MONITORING DEVICE INCLUDING VITAL SIGNALS TO IDENTIFY AN INFECTION AND/OR CANDIDATES FOR AUTONOMIC NEUROMODULATION THERAPY

Abstract

A monitoring system and method for detecting an infection or for assessing a suitability of neuromodulation therapy for a patient. R-R intervals of a patient are detected and stored for a first time period. A heart rate variability (HRV) of the stored R-R intervals is determined using at least one of a time domain analysis, an entropy analysis, a frequency domain analysis, a wavelet analysis, or a detrended fluctuation analysis. The patient is identified as exhibiting symptoms of a systemic infection and/or identified as suitable for neuromodulation therapy if the HRV is higher than a first threshold.

Claims

1. A method for detecting an infection or for assessing a suitability of neuromodulation therapy for a patient, the method comprising: detecting and storing R-R intervals of a patient for a first time period; determining a heart rate variability (HRV) of the stored R-R intervals using at least one of: a time domain analysis, an entropy analysis, a frequency domain analysis, a wavelet analysis, or a detrended fluctuation analysis, wherein the patient is identified as exhibiting symptoms of a systemic infection and/or identified as suitable for neuromodulation therapy, if the HRV is higher than a first threshold.

2. The method according to claim 1, wherein the time domain analysis comprises: calculating a mean R-R interval via application of a sliding time window; calculating, based on the mean R-R interval: a standard-deviation of the R-R intervals, the square root of a mean of a sum of the squares of differences between successive R-R intervals, and a proportion of a number of R-R interval differences of the successive R-R intervals which are greater than a specific threshold; and determining whether the patient exhibits symptoms of the systemic infection and/or identify the patient as suitable for neuromodulation therapy if the proportion exceeds the first threshold.

3. The method according to claim 1, wherein the frequency domain analysis comprises: determining a signal of the HRV of the R-R intervals in the frequency domain; determining a power P1 of the signal of the HRV in the frequency domain in a frequency range from 0.04 to 0.15 Hz; determining a power P2 of the signal of the HRV in the frequency domain in a frequency range from 0.15 to 0.4 Hz; and computing a ratio P1/P2, wherein P1/P2>1 is associated with an emphasis of activity of the sympathetic nervous system, P1/P2<1 is associated with an emphasis of activity of the parasympathetic nervous system, and P1/P2=1 is associated with a balance between activity of the sympathetic and parasympathetic nervous system, wherein the patient is identified as exhibiting symptoms of a systemic infection and/or as the patient as suitable of neuromodulation therapy if P1/P2 is 2 to 9.

4. The method according to claim 1, wherein the wavelet analysis comprises: determining a signal of the HRV of the R-R intervals in the frequency domain; determining the locations of frequency components obtained from the signal in the frequency domain in the time domain, based on the wavelet analysis, determining a ratio P1/P2 reflecting a balance between sympathetic and vagal modulations, wherein the patient is identified as exhibiting symptoms of a systemic infection and/or the patient is identified as suitable for neuromodulation therapy if P1/P2 is lower than a predetermined threshold, wherein the threshold is between 2 and 9.

5. The method according to claim 1, furthermore comprising detecting and storing at least one of the following parameters of the patient for the first time period: a respiratory rate, an accelerometer signal, a systolic blood pressure, a diastolic blood pressure, a mean arterial pressure an oxygen saturation (SpO2), a fluid level, an analyte measurement such as blood urea nitrogen, creatinine, white blood cell count, hematocrit, hemoglobin, potassium, bicarbonate, arterial pH. Of partial pressure of oxygen and/or carbon dioxide in arterial blood (PaO2 and/or PaCO2),and the method further comprising analyzing the at least one of the parameters.

6. The method according to claim 5, wherein the analyzing of the respiratory rate and the accelerometer signal comprises the steps of: deriving respiratory intervals from the respiratory rate, analyzing the accelerometer signal and determining if motion is present in the first time period, wherein if motion is present, the R-R intervals and the respiratory intervals are deleted and detection is restarted; calculating, if motion is not present in the first time period, an average R-R interval for the first time period by averaging all R-R intervals from the first time period; storing the average R-R interval and respiratory intervals of the first time period; and restarting detection of accelerometer signal, the R-R intervals and respiratory intervals for a subsequent time period.

