WO2006076478A2 - System and method for prediction of adverse events during treatment of psychological and neurological disorders - Google Patents
System and method for prediction of adverse events during treatment of psychological and neurological disorders Download PDFInfo
- Publication number
- WO2006076478A2 WO2006076478A2 PCT/US2006/001059 US2006001059W WO2006076478A2 WO 2006076478 A2 WO2006076478 A2 WO 2006076478A2 US 2006001059 W US2006001059 W US 2006001059W WO 2006076478 A2 WO2006076478 A2 WO 2006076478A2
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- WIPO (PCT)
- Prior art keywords
- biopotential
- feature
- predicting
- adverse events
- signals
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Classifications
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/369—Electroencephalography [EEG]
- A61B5/372—Analysis of electroencephalograms
- A61B5/374—Detecting the frequency distribution of signals, e.g. detecting delta, theta, alpha, beta or gamma waves
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
Definitions
- Depression is a mood disorder that affects 17 million Americans each year, and is responsible for 9.7 million doctor visits. It affects sufferers in a variety of ways, resulting in depressed mood, irritability, sleep disorders, feelings of agitation, guilt and worthlessness, loss of energy and initiative, an inability to concentrate and an increased incidence of suicide.
- antidepressant pharmacological agents There are a number of antidepressant pharmacological agents, and once the proper treatment is determined, their effectiveness is quite high.
- MDD Major Depressive Disorder
- the American Association of Suicidology notes on their website that the lifetime risk of suicide among patients with untreated MDD is nearly 20% . About 2/3 of people who complete suicide are depressed at the time of their deaths. In a study conducted in Finland, of 71 individuals who completed suicide and who had Major Depressive Disorder, only 45% were receiving treatment at the time of death and only a third of these were taking antidepressants.
- EEG electroencephalographic
- the present invention is a system and method of deriving and computing features and indices that predict the likelihood of psychological and neurological adverse events such as suicidal thoughts and/or actions.
- the method of the present invention further predicts the likelihood of suicidal thoughts and/or actions prior to and or during treatment for psychological disease.
- power spectrum and time domain values are derived from biopotential signals acquired from the subject being tested.
- the system and method identify people who are likely to experience changing, especially worsening, symptoms of psychological and neurological adverse events such as suicidal thoughts or actions and who therefore may be at risk (e.g. suicide).
- Figure 1 is a block diagram of the system of the present invention for predicting adverse events during treatment of psychological and neurological disorders.
- Figure 2 is a flow chart of the steps of the method of the present invention.
- Figure 3 is an error bar chart showing the values of the Index Pred2 for the Worsening Suicide Ideation (SI) and Not Worsening SI groups, stratified by antidepressant treatment.
- Figure 4 is an error bar chart showing the value of Pred2 vs. the maximum change from baseline observed in Ham-D item 3 during the first four weeks of treatment.
- Figure 5 is an error bar chart showing the baseline value of the left-minus-right relative theta+alpha asymmetry feature (BDRTAS 12) for the Worsening SI and Not Worsening SI groups, stratified by antidepressant treatment.
- BDRTAS 12 left-minus-right relative theta+alpha asymmetry feature
- Figure 6 is a scatter plot of left-minus-right relative theta+alpha asymmetry measured at baseline (BDRTAS 12) and at 1 week (DRTAS 12).
- a preferred embodiment of the present invention shown in Figure 1 incorporates a Data Acquisition Unit (DAU) 20 that is used to acquire an EEG signal in step 22 from a subject 10 for subsequent processing.
- the DAU 20 typically consists of a computer system with an integral analog-to-digital (A-D) converter 25 and a set of electrodes that is representatively shown placed on the scalp of a subject 10. While only a single electrode 15 is shown, any montage of electrodes used to obtain EEG signals may be used in the invention.
- the A-D converter 25 is used to transform in step 24 the analog EEG signals obtained from the electrodes 15 into a sampled set of signal values that may then be analyzed by the processor 35 of a Data Computation Unit (DCU) 30.
- the DCU 30 incorporates a processor 35 and a communications device that receives the sampled values from the DAU 20.
- the processors of the DAU 20 and DCU 30 are one and the same.
