METHOD AND SYSTEM FOR LOGGING QUANTITATIVE SEIZURE INFORMATION AND ASSESSING EFFICACY OF THERAPY USING CARDIAC SIGNALS

Abstract

A system and method for analyzing and logging changes in brain state of a subject for administering therapy to the subject based on the at least one cardiac signal wherein the system and method comprises the steps of receiving at least one cardiac signal of the subject into a processor, analyzing the cardiac signal to detect at least one cardiac signal change indicative of a brain state change, and logging at least one characteristic of the detected signal change or brain state change.

Description

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to medical devices and, more specifically without limitation, to implanted medical devices.

2. Description of the Related Art

Epilepsy affects about 2.3 million Americans, and its direct and indirect annual costs amount to approximately $12.5 billion. Although anti-epileptic drugs are useful, 20-30% of persons are not helped by them and up to 30% of those treated have intolerable or serious side effects.

Recently published studies demonstrate the importance of quantitative analysis of brain signals for automated warning and blockage of seizures, optimization of existing therapies and development of new ones. Cardiac activity is under cerebral control. That is, certain changes in global, regional or focal brain activity, either physiological or brain states associated with changes in heart activity including but not limited to changes in heart rate, most frequently an increase and referred to as ictal tachycardia, or in other indices of cardiac function such as R-R variability. The incidence of heart changes increases as the seizure spreads outside its site of origin to other brain regions being, for example invariably present in all subjects with primarily or secondarily generalized tonic-clonic seizures (“convulsions”), in whom purportedly, most or all of the brain is involved. These changes reflect shifts in the ongoing interplay between sympathetic and parasympathetic influences, which can be quantified using time or frequency domain methods of analysis. For example, tachycardia precedes electrographic onset of temporal lobe seizures by several seconds, as ascertained via scalp electrodes (EEG), while combined activation of parasympathetic and sympathetic systems as estimated by using spectral analysis of oscillations in R-R intervals at respiratory and non-respiratory frequencies, may be detectable minutes in advance of seizure onset. Since these changes may precede visible electrographic or behavioral manifestations indicative of seizures and even of the so-called “aura,” they may have predictive value. Real-time prediction or detection of epileptic seizures, based on extracerebral sources such as the heart, is of great clinical and practical value as it obviates the reliance on cerebral signals which are highly complex and of high dimensionality and whose origin may not only be difficult to localize but quite often requires invasive intracranial implantation of electrodes or other sensors.

While methods presently exist to detect seizures using cardiac signals and quantify their characteristics, for example as described in U.S. Pat. No. 6,341,236 which is incorporated herein by reference in its entirety, no system for logging the times of seizures and their quantitative characteristics, such as date and time of occurrence, and duration based on the degree of cardiac changes, and for using this information in the objective assessment of seizure frequency and of therapeutic intervention, presently exists. This is partially due to the impact of artifacts (noise) on EKG signal analysis which can lead to inaccuracies in heart rate assessments.

Thus, the need exists for a system and method for logging seizures, or other events originating in the brain that impact cardiac activity, and associated event characteristics such as frequency, duration, intensity, and severity. Moreover, this system and method needs to be robust in the presence of artifacts or other sources of noise. The need also exists for a minimally invasive system and method to provide effective and objective means for assessing the efficacy of therapies used to control seizures.

SUMMARY OF THE INVENTION

Changes in heart activity associated with seizures can be used to automatically and in real-time detect the seizures, quantify their frequency, duration, intensity, or severity as reflected in the cardiac signal changes, predict their electrographic or clinical onset in a subset of cases, and control the seizures via therapeutic intervention. The present invention enables the logging of this information and its utilization to objectively assess the efficacy of an applied therapy. To accomplish this task with improved robustness in the presence of signal artifacts or noise, the invention can utilize EKG and complimentary information obtained from other signals representative of cardiac function such as the phonocardiogram (PKG), echocardiogram, or ultrasound.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

FIG. 1 is a schematic representation of a system for receiving and analyzing cardiac signals and detecting and logging seizures according to the present invention.

FIG. 2 shows an EKG signal corrupted by artifact at the start of a seizure.