7. The method of claim 6, further comprising the steps of: transmitting, if a predetermined number of time periods have elapsed, for further processing stored average R-R intervals and respiratory intervals from the predetermined number of time periods to an electronic device for further analysis; and averaging the stored average R-R intervals over the predetermined number of time periods to generate an extended average.

8. The method of claim 6, further comprising the steps of: processing the R-R intervals and the respiratory intervals such that a heart rate variability is calculated, wherein calculation of the heart rate variability comprises: detecting, for each respiratory interval, inspiration peaks and expiration peaks; searching for a peak heart rate following each inspiration peak and storing the peak heart rate; searching for a minimum heart rate following the expiration peak and storing the minimum heart rate; and calculating at least two differences between the peak heart rate and the minimum heart rate over at least two breathing cycles in the inspiration interval, including the inspiration peak and the expiration peak, wherein the at least two differences are averaged as the heart rate variability for the respiratory interval; storing the heart rate variability calculated for each respiratory interval; and averaging the calculated heart rate variability of all stored respiratory intervals to generate an extended heart rate variability.

9. The method of claim 6, further comprising the steps of: analyzing the accelerometer signal to determine if the patient is in a supine position; and identifying, if the patient is in a supine position, the detected R-R intervals and respiratory intervals as nighttime R-R intervals and nighttime respiratory intervals.

10. A monitoring system for a patient, comprising: a wearable device including electrodes, a first processor and a computer-readable memory; and a mobile electronic device including a transceiver and a second processor, wherein the wearable device is configured to detect and store R-R intervals of the patient for a first time period and to transmit the stored R-R intervals to the mobile electronic device, wherein the mobile electronic device is configured to analyze the R-R intervals to determine a heart rate variability (HRV) of the stored R-R intervals using at least one of: a time domain analysis, an entropy analysis, a frequency domain analysis, a wavelet analysis, or a detrended fluctuation analysis, wherein the mobile electronic device is configured to identify the patient as exhibiting symptoms of a systemic infection and/or to identify the patient as suitable for neuromodulation therapy, if the HRV is higher than a first threshold.

11. The monitoring system according to claim 10, wherein the wearable device comprises at least one sensor for measuring at least one of the following parameters of the patient: a respiratory rate, an accelerometer signal, a systolic blood pressure, a diastolic blood pressure, a mean arterial pressure, an oxygen saturation (SpO2), a fluid level, an analyte measurement such as blood urea nitrogen, creatinine, white blood cell count, hematocrit, hemoglobin, potassium, bicarbonate, arterial pH., or partial pressure of oxygen and/or carbon dioxide in arterial blood (PaO2 and/or PaCO2), wherein the wearable device is configured to transmit the data of the at least one parameter to the mobile electronic device, and wherein the mobile electronic device is configured to analyze the parameter of the patient for detecting an infection or for assessing a suitability of neuromodulation therapy for the patient.

12. The monitoring system of claim 10, wherein the respiration rate is detected on the basis of respiratory intervals, wherein the respiratory intervals are detected from an impedance signal, and wherein the impedance signal is analyzed to identify points where a derivative is equal to zero to mark an inspiration peak or an expiration peak.

13. The monitoring system of claim 10, wherein the analysis of the respiratory rate and the accelerometer signal comprises the steps of: deriving respiratory intervals from the respiratory rate, analyzing the accelerometer signal and determining if motion is present in the first time period, wherein if motion is present, the R-R intervals and the respiratory intervals are deleted and detection is restarted; calculating, if motion is not present in the first time period, an average R-R interval for the first time period by averaging all R-R intervals from the first time period; storing the average R-R interval and respiratory intervals of the first time period; and restarting detection of accelerometer data, the R-R intervals and respiratory intervals for a subsequent time period.

14. The monitoring system of claim 13, wherein the wearable device is configured to transmit, if a predetermined number of time periods have elapsed, stored average R-R intervals and respiratory intervals from the predetermined number of time periods to the mobile electronic device for further analysis, and wherein the mobile electronic device is configured to average the stored average R-R intervals over the predetermined number of time periods to generate an extended average.

