blood infections) were admitted through the Emergency Department (ED). [Source: Hall MJ, Williams SN, DeFrances CJ, et al. Inpatient Care for Septicemia or Sepsis: A Challenge for Patients and Hospitals. U.S. Department of Health and Human Services – National Center for Health Statistics. 2011; 62:1-7.]

Predicting the future has never been shown to be possible with 100% certainty. It would seem to be a tautology that uncertainty invades every aspect of life. To a large degree, the inability to predict the future is a direct demonstration of the inability to control or, perhaps more correctly, the inability to fully account for all external influencing events that cause deviation from expected behavior. Thus, establishing certainty in causal relationships (that is, cause and effect) can be difficult.

Such is the case in medicine as in other aspects of life. Over the span of the last 150 years a great deal of research into epidemiology has led to great advances in the ability to predict behaviors based on prior causal evidence. The efficacy of drugs is testament to this statement—just look at penicillin to see a simple example of this. Perhaps the most illustrative example of the use of analysis and modeling to predict or assess the correlation between causal events and outcomes is the famous work of Dr. John Snow relative to identifying the root cause of the cholera outbreak in mid-19th century London. This work presented a cause-and-effect relationship surrounding the presence of cholera in a Broad Street well in 1854. Dr. Snow is identified as one of the fathers of epidemiology. In his analysis he identified the occurrence of cholera in Broad Street and the surrounding environs and traced the cause to a contaminated well. Once the pump handle was removed from the well, the epidemic began to subside. One could argue, perhaps, that early warning as to the likelihood of improvement was the removal of the pump handle on the offending well.

In other areas, though, the ability to predict the causal relationships is much less certain. The battle with cancer in its various forms is testament to this in that the complexity of the cause and effect relationship is not so well understood as to claim that any one approach or cause will guarantee an individual will or will not develop cancer in his or her lifetimes. To be clear, there are correlations and associations based upon past history that establish likely relationships assuming large populations (e.g.: smoking is likely to cause lung cancer). Yet, plenty of individual exceptions exist that violate the general population. My father is an example of an individual who smoked first cigarettes and then a pipe for the vast majority of his life and passed away at the age of 90…of old age. On the other hand, my mother, who did not smoke and yet watched her diet her entire life passed away from breast cancer at the age of 54. However, these individual examples, while outliers, do not provide me, as their progeny, with the rationale or an excuse to engage in destructive behavior. In other words, I am not willing to bet on the gene pool superseding the statistics from the larger population.

Other types of early warning measures (Sepsis and Other Syndromes)

In order to reduce uncertainty in predicting causal relationships it is necessary to improve the fidelity of the models with which the relationships are represented. But, the relationships based upon generalities can provide guidance for expected behavior, and this may be enough to do good, particularly when the expected behavior can target specific outcomes that can mean the difference between life and death. Two examples are given below to illustrate this point.

1) Viability for weaning from mechanical ventilation. Yang & Tobin [Yang KL, Tobin MJ: A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. N Engl J Med 1991; 324:1445-1450] put forth a measure based on the ratio of the respiratory rate to the tidal volume in patients undergoing spontaneous breathing trials. The so-called “rapid shallow breathing index”, or RSBI, is defined as:

RSBI = RR/TV.

In their study, Yang & Tobin determined that this ratio was a good discriminator for weaning success and failure. The threshold RSBI = 105 was identified as the point of demarkation relative to successful weaning (RSBI < 105) and unsuccessful weaning (RSBI > 105). This ratio is used operationally today yet with varying levels of success. However, one of its key benefits is its simplicity. In terms of predicting the causal relationships between successful weaning and failed weaning attempts, the RSBI is, perhaps, moderate to good. Yet, its simplicity lends itself to a general rule-of-thumb that clinicians can opt to use (or ignore) operationally that could motivate the seeking of further, more accurate or telling information that will have a higher, more telling influence on clinical predictability (e.g.: end-tidal CO2, for one).

2) Modified Shock Index and mortality in emergency department patients. Ye-cheung Liu et al. showed that the modified shock index (MSI) (Modified shock index and mortality rate of emergency patients; World J Emerg Med, Vol 3, No 2, 2012) is a superior measure to Shock Index (SI) alone for determining hemodynamic stability. Shock Index is a common measure to assess hypovolemic shock. Shock Index is given by:

SI = HR / BP-SYS,

this is the ratio of the heart rate (pulse) to the systolic component of blood pressure.

Modified Shock Index is given by:

MSI = HR / MBP,

this is the ratio of heart rate (pulse) to mean blood pressure (MBP), which is given by:

MBP = [(BP-DIAS x 2) + BP-SYS] / 3.

