MXPA99009246A - Method for diagnosing psychiatric disorders - Google Patents

Abstract

A method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of:measuring the pattern of a subject's biophysical parameter and using said pattern to diagnose the psychiatric disorder. It has been found that certain clinical states are consistently associated with distincly different patterns of biophysical parameters. Normal and abnormal profiles are established, and subjects are tested by monitoring the pattern of a given parameter and comparing said pattern with reference patterns indicative of a psychiatric disorder. Heart rate is one biophysical parameter that can be readily and conveniently measured. Psychiatric disorders may be identified and diagnosed from analysis of characteristic patterns within portions of the circadian heart rate pattern. In one particular form, the heart rate pattern is measured over a period of at least approximately 90 minutes. The portion of the circadian heart rate pattern is preferably the sleep portion and in particular the sleep portion including the transition of the subject into and out of sleep. Preferably, the subject's heart rate is measured with a monitor that is unobtrusive and leaves the person freely ambulant. Heart rate patterns may be affected by a range of factors. Some factors may produce noise that may hamper the interpretation of the heart rate pattern. Comparison of the subject's heart rate pattern with the record of the subject's activities allows for the effects of noise in the subject's heart rate pattern to be negated.

Description

METHOD FOR DIAGNOSING PSYCHIATRIC DISORDERS DESCRIPTION OF THE INVENTION The present invention relates to a method for diagnosing psychiatric disorders by monitoring the pattern of the heart rate of a subject, and more particularly to a method for diagnosing psychiatric disorders by monitoring at least one portion of a pattern of the circadian heart rate of a subject. The present invention also provides a method to assess the effectiveness of treatments for psychiatric disorders. Despite intensive research for almost a century, there is still no reliable "laboratory test" for mental illness. The diagnoses are still made in a "clinical" manner based on the subjective experience (symptoms) and the observed behavior (signs). Given the difficulties in defining normal experience and behavior as well as the lack of any reliable objective indicators, it is not surprising that until now, all diagnostic / classification systems in psychiatry have been less than satisfactory for one reason or another. A reliable laboratory test would have enormous practical value in daily clinical practice and would greatly contribute to the advancement in theory and practice more generally.

It is suggested that up to now attempts to find "laboratory indicators" of mental illness have not been successful due to their conceptually misguided approach. Previous researchers have tended to look for some "fixed" chemical / anatomical lesion in the brain, in imitation of a neurological or neuropathological approach. If, however, there is no such "fixed" injury, but rather a functional disorder, (similar to a "tuning problem" in a car and a television set), then the neuropathological approach is bound to fail. The present invention seeks to provide a method for diagnosing psychiatric disorders or to provide at least one diagnostic method that can provide objective indications of clinical status and change and contributes to the diagnostic assessment of a subject. The present invention provides a method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of: measuring the pattern of the subject's heart rate and; use such a pattern to diagnose the psychiatric disorder. The present invention is based on the identification of a psychophysiological correlation between the cardiac regime and the psychiatric state. In this regard, it has been found that certain clinical states are consistently associated with distinctly different heart rate patterns. The heart rate pattern can be measured during a variety of periods. Thus, in some form, the heart rate pattern is a pattern of circadian heart rate, in that it is measured over a 24-hour period. While the circadian heart rate pattern in its entirety can be used in the method of the present invention, certain portions of the circadian heart rate pattern can also be used to diagnose psychiatric disorders. In this regard, psychiatric disorders can be identified and diagnosed from the analysis of characteristic patterns within the portions of the circadian cardiac rhythm pattern. Therefore, the present invention also provides a method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of: measuring at least a portion of the pattern of the circadian heart rate of the subject y; use such pattern or portion thereof to diagnose the psychiatric disorder. When the method comprises the measurement of a portion of the circadian heart rate pattern, the measured portion can be varied with the condition that it is capable of exhibiting a pattern in correlation with a psychiatric disorder. In a particular form, the heart rate pattern is measured over a period of at least about 90 minutes. The heart rate pattern of a subject while asleep and during the transition from the waking state to the sleep state and from the sleep state to the waking state may be particularly useful in the method of the present invention. Thus, when the method comprises the measurement of a portion of the circadian heart rate pattern, the portion of the circadian heart rate pattern is preferably the sleep portion and in particular the sleep portion which includes the subject's transition to sleep and outside of sleep. East. The heart rate pattern can be measured in a variety of ways. Preferably, the heart rate pattern is measured as beats per minute over time. Alternatively, the heart rate pattern can be measured as a difference diagram that reflects variations or fluctuations in the heart rate. When the heart rate pattern is a difference diagram, the preference difference diagram is a diagram of [heart rate (t + 1) - heart rate (t)], where t is the time in minutes, throughout weather. Of course, the heart rate pattern of a subject can be measured in a plurality of ways, and the plurality of forms can be used together to diagnose psychiatric disorders according to the method of the present invention. Thus, the present invention also provides a method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of: measuring the heart rate pattern of the subject in a plurality of ways, such as beats per minute over time and [heart rate (t + 1) -cardiac regimen (t)], where t is the time in minutes, in the course of time and; diagnose the psychiatric disorder. The method of the present invention can be computerized. In this regard, the pattern of a subject's heart rate can be measured and recorded in a way that allows it to be cross-checked with a database of reference heart rate patterns that indicate psychiatric disorders. Thus, the present invention also provides a method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of: measuring the heart rate pattern of the subject and; compare such pattern with at least one reference heart rate pattern indicating a psychiatric disorder where the reference heart pattern is provided in a computerized database.