15. The monitoring system of claim 12, wherein the mobile electronic device is furthermore configured to: process the R-R intervals and the respiratory intervals such that a heart rate variability is calculated, wherein calculation of the heart rate variability comprises: detecting, for each respiratory interval, inspiration peaks and expiration peaks; searching for a peak heart rate following each inspiration peak and storing the peak heart rate; searching for a minimum heart rate following the expiration peak and storing the minimum heart rate; and calculating at least two differences between the peak heart rate and the minimum heart rate over at least two breathing cycles in the inspiration interval, including the inspiration peak and the expiration peak, wherein the at least two differences are averaged as the heart rate variability for the respiratory interval; store the heart rate variability calculated for each respiratory interval; and average the calculated heart rate variability of all stored respiratory intervals to generate an extended heart rate variability.

16. The monitoring system of claim 11, wherein the mobile electronic device is further configured to: analyze the accelerometer signal to determine if the patient is in a supine position; and identify, if the patient is in a supine position, the detected R-R intervals and respiratory intervals as nighttime R-R intervals and nighttime respiratory intervals.

17. The system according to claim 10, wherein the mobile electronic device displays an identification result on a display screen.

18. The monitoring system of claim 10, wherein the wearable device is embedded in an adhesive patch for application to the patient.

19. The monitoring system of claim 10, wherein the wearable device is connected to a desktop computer or a hospital server.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0056] The present invention will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only, and thus, are not limitative of the present invention, and wherein:

[0057] FIG. 1 is an overview of the system communication network;

[0058] FIG. 2 is an diagram of the initial processing of the system;

[0059] FIG. 3 is an illustration of the wearable device;

[0060] FIG. 4 is a diagram of the long-term data collection and processing for the heart rate at rest;

[0061] FIG. 5 is an annotated heart rate and respiratory record according to an embodiment of the system;

[0062] FIG. 6 is a graph of the normal response to atropine dosage levels;

[0063] FIG. 7 is an annotated graph of an atropine test; and

[0064] FIG. 8 is an algorithm for determining candidates of neuromodulation therapy.

[0065] FIG. 9 shows examples for power spectral analysis of HRV for different frequency ranges.

[0066] FIG: 10a shows an example plot of SDNN values from HRV measurements including body temperature information of a patient for evaluation of an infection.

[0067] FIG: 10b shows the SDNN values with body temperature information from

[0068] FIG. 10a mapped along two axes infection no and infection yes.

DETAILED DESCRIPTION OF THE DRAWINGS

[0069] The external monitoring system that provides an assessment of intrinsic autonomic imbalance is shown in FIG. 1. The monitoring system includes an external wearable device 10 and either a physician's mobile device 11 or, for example, a mode of transmission 12 to an internet-based service center 13. The wearable device 10 stores information until it is either interrogated by the clinician's mobile device 11, and/or until it can be transmitted to the internet service center 13. In the case of transmission to an internet service center 13, the wearable device 10 may contain cellular or wireless internet capability that allows it to transmit directly to the internet service center 13. In either case, the physician 14 is then able to view the compiled and processed results.

[0070] The wearable device 10 may also communicate via radio frequency with a mobile device 11 used by the patient. The patient's mobile device 11 then has either a cellular or wireless or wired internet connection for sending the information to the internet service center 13. The patient may wish to view the daily changes or view the treatment response even if they are unable to interpret the signals.

[0071] The overview of the process performed by the wearable device 10 is shown in FIG. 2. The wearable device 10 first collects the physiological signal S200, then filters, processes and extracts the signal data in real-time S201, then stores the filtered/processed data S202. This locally stored data is then transmitted to the mobile device 11 or service center 13 in step S203, and/or the stored data is processed and filtered further in S204 by utilizing a longer time series, for example. This additionally processed data is then also stored S205 and transmitted to the mobile device 11 or service center 13 as in step S203.

[0072] According to an exemplary embodiment, the wearable device 10 includes at least two electrodes 31 enclosed in a water-resistant, self-adhesive patch 33 designed to be worn by the patient for several days to weeks. The electrodes 31 sense relevant electrical physiological signals such as chest electrocardiogram (ECG) and impedance signals that can indicate respiration. Additionally, the wearable device 10 may include an accelerometer 34 for detecting patient activity levels and/or postural information. It may include a trigger button or buttons 35 through which the patient or physician can indicate the start of an event. The physiological signals from the electrodes 31 and the accelerometer 34 are received by a processor in integrated circuit 30. The integrated circuit 30 does preliminary processing as shown in FIG. 2 and also stores processed and unprocessed data between processing and transmission periods.