- the DAU 20 may acquire the EEG signals and transmit the sampled EEG signals over a communications link to a remote DCU 30.
- Such a communications link may be a serial or parallel data line, a local or wide area network, a telephone line, the Internet, or a wireless connection.
- the clinician conducting the assessment may communicate with the DCU 30 using a keyboard 40 and display device 50.
- an additional keyboard and display device may be attached to the DAU 20 for the use of the clinician.
- the DCU 30 After the DCU 30 receives the sampled values from the DAU 20, the DCU 30 first examines in step 26 the sampled EEG signals for artifact arising from patient movement, eye blinks, electrical noise, etc. Detected artifact is either removed from the signal, or the portion of the signal with artifact is excluded from further processing.
- the EEG signal is also filtered to reduce or remove artifact from high and/or low frequency noise sources, such as electromyographic and radio frequency interference and movement artifact, respectively. Low-pass filtering is also employed prior to sampling to reduce the power at frequencies above the signal band of interest, preventing that power from appearing artifactually at lower frequencies due to an inadequate sampling frequency (aliasing).
- the DCU 30 next computes a set of parameters from the artifact-free EEG data in step 28.
- Parameters may be derived from power spectral arrays, higher-order spectral arrays (bispectrum, trispectrum, etc.), cordance (such as described in U.S. Pat. No. 5,269,315 and U.S. Pat. No. 5,309,923), z-transformed variables, entropy metrics, and time-domain metrics, including but not limited to parameters derived from various techniques applied to the various data series, such as template matching, peak detection, threshold crossing, zero crossings and Hjorth descriptors.
- features are referred to as features.
- Features may also be formed from combinations of parameters.
- An index is a function incorporating one or more features as variables.
- the index function may be linear or nonlinear, or may have an alternative form such as a neural network.
- the DCU 30 calculates from all the parameters a series of features and indices that are predictive of the probability the subject may experience adverse events, such as suicide ideation or suicidal actions. These features and indices may be displayed to the user on the display device 50 in step 34. In the embodiment in which the DCU 30 is remote from the DAU 20, the result may be transmitted back to the display device on the DAU 20, or transmitted to the patient's physician via e-mail or made available via a secure internet World Wide Web page.
- the EEG data is collected using Ag-AgCl electrodes of the type sold by Grass-Telefactor of Warwick, Rhode Island under the designation Model F- E5SHC.
- a bipolar 4-channel electrode montage is preferentially utilized, with each EEG channel collected as the voltage difference between each of the four pairs of electrodes F7- Fpz, F8-Fpz, Al-Fpz and A2-Fpz (International Ten-Twenty System of Electrode Placement, Jasper) where Al is the left earlobe and A2 is the right earlobe.
- the electrodes are preferably of the Zipprep ® type manufactured by Aspect Medical Systems, Inc.
- Electrodes When electrodes are placed within the hair, gold-cup type electrodes may be used, held in place by either collodion or a physical restraint such as an electrode cap placement device, as provided by various manufacturers. A variety of different electrode placements, or montages, may be used.
- EEG signals are sampled by the A-D converter 25 at 128 samples-per-second, preferably while the subject's eyes are closed in order to minimize eye-blink artifacts.
- the sampled EEG signal from each electrode pair is processed independently; the initial processing will be described for a single channel, but it should be understood that it is identical for each channel.
- the sampled EEG signal is divided into non- overlapping, 2-second epochs. In the preferred embodiment, 4 minutes of EEG data is processed, consisting of 120 non-overlapping, consecutive, 2-second epochs.
- a power spectrum (at 0.5 Hz resolution) is calculated using a Fast Fourier Transform (FFT) after first mean de-trending to remove the DC (offset) component of the signal and then minimizing spectral leakage (smearing) by multiplying the epoch with a Hamming window.
- the median power spectrum of the 120 epochs is calculated by computing the median of the corresponding frequency values of the power spectra associated with each of the 120 epochs.
- Absolute and relative powers are calculated from the median power spectrum for a set of predefined frequency bands; these are the theta (4-7.5 Hz), alpha (8-11.5 Hz), theta+alpha (4-11.5 Hz) and total power (2-20 Hz) frequency bands.