The heart rate pattern of the subjects can be measured in a variety of ways. Preferably, the heart rate of the subjects is measured with a monitor that is not very striking and leaves the person wandering freely. When the method of the present invention comprises the use of reference heart rate patterns they can be varied and are preferably developed to collect data from a sufficient number of patients with psychiatric disorders to determine a typical pattern. When the biophysical parameter is a circadian heart rate pattern, the reference heart rate pattern can be selected from those illustrated in the examples and in particular those patterns illustrated in Figures 1 to 4. The present invention can be used to diagnose a variety of psychiatric disorders. For example, the method of the present invention can be used to diagnose a psychiatric disorder selected from the group comprising; General Anxiety Disorder (GAD), Panic Disorder (PD), Obsessive-Compulsive Disorder (OCD), Nonpsychotic Major Depression, Somatoform Disorder (of the hypochondriacal type), Delusory disorder (of the paranoid and somatic type) Attention Deficit Disorder (ADD) and Acute schizophreniform disorder.

Heart rate patterns can be affected by a variety of factors. Some factors may produce noise that may impede the interpretation of the heart rate pattern, which is obviously not desired. To help to account for, and therefore deny the effects of noise, the method of the present invention may further comprise recording the subject's activities over the course of time the subject is subject to the method. Therefore, the present invention provides a method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of: measuring the heart rate pattern of the subject; compare the heart rate pattern of the subject with a record of the subject's activities; and comparing the pattern with at least one pattern of the reference heart rate indicating a psychiatric disorder wherein the comparison of the pattern of the subject's heart rate with the recording of the subject's activities allows to deny the effects of the noise on the pattern of the regimen cardiac of the subject. Preferably, the recording of the activities of the subject comprises a daily newspaper that the subject performs by being subject to the method of the present invention. The present invention may also be useful for monitoring the effectiveness of a particular treatment administered to a subject suffering from a psychiatric disorder. Thus, the present invention also provides a method for evaluating the effectiveness of a treatment for a psychiatric disorder, the method comprising the steps of: measuring the heart rate pattern of the subject before and during treatment and; compare those patterns in terms of changes to determine the effectiveness of the treatment. In a particular form, the subject's heart rate pattern can be measured before, during and after treatment to evaluate the effectiveness of the treatment. Preferably, the treatment is a drug treatment in which the drug is administered to the subject. For example, drug treatment may comprise administration of a drug selected from the group comprising: benzodiazepines; antidepressants such as Selective Serotonin Collection Inhibitors (SSRI), Tricyclic Antidepressants (TCA) and Reversible Monoamine Inhibitor (RIMA); and sertraline. The present invention will now be described with reference to the following examples. The description of the examples does not limit in any way the generality of the above description.