[0073] Finally, the wearable device 10 includes some components for communication, for example, wireless internet communication directly to an internet service center, cellular communication to an internet service center, radiofrequency communication to a patient device (such as a monitor in the house) or clinician device (such as an in-office programmer), and/or near-field induction communication to a patient device or clinician device. The communication is performed over the embedded antenna 32 of the wearable device 10 and controlled by a transceiver in the integrated circuit 30, where the integrated circuit is, for example, a flexible printed circuit board.

[0074] In order to evaluate intrinsic autonomic tone, the external monitoring system calculates and stores trends for one or more of the following parameters: average heart rate, resting heart rate, short-term heart rate variability, heart rate variability in relation to respiration, heart rate variability at rest, premature ventricular contraction (PVC) count, and the heart rate response to specific challenges.

[0075] Each of these parameters may be calculated from one or more physiologic signals that are collected by the wearable device 10. In one embodiment of the system, the processing and calculation of the parameters occurs within the hardware and software of the wearable component, and the calculated values are then stored for access via a clinician's mobile device or for transmission to an internet service center. In an alternative embodiment, the wearable component stores only raw values of physiologic signals, such as snapshots of the ECG or impedance trends, which are measured between electrodes via delivery of low-level current pulses delivered in a series of pulse per second. In this embodiment, the raw signals are acquired via the clinician's device or via the internet service center, after which the parameters of interest are derived. In a third intermediate embodiment, some of the processing may be performed within the wearable component, with additional processing performed by the clinician's device or internet service center.

[0076] Heart rate is known to be a function of both parasympathetic and sympathetic influences, and thus is a potential physiological parameter used by the external monitoring system for evaluating likelihood of response to autonomic neuromodulation. In one embodiment, this system uses the ECG signal to derive heart rate by detecting the occurrence of ventricular R-waves and calculating the interval between them (R-R intervals), where R is a point corresponding to the peak of the QRS complex of the ECG wave. The system stores heart rate values in order to calculate the average heart rate over a preset time period, for example, a 24 hour period. Furthermore, heart rate during times of rest can be a useful indication of intrinsic parasympathetic tone because sympathetic tone is withdrawn in the absence of exercise.

[0077] Therefore, alternatively or in addition to overall average heart rate, the system can use heart rate data along with data from the accelerometer to calculate a heart rate at rest or a nighttime heart rate S400. In one embodiment for calculating heart rate at rest, the system first evaluates if motion is present on the accelerometer S401, and if no motion is present, it then stores the heart rate values to use in calculating an average. In the case of nighttime heart rate, the intention is to calculate a heart rate average that is only representative of when the patient is sleeping.

[0078] According to an exemplary embodiment for calculating night time heart rate, the system first evaluates if the patient is in a supine position S401 according to three-dimensional orientation data from the accelerometer 34. If the patient is supine, the system evaluates if the patient is also motionless S401 according to the accelerometer. If both conditions are met, the system then calculates S403 and saves the average of the past interval of recorded heart rate values S405 for use in calculating the nighttime heart rate average. If one or both of the conditions fail then the heart rate values for the interval are discarded S404.

[0079] In an embodiment, the system stores R-R intervals and respiration intervals continuously as long as the requirements are met, and then after a preset time period (e.g. 24 hours) S406, the system calculates the average of all saved values S407. Alternatively, the system may store averages over smaller time intervals (e.g. 5 minutes) S405 during which the criteria are met, then after a preset period of time S406, average together all of the smaller interval averages into a final average. This final average for the entire day or for the nighttime is then stored or transmitted S409 and the memory storing the smaller interval averages or all the interval data is cleared.

[0080] The system also automatically restarts recording the accelerometer, heart rate and impedance from the electrodes 31 and accelerometers 34 after the end of each smaller time interval. Furthermore, if the preset period has not been reached, the system continues recording physiological signals into local memory. Alternatively, the system could generate a running average that is reset and output every 5 minutes or after 24 hours.