- the absolute power is calculated as the sum of the power within each specific frequency band in the median power spectrum, and the relative power is calculated as the ratio of the absolute power of a specific frequency band to the absolute power of the total power frequency band.
- Various absolute and relative powers as well as combinations, products and ratios of absolute and relative powers within and among the EEG channels are combined to form a pool of candidate features.
- the pool of candidate features could be extended beyond power spectral features to include features derived from other methods of representing EEG information, including, but not limited to, bispectral analysis, time- frequency analyses, entropy metrics, fractal metrics, correlation dimension, as well as cross- channel analyses including coherence, cross-spectra, cross-bispectral features and mutual information metrics.
- a set of EEG features are combined to form an index whose value is predictive of the probability that the subject will respond to antidepressant treatment.
- the mathematical structure of the index, the variables and the coefficients used and their method of combination were developed using a statistical modeling technique.
- DSM-IV Diagnostic and Statistical Manual of Mental Disorders - Fourth Edition
- SSRI serotonin reuptake inhibitor
- MRT12 is the mean of the relative theta powers calculated on channels Al-Fpz and A2-Fpz
- MRT12 one _ W eek is the value of MRT 12 measured at one week
- BMRT12 is the value of MRT12 measured at baseline
- MRT78 is the mean of the relative theta powers calculated on channels F7-Fpz and F8-Fpz,
- MRT78one_week is the value of MRT78 measured at one week
- DRTAS 12 is the value of the combined relative theta+alpha power on channel Al- Fpz minus the combined relative theta+alpha power on channel A2-Fpz (DRTAS 12 is therefore a measure of left-minus-right asymmetry),
- DRTAS 12 O ne_week is the value of DRTAS 12 measured at one week
- BDRTAS12 is the value of DRTAS12 measured at baseline.
- the structure of the index Pred2 and its components were further refined to form an index whose value is predictive of the probability of the subject suffering an adverse event.
- the adverse event is the ideation of suicide (e.g., the occurrence of suicidal thoughts or actions, as quantified by a neurocognitive assessment scale).
- Pred2 Index In order to evaluate the ability of the Pred2 Index to predict suicide ideation, following model development additional subjects were added to the database for a total of 42 subjects. Item 3 of the Hamilton Depression Rating Scale was examined for each subject to identify those individuals who developed new (or worsening) symptoms of suicide ideation. Pred2 and its components were evaluated to determine if they could predict which subjects would have new or worsening symptoms of suicide ideation. These variables were also evaluated to determine if they correlated with change in severity of symptoms of suicide ideation from baseline.
- a binary- valued variable (SuicideGroup) was calculated for each subject to indicate whether the subject developed new or worsening symptoms of suicide ideation (WorseSI) or not (NotWorseSI) at visits at 1 and 4 weeks.
- Analysis of variance of Pred2, controlling for the antidepressant treatment the patient later received i.e., escitalopram, fluoxetine or venlafaxine
- escitalopram e.citalopram
- fluoxetine venlafaxine
- Figure 3 The horizontal line across the inside of the box is the median value of the data points in each subgroup, while the upper and lower box edges are the 75 th and 25 th percentiles, respectively, and thus the box length is the interquartile range.
- BDRTAS 12 baseline relative theta+alpha asymmetry
- PPA positive predictive accuracy
- NPA negative predictive accuracy
- Relative theta+alpha asymmetry (DRTAS 12 0ne _week) measured at week 1 provides additional information that improves discrimination of subjects who do (and don't) develop SI symptoms ( Figure 6). EEG asymmetry in subjects who developed new SI symptoms initially was > 0 at baseline, and did not significantly decrease after 1 week of treatment.
- FIG. 5 shows that the distance from the origin (0,0) of the DRTAS 12 on e_w e ek vs. BDRTAS 12 relationship is a predictor of the probability of suicide ideation in a specific individual. All those patients who experienced suicide ideation were tightly clustered at the center of the DRTAS 12 one _ Week vs. BDRTAS12 scatter plot. Among those patients corresponding to data points far from the origin there were no instances of suicide ideation. Therefore, an alternate embodiment of the invention is derived from the sum of the absolute values of DRTAS 12 on e_ w eek and BDRTAS 12.
- a very low risk of suicide ideation is associated with values of Index SU icide_ideation > 0.06.