FIG. 3 illustrates simultaneously recorded EKG and PKG data and the ability of PKG to provide complementary information regarding cardiac function contained therein.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Changes in certain types of global, regional or focal brain activity, either physiological or pathological, modify heart activity. Epileptic seizures are one important example of a pathological brain state with demonstrated association to changes in heart activity. The incidence of heart changes increases as the seizure spreads outside its site of origin to other brain regions being, for example invariably present in all subjects with primarily or secondarily generalized tonic-clonic seizures (“convulsions”), in whom purportedly, most or all of the brain is involved. Real-time prediction or detection, and quantitative analysis of epileptic seizures, based on extracerebral sources such as the heart, is of great clinical and practical value as it obviates the reliance on cerebral signals which are highly complex and of high dimensionality, and whose origin may not only be difficult to localize but quite often requires invasive intracranial implantation of electrodes or other sensors. The invention disclosed herein, and depicted schematically in FIG. 1, utilizes information present in signals 101 representative of the cardiac activity state, fed to system 100 that performs real-time quantitative analysis of this information in a processor 102. This processor analyzes and measures relevant changes in the cardiac activity state in order to predict or detect and quantify underlying events occurring in the brain of a subject. The system is configured to include a clock 103 and non-volatile memory 104 to enable these events and relevant data and information about associated event features to be logged. These features may include, but are not limited to, start time, end time, or duration of detected cardiac changes which provide information about underlying seizures or other brain events, the frequency or relative intensity of these changes, and the evolution of the distribution of such features as inter-beat intervals. These and other features representative of cardiac state well known to one skilled in the art, such as heart rate and heart rate variability measures, can be measured and relevant changes logged. The system can be configured with a communication interface 105 that allows the subject or other user to access information from the logs stored in non-volatile memory 104 and/or to program parameters used in the operation of the system 100, including parameters involved in the analysis of cardiac signals performed using processor 102.

While methods presently exist to detect seizures using cardiac signals and quantify their characteristics, for example as described in U.S. Pat. No. 6,341,236, no system presently exists for logging these quantitative characteristics, such as times of occurrence, durations and degrees of cardiac changes associated with seizures or other underlying brain events and for using this information in objective assessment of the neurological disorder and of efficacy of therapeutic intervention. This is partially due to the impact of artifacts (noise) on EKG signal analysis which can lead to inaccuracies in heart rate assessments.

Seizures often are associated with movement, muscle, and other artifacts that can obscure or distort the EKG making it difficult or impossible to extract the important information contained in the contaminated signals. FIG. 2 illustrates an EKG signal from a subject just before and during the onset of an epileptic seizure in which there is significant artifact present as the seizure begins. This artifact makes it difficult to determine from this signal precisely when heart beats are occurring. One strategy to overcome noise is to simultaneously acquire other cardiac signals such as PKG to take advantage of the fact that these other signals have different sensitivities than EKG to certain types of noise. This approach increases the information content about the state of the heart, and indirectly about the state of the brain, and decreases the probability of signal loss or degradation by noise. FIG. 3 illustrates the differential sensitivity to noise: the EKG signal shown is contaminated by muscle artifact, a common source of noise during seizures, and is not easily recognizable visually or using spectral signal analysis while the simultaneously recorded PKG is virtually immune to this type of noise. The two arrows in FIG. 3 annotate time points when R-waves in the EKG are obscured by muscle artifacts; FIG. 3 also displays the simultaneously recorded PKG signal which is immune to this type of artifacts.

The PKG can be used instead of EKG for tracking heart rate and its variability given the high temporal correlation between the S1 and the QRS complex. Simultaneous use of PKG and EKG allows the system access to good quality information for more complete and accurate tracking of the cardiac system dynamics and, indirectly, of brain dynamics effecting the heart. Those skilled in the art can appreciate that many other types of physical or chemical heart signals suitable for use with implantable devices can also be used in addition to, or instead of, EKG and PKG for this purpose.