In the referenced study, MSI > 1.3 or MSI < 0.7 is associated with an increased probability of ICU admission and death.

Many more examples exist in which predicting causal relationships can be shown. The absolute predictability of the event is not possible to assess with 100% certainty. But, the ability to identify the likelihood of an event on the basis of the causal relationship is possible to show. In posts to follow we will take a look at the possible uses for these causal relationships in operational care management of patients.

Studies of Shock Index as an Early Warning Marker for Sepsis Onset is Promising

A cross-institution 2011 study presented in The Western Journal of Emergency Medicine (Berger et al., “The Shock Index and Early Recognition of Sepsis in the Emergency Department – A Pilot Study”) conducted by researchers at UC Davis, NY Hospital and Harvard University proffered using the Shock Index (SI) as an early warning marker for sepsis in the emergency department (ED). The objective of the study was to:

“…compare the ability of SI, individual vital signs, and the systemic inflammatory response syndrome (SIRS) criteria to predict the primary outcome of hyperlactatemia (serum lactate ≥ 4.0 mmol/L) as a surrogate for disease severity, and the secondary outcome of 28-day mortality.”

Per the studies presented above, a cohort of adult patients suspected of infection were screened for sepsis using data that included vital signs, laboratory data, and initial serum lactate levels.
Test characteristics were computed for two specific outcomes:
1) hyperlactatemia (identified as a marker for morbidity); and,
2) 28 day mortality (see here and here).
Covariates considered in the analysis for sepsis diagnosis included:
a. heart rate (HR) > 90 beats/min
b. mean arterial pressure (MAP) < 65 mmHg
c. respiratory rate (RR) > 20 breaths/min
Shock Index = HR / SYS-BP
Results of the analysis showed the following causal relationships:
of 2524 patients with complete medical record data considered in the analysis, 290 patients presented with hyperlactatemia and 361 died within 28 days.

Threshold measures for Shock Index as an Early Warning Score

 Patients with SI > 0.7 (representing 15.8%) were 3 times more likely to present with hyperlactatemia than those with normal SI (4.9%). Negative predictive value of SI > 0.7 was 95%. This was identical to that of SIRS. Furthermore, an SI > 1.0 was “the most specific predictor of both outcomes.”

Medical Device Integration, the Lomb-Scargle Periodogram, and Heart Rate Variability (HRV)

Medical Device Integration, HRV and Lomb-Scargle Periodogram — What’s the Connection?

Predicting the future accurately is a capability essential to the basic functioning of our lives. Many fields identify the benefits of forecasting behavior. These include, but are not limited to, weather, financial, sales, and defense. In these cases, the objective is to estimate with as high a degree of confidence as possible the expected outcome such that the estimated result will match the actual result, once the actual result occurs.

The accuracy of the predictions is, of course, dependent upon many factors. Some of these factors include the accuracy of the models used to predict the future events, the amount and fidelity of information these models require to ensure accuracy, and the length of time into the future over which the prediction is estimated to be valid.

Medical device integration provides access to the raw data collected for heart rate variability assessment (that is, raw ECG signals). The variability of these signals is well known in terms of diagnostic inference and, hence, the data provide the source for data analysis and predictive assessment. The HRV analysis of the raw signals in the case of the LSP focuses on determining the periodicity of the heart rate, how it changes over time, and given other observations, can be used in concert to assess weather there is an impending issue.

Signal processing of time-varying signals can produce information and knowledge that are useful in diagnosis and analysis of underlying ailments. Hence, one benefit of medical device integration is providing these time-varying signals at relatively high frequency. One technique for determining the frequency of events in measurements — periodic signal behavior — is the Lomb-Scargle Periodogram.

The LSP is a technique that is in a class of predictive analytic algorithms for detecting signal periodicity and identifying frequency occurrence of events in raw data

Lomb-Scargle Periodogram derived from Data Sourced through Medical Device Integration for HRV Assessment

The use of the Lomb-Scargle Periodogram (LSP) for the analysis of biological signal rhythms has been well-documented in the literature. I include a White Paper as the start of my analysis into Heart Rate Variability (HRV) and its calculation for the purpose of alert notification on change. Heart Rate Variability (HRV) has been used as an assessment of the autonomic nervous system, based on sympathetic and parasympathetic tone (SNS versus PSNS). High HRV is indicative of parasympathetic tone. Low HRV is indicative of sympathetic tone. Low HRV has been associated with coronary heart disease and those who have had heart attacks.

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

Medical Device Integration, Informatics & 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 heart rate variability data collected via medical device integration to assess the value of this method for advanced informatics & 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.

A white paper describing the development of an algorithm written in Visual Basic within MS Excel is contained here: Lomb-Scargle Periodogram

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 mathematics of the Lomb-Scargle 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.