- a mathematically intuitive measure of the risk of suicide ideation may be constructed as the distance of a data point from the origin of the scatter plot in FIG. 5, computed as
- the EEG Pred2 index and the EEG asymmetry features DRTAS 12 0ne _week and BDRTAS 12 are useful predictors of response to treatment and probability of adverse events, especially suicide ideation. Change in these metrics in response to initial treatment may provide additional information that might improve prediction performance. Although these metrics were developed to predict responses related to pharmacological treatment, it is anticipated that they may predict response to other forms of treatment, including, but not limited to, psychotherapy, electroconvulsive therapy (ECT), transmagnetic stimulation and various forms of neurostimulation including deep brain stimulation and peripheral nerve stimulation (e.g., vagus nerve stimulation).
- ECT electroconvulsive therapy
- transmagnetic stimulation various forms of neurostimulation including deep brain stimulation and peripheral nerve stimulation (e.g., vagus nerve stimulation).
- indices of the preferred embodiment were developed to predict responses and events related to treatment of depression, it is anticipated that these metrics may predict response and/or adverse events when treating other types of psychological and neurological disorders, including, but not limited to, anxiety, bipolar depression, mania, schizophrenia, obsessive-compulsive disorder and dementia.
- EEG Pred2 index the EEG asymmetry features DRTAS 12 one _week and BDRTAS 12, and the indices Index SU i C ide_ideation and IndeX su i c i de j deat i o i ⁇ , may be used to predict onset of adverse symptoms, including changes in suicide ideation and suicidal actions.
- These indices, as well as other EEG-based indices, hereafter referred to as EEG Index may also be used prior to treatment to predict eventual onset of symptoms due to treatment.
- the EEG Index may be computed and used to predict the onset of adverse symptoms throughout the course of therapy.
- the EEG Index may be used to predict other adverse symptoms such as somatic symptoms, sexual side-effects, nausea, vomiting and other symptoms not considered to be manifestations of improvement of the psychological and/or neurological condition.
Description
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Priority Applications (6)
Application Number | Priority Date | Filing Date | Title |
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BRPI0606306-3A BRPI0606306A2 (en) | 2005-01-12 | 2006-01-12 | system and method for predicting adverse events during the treatment of psychological and neurological disorders |
MX2007008439A MX2007008439A (en) | 2005-01-12 | 2006-01-12 | System and method for prediction of adverse events during treatment of psychological and neurological disorders. |
EP06718167A EP1856643A1 (en) | 2005-01-12 | 2006-01-12 | System and method for prediction of adverse events during treatment of psychological and neurological disorders |
JP2007550586A JP2008526388A (en) | 2005-01-12 | 2006-01-12 | System and method for predicting adverse events during treatment of mental and neurological disorders |
AU2006204963A AU2006204963B2 (en) | 2005-01-12 | 2006-01-12 | System and method for prediction of adverse events during treatment of psychological and neurological disorders |
CA2594456A CA2594456C (en) | 2005-01-12 | 2006-01-12 | System and method for prediction of adverse events during treatment of psychological and neurological disorders |
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US64335005P | 2005-01-12 | 2005-01-12 | |
US60/643,350 | 2005-01-12 |
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US (1) | US8005534B2 (en) |
EP (1) | EP1856643A1 (en) |
JP (1) | JP2008526388A (en) |
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BR (1) | BRPI0606306A2 (en) |
CA (1) | CA2594456C (en) |
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US11406316B2 (en) | 2018-02-14 | 2022-08-09 | Cerenion Oy | Apparatus and method for electroencephalographic measurement |
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JP2008526388A (en) | 2008-07-24 |
US8005534B2 (en) | 2011-08-23 |
AU2006204963A1 (en) | 2006-07-20 |
CA2594456A1 (en) | 2006-07-20 |
CA2594456C (en) | 2014-04-29 |
EP1856643A1 (en) | 2007-11-21 |
MX2007008439A (en) | 2007-09-21 |
AU2006204963B2 (en) | 2011-12-01 |
BRPI0606306A2 (en) | 2009-06-16 |
CN101529429A (en) | 2009-09-09 |
US20060167370A1 (en) | 2006-07-27 |
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