Examples The data presented in examples 1 and 2 illustrate the relationship between the circadian pattern of the cardiac regimen and psychiatric disorders. The independent variable in the examples was an ACTIVE I-axis disorder DSM-IIIR ("IIR" - third revised edition of the diagnostic manual published by the American Psychiatric Association); the dependent variable 24MAHR. Efforts were made to control a number of influences that could cause confusion in the cardiac regimen. All subjects were given instructions as to how to keep the diary. The diary consisted of an individual card provided with schedules to note the potentially confusing influences that include: physical efforts, tea / coffee / alcohol / nicotine digestion and social interaction. Only certain diagnoses were studied. The objective was to select easily diagnosed states between normality and psychosis and the following were included: Generalized Anxiety Disorder (GAD), Panic Disorder, Obsessive-Compulsive Disorder (OCD), Non-psychotic Major Depression, Somatoform Disorder, (from hypochondriac type), Delusory disorder (paranoid and somatic type) and acute Schizophreniform disorder. Clinical subjects were selected from consecutive admissions to an adult psychiatric unit of a large teaching hospital. The subjects of normal control (without any history of psychiatric illness), were students, nurses, priests and medical personnel. The patients in the study were included initially if they met the DSM-IIIR criteria for one of the disorders of axis I listed above. Age was restricted from 18 to 65 years. The subjects were required to be physically healthy and were excluded if, after a thorough physical examination and relevant laboratory investigations, there was evidence of any physical disorder that could affect the heart rate. We also excluded subjects in whom any evidence of abusive alcohol or illicit drug use was found recently. Although efforts were made to select subjects who had not ingested any medication within two weeks of admission, those who had taken medication were not excluded if at the time of registration they did not show clear evidence of an active axis I disorder included in the study. The medical history was recorded in all cases and the inclusion of subjects who were free of medication with those who had or were taking medication at the time of registration, giving the opportunity to examine the effects of the drugs in each category of diagnosis.

Measurements were made of the average heart rate per minute for 24 hours, (24MAHR) with the use of a cardiac monitor that returned data comparable to those obtained using a conventional ECG monitor. The acquisition of data was not very striking and allowed people to wander freely. The number of serial records per subject varied from 2-10, with a rounded average of 3 per subject. The purpose of taking serial records was to examine the degree of variation between subjects in circadian activity, depending on changes in mental state. Normally, serial records were obtained every third day. The diagnostic revelation was made before each serial registration. As data were obtained for more than two years, it was possible to obtain serial records over relatively long periods in a percentage of subjects who were not readmitted during this period. Although diagrams of the heart rate versus time of day may reveal the qualitative aspect of circadian activity at first sight, it is difficult to quantify this temporal aspect in a numerical form and there are certain difficulties in creating mixed group data. There are a number of latent dangers if the data is simply averaged. There are various changes in the heart rate that depend on whether the person is awake or asleep (see Figure 1), and there is considerable variation in the sleep habits of different individuals, both in terms of the time they go to sleep and as to how long is the time during which they usually sleep. Therefore, if the group data are simply averaged, the resulting average will inevitably be confused by overlapping the segments of activity during sleep / wake between the subjects. Likewise, potentially relevant transient changes, such as a sudden rise or fall in the heart rate during periods of sleep and wakefulness, will tend to degrade or "get lost" when averaging. Therefore, averaging is not an appropriate method of reducing group data to compare patterns of circadian activity between different diagnoses. A comparison of the qualitative aspect requires a classification of the pattern of the individual records in terms of the particular "morphological" characteristics. This was the approach taken in the examples. The individual records of 24MAHR were superimposed on VDU and classified into different types of patterns, based on their circadian morphology. A frequency count was made after the pattern types found in each diagnosis and a Chi-square test was applied to see if any particular pattern predominated.

The findings are presented later. Example 1 describes the qualitatively different circadian patterns and illustrates how these data can provide clinically useful information. Example 2 shows the results of the analysis of the group data and includes an analysis of the effects of the drugs. EXAMPLE 1 24MAHR measurements provide a time history of two very different but complementary aspects of circadian activity. The first aspect, which is clearly evident in time diagrams of the net data, consists of the broad contours of the activity that are created by changes in the mean of the baseline around which the rates of beats per minute vary. The second aspect is revealed in a variability or difference diagram, [beat ratio (t + 1) pulse ratio (t), with t in minutes], and consists of the changing trends in the beat-per-minute variation that to a certain degree they are independent of the broad media contours. These two complementary aspects are illustrated in Figure 1, which show typical examples of three widely different circadian patterns. The patterns illustrated in Figure 1 were commonly found in subjects with General Anxiety Disorder (GAD), Nonpsychotic Depression (DEP) and Normal Subjects (ÑOR).