In view of the clinical importance of real-time automated quantitative seizure analysis and the greater signal-to-noise ratio and shorter propagation time from source to sensor, and ease of use of heart signals (electrocardiogram, EKG, or phonocardiogram, PKG) compared to scalp signals (EEG) or intracranial recording of electrical signals (ECoG), the approach of the present invention disclosed herein makes use of these signals for a) the invasive or non-invasive extracerebral, real-time automated detection of seizures based on heart signals; b) the logging of frequency, date/time of occurrence, relative intensity and relative duration of seizures; c) the anticipation of electrographic or behavioral seizure onset and/or loss of function in a subgroup of subjects with epilepsy, for automated warning and other useful purposes; and d) the automated delivery of a selected therapy, either contingent or closed-loop. Additionally and of equal importance is the ability to detect, in real-time, cardiac rhythm abnormalities, which may be life-threatening and which are temporally correlated with seizures or occur in between seizures and to provide appropriate intervention such as pacing or defibrillation.

Use of cardiac signals such as EKG and/or PKG for the automated detection of seizures, and in certain cases for the anticipation of their onset, will complement and, in a subgroup of subjects with pharmaco-resistant epilepsy, may replace scalp or intracranial (invasive) acquisition of cortical signals for automated warning and/or therapy delivery and in either case will allow for seizure logging and other tasks. One of the main advantages of using EKG/PKG for seizure detection is that unlike methods based on cortical signals recorded either directly from the cortex, which requires a craniotomy or burr hole, or indirectly from the scalp (EEG), it is not critically dependent on accurate placement of electrodes and, in a subset of cases, the onset of heart changes may even precede scalp or behavioral changes providing yet another advantage.

Heart signals, can be obtained from several body sites obviating, in a subgroup of patients, the need for surgery, thus decreasing the inconvenience, stress, cost, potential morbidity and recovery time associated with such procedures. Furthermore, the wealth of commercially available, low power, implantable devices for analysis and control of heart signals can be easily leveraged for this application. Another advantage of EKG over EEG or ECoG is its lower dimensionality and relative simplicity wherein a single channel recording is sufficient for capturing all of the information necessary for the tasks at hand.

Those skilled in the art can appreciate that in certain cases or situations, dual, simultaneous monitoring of brain and heart may be necessary or useful to improve detection of changes in either organ or to improve the efficacy of control measures. Also, undesirable changes in heart activity caused by abnormal brain activity may be better controlled by directing the intervention to the brain rather than to the heart. For instance, while asystole caused by seizures can be controlled using a demand pacemaker, a more definitive and rational approach is to prevent or block asystole-inducing seizures. It is clear that the dynamic interactions between heart and brain can be exploited to detect changes and to control them by monitoring either organ or both and by applying control to either of them, or to both.

Frei and Osorio and others, see for example U.S. Pat. No. 6,341,236, have disclosed methods for automated EKG analysis and detection of cardiac signal changes associated with epileptic seizures. The methods developed by Frei and Osorio are especially well-suited for seizure detection using heart signals, a task which requires analysis of data over very short windows (1-2 sec). The length requirements of other methods for standard low/ultra-low frequency band power assessments of heart rate variability, typically five-minute segments assumed to be stationary, are much longer than the duration of a seizure or a dangerous cardiac abnormality that may lead to sudden death. In addition, the assumptions regarding stationarity of the system/signal are counter to the well-known nonstationarity of the normal cardiac system. Given these deficiencies, it is therefore impractical to apply methods of heart signal analysis that require long segments of data, minutes for example, for the detection of phenomena for which warning and control must take place in a very short time period (e.g., under ten seconds) for purposes of safety and efficacy. Another advantage of the⋅methods of the present invention is that they can be used to quantify the intensity, type, and evolution of cardiac changes, which in turn may be used to detect and estimate the duration and intensity of underlying brain state changes whether physiological or pathological. The changes in heart activity that may indicate a possible onset of a seizure (a pathologic state change) include, but are not limited to, changes in heart rate and heart rate variability and their interrelationship, rhythm, morphology of the P-QRS-T complex or of the length of the different intervals (e.g., Q-T).