[0081] Heart rate variability (HRV), particularly the high frequency component associated with respiration, is known to be vagally mediated. Therefore, HRV is another potential physiological parameter that should be recorded. According to one embodiment, the HRV calculation used by the system is the SDNN index, in which the mean of the 5-minute standard deviations of the R-wave intervals is calculated over 24 hours. The system may also incorporate an ability to discriminate between normal R-waves (originating from atrial conduction) and PVCs, in order to include only normal R-waves into the calculation of HRV.

[0082] Likewise, HRV at rest may be a parameter of interest. Like the heart rate at rest described above, the HRV at rest is acquired by the system first evaluating if motion is present on the accelerometer, and if no motion is present, it then stores the HRV values for use in averaging a HRV at rest value. Alternatively or in addition to HRV based on R-R intervals alone, the system may also monitor breathing rate respiration according to thoracic impedance fluctuations in order to assess the variations in heart rate that are specifically associated with respiration.

[0083] An illustration of HRV assessment with respiration is shown in FIG. 5 in graph format. Thoracic impedance measurements are acquired at a high sampling rate (multiple times per second) and saved in a buffer. The impedance signal (z) is analyzed to identify points where the derivative is equal to zero (dz/dt=0) in order to identify times at which the peaks of inspirations and expirations occurred. The heart rate on a beat-to-beat basis is also saved in a memory buffer during the same period.

[0084] For each peak of inspiration that is found, the algorithm searches for a peak heart rate within a time window (tw) and saves that heart rate value as i.sub.n (e.g. i.sub.2, i.sub.3). For each expiration that is found, the algorithm searches for a local minimum in the heart rate within time window tw following the expiration peak, and saves that heart rate value as e.sub.n. For each pair of respiration cycle heart rates, i.sub.n and e.sub.n, the algorithm calculates the difference d.sub.n between the values. Then, a series of differences (d.sub.1, d.sub.n) are averaged to find the mean difference in heart rate between inspiration and expiration.

[0085] Premature ventricular contractions (PVCs) and other ventricular arrhythmias are known to be suppressed by vagal activity. Thus, the external monitoring system may also monitor the occurrence of PVCs to evaluate intrinsic autonomic influences. In order to distinguish PVCs from normal R-waves (originating from atrial conduction), the system may look for a deviation from the average R-R interval that exceeds a certain percentage change, or it may use more advanced forms of PVC detection such as morphology discrimination.

[0086] Finally, the external monitoring system may include monitoring of physiological response to special clinical test scenarios in order to evaluate intrinsic autonomic tone. For instance, the magnitude of average heart rate change in response to atropine administration is considered a gold standard for evaluating cardiac intrinsic vagal tone. As shown in FIG. 6, administration of atropine (0.01 mg/kg and 0.02 mg/kg) causes a marked heart rate increase in healthy individuals. Specifically, the curve for young adults is labeled Y and the curve for elderly adults is labeled O.

[0087] In individuals with impaired intrinsic vagal tone, the heart rate change in response to atropine is blunted. Based on these known physiological factors, the external monitoring device can perform a method to test for a heart rate response to atropine as shown in FIGS. 7 and 8. The graph in FIG. 7 illustrates a typical response to atropine as measured by the device with 1 designating the normal period before the dosage was administered. At 2 the dose is administered and a button 35 on the wearable device 10 is pressed at 3 to indicate that the test has begun. The device continues to monitor and record heart rate throughout the process as disclosed above.

[0088] As can be seen, the atropine dosage typically increases the heart rate significantly to a peak at 4. The three stages are also shown in FIG. 7 as the pre-test, the delay and then the post-period with the response. The system logs beat-to-beat heart rate data in a memory buffer in the pre-test time. Then, the clinician inputs the start of the atropine test just prior to injection of the atropine bolus, for example, through pressing a button for a designated time or number of presses on the wearable device 10. The system logs the time at which the test is started. The system continues to store heart rate data to the memory buffer for a delay period and a post-test period. The system finds HRpre, the average heart rate during the pre-test period, and HR.sub.post, the average heart rate during the post-test period. The HR.sub.atropine is calculated as the difference HRpostHRpre.