The diagrams of the first corresponding differential are shown on the right. The respective 24-hour scalar means [X] and [Xd], in beats per minute (BPM), are shown at the end of each diagram. The diagrams to the left show visibly obvious differences in the pattern of ample circadian architecture, particularly in the pattern of activity that extends during the sleep period. The sleep period is more clearly defined in the normal data. There is a rapid decline in the cardiac regime at the onset of sleep, an equally rapid rise upon awakening and a relatively flat pattern of low-level activity in the middle. In comparison, GAD data show a well-defined large elevation of the heart rate upon awakening, but there is no rapid decline that marks the onset of sleep. Instead, there is a progressive decrease from the waking regimes to the lower speeds, just before awakening. The opposite occurs in subjects who suffer from Depression (DEP). Normally, in these subjects the onset of sleep is marked by a relatively rapid decline in the cardiac regime that is followed by a fluctuation, but progressive elevation towards the levels of wakefulness, without a clearly defined transition from sleep to wakefulness. In GAD, cardiac regimens are relatively high at the onset of sleep and are at their lowest level just before awakening. In DEP the opposite is presented. With respect to the first differential diagrams to the right of Figure 1, it can be seen that the normal data show the lowest differential average of 3.4 BPM and the highest value of 7.0 BPM occurs in GAD. However, it should be noted that the 24-hour average (X) is not a reliable indicator of the amount or pattern of pulse variation per minute. That is, the changes in pulse rate variation per minute are to some degree independent of the broad contours of activity. In a very general way, it can be observed that these differential diagrams also reveal something of a circadian pattern, which is created by the variation in the amount of activity at different times of the day. Again, this architecture of circadian variation is very clearly defined in the normal data, which shows an evident reduction in the pulse variation at the beginning of sleep, followed by a reduced visible level of activity during the sleep interval and a return to sleep. previous levels of sleep upon awakening. In comparison, depression shows only a brief period of reduced activity at the onset of sleep, followed by a rapid return to pre-sleep activity, even when the average trend is still below the pre-sleep wakefulness values. GAD data show a discernible reduction in sleep activity similar to what is evident in normal data, but the reduction is less obvious and there is a much greater amount of activity during the sleep interval. More generally, and compared to normal, data for GAD and Depression show more peaks over the 24-hour period. The differences in activity that extend throughout the sleep period, both in the broad mean contours and in the amount of variation of pulses per minute, are considered particularly significant, since we would expect the least number of confusing influences during sleep . The obvious differences during this period are very likely to be particularly valid indicators of the genuine physiological differences between these states. Apart from the qualitative differences, the data in Figure 1 also show quantitative differences in the 24-hour averages indicated at the end of the diagram. Figures 2a and 2b show additional typical patterns associated with Depression (Figure 2a) and GAD (Figure 2b) and their corresponding 24 hour averages. It can be seen that the GAD and DEP patterns can be prolonged over a baseline counterpart scale and while the average of 24 hours is generally lower in the Depression than in the GAD, Figures 2a and 2b show that this is not always the case. It can be seen that the 24-hour average of 94 BPM for the upper diagram in Figure la is significantly higher than the 24-hour average of 76 BPM for the lower diagram in Figure 2b. This shows that the qualitative differences between these two patterns can not be explained simply by the quantitative differences in the 24-hour average. This does not mean that the quantitative differences within and between the particular patterns are not relevant and likewise the importance of such quantitative variation is discussed in more detail later in Example 2. Although the GAD and Depression states will usually reveal the respective signature patterns of circadian activity as shown in Figures 1 and 2, individual records can show a number of minor variations that provide potentially useful information about particular individuals. For example, a typical GAD pattern may show the following variations, as long as it still retains its signature outline. There may be variation in the baseline counterpart as shown in Figure 2. There may be variation in the gradient of downward activity during the sleep interval, as well as gradient and relative elevation of the heart rate upon awakening. There may be variation in the insomnia pattern, as indicated by the amount of peak activity at the waking levels during the sleep intervals. There may be a greater or lesser amount of variation of pulses per minute over the course of 24 hours or selected time intervals. Therefore, in the same way that two individuals with a clinical diagnosis out of doubt of GAD for example, may show some variation in the severity and number of clinical phenomena, so the 24-hour pattern shows some variation, but still retains its contour GAD signature. However, subjects may also exhibit mixed heart-rate patterns, with characteristics of both GAD and DEP, that appear analogous to the phenomena of mixed mental states that may be found in the clinical domain. These mixed patterns suggest a dynamic continuum of manifestations and the circadian pattern in individual cases may depend on the relative amount of activation in two widely different physiological trajectories. In this sense, it may be that the typical patterns for GAD and Depression shown in Figures 1 and 2 reflect "pure" GAD and "pure" Depression, respectively, while the mixed forms reflect activation of both GAD and Depression physiology. An example of such a mixed pattern ("MIX") is shown in Figure 3 together with a GAD pattern and a Depression pattern.AVE.