One skilled in the art can appreciate that, in addition to the aforementioned methods, a number of methods for detection and analysis of cardiac signal changes exist in prior art, many of which have been implemented in hardware, software, or in a hybrid configuration, and which can be used, for example, to obtain the time of each heart beat as well as the interbeat (e.g., R-R) interval. One skilled in the art will appreciate for example, that the R-R interval times or other quantities representative of cardiac state may be processed/analyzed using methods in the time or frequency domains to generate a multitude of derivative signals/sequences or ratios from which a wealth of information can be obtained about the heart, including instantaneous heart rate (IHR), its average rate in fixed or moving windows of any desired length, and measures of heart rate variability (“HRV”) (e.g., standard deviation of means of R-R intervals in a moving window, or the second derivative of instantaneous heart rate, etc.) on any desired timescale but with emphasis on those timescales suitable for seizure detection. Changes in the distributions of these, or any other quantity derived from the time-of-beat sequence, as time evolves can be detected and quantified in real-time, for example using the “lambda estimator” as disclosed in Nikitin et al, U.S. Patent Application Publication No. 20030187621, or other statistical methods. Stereotypical patterns, if found in these data, may be learned over time as more seizures are recorded and analyzed. For example, cardiac data from a subject can be used to establish normal or baseline patterns for this subject and compared against moving windows of new data to determine deviations from normalcy or baseline, proximity to dangerous or undesirable patterns, and to quantify these deviations. Degree of absolute or relative changes in heart rate, heart rate variability, and their interrelationship, ST-wave depression, and QT prolongation are examples of such quantities. The time of specific changes and their duration and/or intensity are obtainable from these analyses. As in the aforementioned Nikitin et al reference, one skilled in the art will appreciate that the analysis of interest can⋅be multifactorial and/or multidimensional. For example, U.S. Pat. No. 6,341,236 of Osorio and Frei disclosed that changes in the relationship between IHR and HRV provide information about heart function which is not obtainable if IHR. and HRV are analyzed separately.

The aforementioned lambda estimator provides one of many possible examples illustrating how statistical changes in feature signals, even when multi-dimensional, obtained from cardiac recordings, such as EKG, can be quantified as they evolve. By applying thresholding techniques or, more generally, by identifying values of quantified features that are associated with particular cardiac or body states, e.g., seizures, the start and end of these state changes can be localized in time and their relative intensity quantified.

These analyses may also be applied to PKG signals in order to detect similar, complementary, or different changes reflective of heart state. Other measures that may be used by the present invention include but are not limited to: duration including time of onset and termination of changes in heart rate or in any of its derivatives; changes in heart rate variability or in any of its derivatives; changes in rhythmicity or in generation and conduction of electrical impulses; or changes in the acoustic properties of heart beats and their variability. It will be appreciated that additional information may be obtained through analysis of occurrence times of other EKG waveforms such as Q-T intervals, changes in spectral properties of the EKG or signal morphology, as well as time-of-beat information obtained from PKG such as sl-sl intervals, amplitude (magnitude) of the signal, or changes in its waveshape and/or spectral characteristics, etc. For example, changes in the magnitude or rate of change of the high- and low-frequency components of the heart beat, using autoregressive, Fast Fourier, wavelets, Intrinsic Timescale Decomposition (U.S. patent application Ser. No. 10/684,189, filed Oct. 10, 2003), or other suitable techniques, may be used alone or combined with other cardiac measures to increase the sensitivity, specificity, and/or speed of prediction or detection of seizures or in their ability to quantify brain state changes. Other measures derived from the raw or processed signals that may be of additional use in the present invention include, but are not limited to, analysis of entropy, correlation dimension, Lyapunov exponents, measures of synchronization, fractal analysis, etc.

The real-time prediction, detection and quantification of seizures that is possible by using the methods disclosed herein and/or associated systems may be adapted or tailored to fit an individual's cardiac state change patterns or characteristics, thereby increasing sensitivity, specificity or speed of detection of state changes. The performance of the detection methods, such as sensitivity, specificity and speed, may be enhanced, if necessary, by characterizing baseline patterns for a subject and comparing them against moving windows of current data to determine and quantify deviations from baseline and proximity to patterns indicative of state change. These can be used, together with the frequency, intensity, and duration of heart signal changes, time to maximal deviation from baseline, time to recovery to baseline rates, for assessing a patient's condition, safety risks, and even efficacy of therapy. Moreover, degree of conformance to stereotypical cardiac signal patterns that may be associated with certain seizure types can be used to infer other severity-related measures such as degree of seizure spread in the brain. Simultaneous recording and analysis of other non-cardiac signals, such as muscle, joints, skin or peripheral nerves, may also improve prediction, detection and quantification of state changes. For example, the recording, analysis, and comparison of changes in cardiac signals during the state change of interest, e.g., seizures, to that obtained during activities such as exercise, can increase their sensitivity, specificity, and/or detection speed for real-time seizure detection purposes, or for detecting changes of body state. These processes may be carried out on- or off-line.