[0089] Other examples of specialized tests which may be incorporated in a similar fashion include: measuring the heart rate recovery change following an exercise period; heart rate response to tilt testing, heart rate response to a Valsalva maneuver, and heart rate response to phenylephrine infusion. For all of the physiological parameters collected by the system, the results could be displayed as summary trends for the physician to interpret. In an exemplary embodiment, or the system itself could process the results of multiple physiological parameter calculations to determine a recommendation of whether the patient is a candidate (e.g. likely to be a responder) for autonomic neuromodulation.

[0090] The process for analyzing the test as performed by the external monitoring device is shown in FIG. 8. In this embodiment, three different physiologic parameters are used. First, the averaged heart rate at rest is assessed S800 and compared to two thresholds ( and ). If the heart rate at rest is less than a S801, the patient has good vagal tone and is not a candidate for therapy S802. If the heart rate at rest is greater than but less than S803, an atropine test is required for further characterization of the resting heart rate.

[0091] In the case that the response to atropine is blunted (less than a threshold c) S807 and/or the heart rate at rest exceeds S804, additional evaluation of HRV with respiration is performed S805 as described in FIG. 5. However, if the change in heart rate after the atropine dosage is greater than threshold c S808, then the patient is not a good candidate S809.

[0092] Finally, if HRV with respiration is greater than d S810, the patient does not have clear autonomic impairment and is not a good candidate S811; however, if HRV with respiration is less than d S812, there is clear evidence of vagal impairment and the patient is a good candidate S813 for neuromodulation therapy. For this system, some exemplary cutoff variables are shown in Table 1 below:

TABLE-US-00001 TABLE 1 Variable Threshold Example Value HR at rest lower threshold 60 bpm HR at rest upper threshold 75 bpm HR response to atropine c 30 bpm HRV with respiration d 6 bpm

[0093] Referring to FIG. 9, power spectral analysis of HRV examples are shown for different frequency ranges (HF=high frequency, LF=low frequency; VLF=very low frequency, ULF=ultra low frequency).

[0094] Referring to FIG. 10a, an example plot of SDNN values from HRV measurements with body temperature information of a patient is shown for evaluation of an infection. The data is evaluated over a time period of six month, and the plot shows the the SDNN values measured across this time period. From December to March, the measured SDNN is spread within a range of 50 ms to 112.5 ms, and the body temperature keeps below 37 C. In April, a drop can be observed of an SDNN below 50 ms, and whereby the body temperature rises significantly above 37 C. The combination of a significant drop of SDNN (for instance set to a change of 10-50% from the long term mean value) and a rise of body temperature above 37 C. can be identified by the inventive monitoring system as change of physiological parameters of the patient due to an infection. In May, SDNN rises again, and body temperature recovers to below 37 C.

[0095] FIG: 10b shows the SDNN values with body temperature information from FIG. 10a mapped along two axes infection no and infection yes. FIG. 10b allows to see the probability distribution of SDNN values for all measured SDNN values in the interesting time period. The outer shape formed by the values of infection no show an accumulation for SDNN=80 ms. Assuming 80 ms as the mean value for the time period of interest, the SDNN for infection yes are approximately 37-56% lower than the mean value.

[0096] The auto-screening of the candidates for neuromodulation therapy allows the physician to select the best possible patients for the response study without direct supervision. After some time at home or living in normal circumstances, the patient data collected can already rule out some candidates. The remaining candidates are then subjected to atropine tests. This reduces the upfront costs of the screening. The system also allows for automation of the atropine test.

[0097] The system sequences described above are exemplary and can be modified or combined. The recording intervals and the averaging period can be varied for different observation parameters. For instance, determining the nighttime heart rate at rest would not require a full 24 hours to be averaged. Likewise, the example thresholds listed above can change for young and old candidates or other patient variations.

[0098] The invention being thus described, it will be obvious that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the invention, and all such modifications as would be obvious to one skilled in the art are to be included within the scope of the following claims.

[0099] It will be apparent to those skilled in the art that numerous modifications and variations of the described examples and embodiments are possible in light of the above teaching. The disclosed examples and embodiments are presented for purposes of illustration only. Other alternate embodiments may include some or all of the features disclosed herein. Therefore, it is the intent to cover all such modifications and alternate embodiments as may come within the true scope of this invention.