The information about heart state provided by the present invention can be used to compute seizure index, which is defined as the fraction of time spent in a seizure over a moving window of a given size. The information can also be used to determine seizure severity, e.g., using the product of intensity and duration. These and other measures may be logged as part of the present invention (or later computed from other logged information) and can provide valuable diagnostic and prognostic information, as well as information regarding efficacy of any therapy attempted during the period of monitoring/analysis. The set of logged information stored by the present invention can also be used to develop models that may allow or refine seizure prediction or detection (using cardiac signals or in general) and shed light on an individual's seizure dynamics.

The implantable or portable device implementing the present invention is configured to include a real-time clock and a rewritable, non-volatile memory, as well as one or more sensors for use in recording EKG, PKG and/or other representative signals indicative of cardiac function and/or state, such as echocardiogram, ultrasound, blood pressure, blood flow rate or volume, heart muscle tension, etc., and processing components capable of receiving, conditioning, and analyzing the EKG and/or PKG signals to detect and/or quantify events of interest such as seizures. The logging process consists of reading the real-time clock each time an event or cluster of events of a certain designated type occurs, and logging the clock time and variables associated with the quantification of the event to the non-volatile memory. These variables may include but are not limited to information obtained through processing of the signals, and/or the raw signals themselves, i.e., “loop recordings” of events.

The system of the present invention may be further configured with an output mechanism to: a) warn the subject of an impending seizure or other type of detected event such as a cardiac arrhythmia, low system battery, full memory, etc., and b) deliver a selected therapy to the subject when heart activity reaches or exceeds safe or prespecified limits. For example, Osorio and Frei in U.S. Pat. No. 6,341,236 disclose a means to trigger the pacing of the heart in the event of a seizure detected by analysis of EKG. Osorio et al., in U.S. Pat. No. 5,995,868, disclose a method of treating seizures by, among other methods, stimulating the brain, heart and/or vagus nerve when a seizure is detected. The output mechanism may include or be connected to a neurostimulator and/or a pacemaker to control brain and/or heart activity within prespecified tolerable/safe limits. Commonly used types of warnings include audio alarms with varied tones and/or combinations of short and long sounds, other types of acoustic devices, LED or other visual displays, e.g., flashing lights, etc., low-voltage so-called “tickler” stimulus, and communication with external devices, e.g., triggering an external device such as “calling 911,” etc.

Any additional implanted or portable device may also use the non-volatile memory for storing information about events through the use of a uni- or bi-directional communications protocol. For example, a pacemaker that detects an unusual EKG rhythm or heart beat pattern could trigger the device described herein that an event has occurred and potentially could communicate other features/attributes of the event, such as type, severity, etc., to the device for logging purposes. The system may also contain a display, or means to be externally interrogated, to review and/or download the information it has stored and/or logged for review by the user, subject, or physician. In addition to logging seizures or other events of neurological origin which impact the cardiac system, the system and method of the present invention can be used to objectively assess the efficacy of therapies used to control the occurrence or severity of these events. For example, when a subject takes medication in order to control his seizures, the availability of a seizure log that includes their time of occurrence, severity, and other features can be analyzed in reference to administration times and concentrations of medication or other therapy, which also can be logged by the system via the communication interface described above. Such comparisons enable the modeling and objective efficacy assessment of the effect of the therapy on the system.

For instance, the seizure frequency measure plotted against the level of medication expected to be present in the subject's system as time evolves allows the user to optimize dosing levels and times to minimize seizure frequency.

It is to be understood that while certain forms of the present invention have been illustrated and described herein, it is not to be limited to the specific forms or arrangement of parts described and shown.

Claims

1. A method of detecting and logging a non-induced seizure event, comprising: receiving a cardiac signal of a patient into a processor;determining via the processor a first cardiac parameter based on the received cardiac signal;detecting a non-induced seizure based on the first cardiac parameter;determining via the processor at least one seizure characteristic, wherein the seizure characteristic is different from the first cardiac parameter; andlogging the at least one seizure characteristic in a memory.

2. The method of claim 1, wherein the first cardiac parameter is a parameter indicative of at least one of: a change in heart rate; a change in heart rate variability; or a change in an acoustic property of at least one heart beat.

3. The method of claim 1, wherein the at least one seizure characteristic comprises at least one of: a date and a time of an occurrence of the seizure;a start time of the seizure;an end time of the seizure;a duration of the seizure;an intensity of the seizure;a severity of the seizure;a degree of spread of the seizure in a brain of the patient; ora fraction of time spent in the seizure over a moving time window.

4. The method of claim 1, further comprising: logging at least a time of an occurrence for at least two seizure events; anddetermining, based on the logging of the at least two seizure events, a seizure frequency.

5. The method of claim 1, further comprising: receiving an electrocardiogram (“EKG”) signal;receiving a phonocardiogram (“PKG”) signal when the EKG signal-to-noise ratio is below a predetermined number; andwherein the determining the first cardiac parameter is based on the EKG signal and on the PKG signal when the EKG signal-to-noise ratio is below the predetermined number.

6. The method of claim 1, further comprising: performing an action in response to detecting the seizure, wherein the action is at least one of: providing a warning signal of the seizure;providing a warning signal of a detected arrhythmia;providing a therapy to treat the seizure utilizing a heart pacing procedure, stimulating a brain of the patient, stimulating a heart of the patient, or stimulating a vagus nerve;displaying the at least one seizure characteristic; andassessing an efficacy of a patient therapy.

7. The method of claim 1, further comprising: administering a therapy in response to detecting the epileptic seizure based on the cardiac signal change;receiving a second cardiac signal of the patient into the processor, after detecting the epileptic seizure and administering the therapy;determining a second cardiac signal change based on the received second cardiac signal;assessing at least one of an efficacy of the therapy and an occurrence of a side effect based on the cardiac signal change and the second cardiac signal change; andlogging information corresponding to at least one of the efficacy of the therapy and the occurrence of the side effect into the memory.

8. The method of claim 7, further comprising communicating at least one of the logged information corresponding to the efficacy of the therapy and the occurrence of the side effect to the external device.

9. The method of claim 7, wherein administering the therapy comprises applying an electrical signal to a vagus nerve of the patient.

10. The method of claim 7, wherein the second cardiac signal is at least one of an electrocardiogram signal and a phonocardiogram signal.

11. The method of claim 7, further comprising modifying at least one of the therapy and at least one stimulation parameter based on at least one of a determination that the therapy is not efficacious and a side effect determination.

12. A method of detecting and logging an epileptic seizure event via a medical system comprising: receiving a cardiac signal of a patient into a processor of the medical system;detecting via the processor of the medical system a cardiac signal change associated with an epileptic seizure;determining via the processor of the medical system an epileptic seizure characteristic where the epileptic seizure characteristic is at least one of an intensity, a duration, a severity, an extent of spread, a date, and a time of an occurrence of the epileptic seizure;logging the epileptic seizure characteristic in a memory;transferring to an external device the logged epileptic seizure characteristic;administering a therapy in response to detecting the epileptic seizure based on the cardiac signal change;receiving a second cardiac signal of the patient into the processor, after detecting the epileptic seizure and administering the therapy;determining a second cardiac signal change based on the received second cardiac signal; andassessing at least one of an efficacy of the therapy and an occurrence of a side effect based on the cardiac signal change and the second cardiac signal change.

13. The method of claim 12, further comprising logging information corresponding to at least one of the efficacy of the therapy and the occurrence of the side effect into the memory.

14. The method of claim 12, further comprising classifying the epileptic event as one of a partial seizure and a generalized seizure based on the cardiac signal change.