Such patterns were found less commonly in subjects diagnosed with Panic Disorder, GAD and Depression, and in this regard it is emphasized that these subjects did not receive a mixed diagnosis in the clinical assessment. However, his heart rate showed a circadian pattern that apparently enters or is among the most common typical patterns for GAD and Depression. The mixed pattern in Figure 3 has been placed between the additional examples of typical GAD and DEP patterns, to facilitate an appreciation of what is meant by mixed. It can be seen that the mixed pattern shows a progressive decline in activity in the sleep period that closely resembles the GAD pattern above it. However, the similarity ends at approximately 4.00 am. Subsequently, there is a progressive increase in the cardiac regime to the waking levels, which resembles the Depression pattern immediately below. This suggests a combined activation of Depression and GAD physiology and even if this interpretation requires modification, the physiological perspective relieved by these data can contribute with adjunct information practically useful in a variety of clinical and research applications. In all, seven widely different circadian patterns were identified and virtually all the data obtained could be broadly classified into one or the other of these patterns. Four of these seven patterns, namely Normal, GAD, Depression and Mixed, were already presented in the previous. The remaining three, shown in Figure 4, have been found to be the most common in patients with Panic Disorder (PAN), Obsessive-Compulsive Disorder or Delusory Disorder (HSR) and Acute Schizophreniform Psychosis (SCH). The PAN pattern is characterized by a flat pattern of activity for much of the 24-hour period, a relatively low 24-hour average and a relatively large amount of peak pulse variation. There is a discernible flat sleep period (from about midnight to 8 a.m.), defined by a small change in the descending baseline and a slight reduction in the amount of pulse variation per minute. The HSR pattern resembles the normal pattern, but differs in the consistently high rates of flat activity, both in the waking and sleep periods. In the example shown, the sleep interval is clearly defined by a precipitous drop in the heart rate at the onset of sleep, an equally precipitous rise upon awakening and a relatively flat pattern of activity throughout the sleep period, with regimens around of 80 BPM. Elevations of the sleep regime around, and beyond such levels, show a progressive disruption of the sleep architecture towards the largely disorganized pattern found in Schizophreniform Psychosis (SCH) states. All but two of all records obtained can be broadly classified into one or other of the seven patterns discussed above. Those who do not conform to one or the other of these patterns were classified as others (OTH). It is contemplated that additional classification patterns may be found by including more diagnostic states and by making more distinctive pattern distinctions that include differences in the variation of pulses per minute. Attention is drawn to the variation of pulses per minute to show how this perspective also provide clinically useful information. It was very evident that the same broad circadian pattern in different individuals, was of considerable variation in the amount of variation variation of pulses per minute. Serial records of all subjects were obtained. However, only the first record of each subject was used for comparisons of group data between the different diagnoses included in the study. Subsequent records were used to study changes among individuals and did not contribute to group data. The purpose of serial records was to observe whether a change in the subject's mental state, for example from GAD to normal, was associated with a change in the circadian pattern and if so, if the change recapitulated the most common group data pattern for those states. Such a state-dependent recapitulation among the individuals of the group data patterns for those states would support the proposed hypothesis that there is a systematic link between the psychiatric state and the circadian pattern of the cardiac regimen. EXAMPLE 2 The data presented in Example 2 show that, notwithstanding other influences, the patterns of the cardiac regime depend demonstrably on the mental or psychiatric state. When the mental state is altered, for example, from anxious to normal, the 24-hour activity pattern shows corresponding changes in the serial registers. An example of such state-dependent changes is shown in Figure 5. Data was obtained from an individual whose GAD symptoms were lessened with treatment. The initial data are shown on the left and the corresponding variability data on the right. The respective 24 hour averages have been added at the end of each diagram. It can be observed that the broad contours of activity change from a typical GAD pattern to a normal NOR pattern and that there are concomitant changes in the variation of pulses per minute.

In particular, there is a relative reduction in activity at the beginning of and during sleep. From a purely quantitative perspective, there is a reduction in the average of 24 hours (X) from 98 to 77, and in the differential mean Xd from 7.3 to 5.4. Taken together, these changes are intuitively consistent with someone who is increasingly less anxious. The advantage of these physiological adjuncts is that they can provide objective indications of clinical change. At this stage it is appropriate to make certain comments about the noise that causes confusion. It has been recognized that the heart rate is susceptible to a wide range of influences and the data presented here may be contaminated to some degree by the noise caused by variation in, build, age, sex, tea / coffee intake, motor activity , stimulation of the environment, etc. Keeping a journal can help control more obvious influences such as exercise, but a certain amount of noise pollution will inevitably remain. In this regard, it is found that while physical exercise and other unusual stimuli / exercises can undoubtedly produce confounding effects, these can be easily identified with the help of diary information. Minor and brief influences apparently do not exert a significant effect in terms of confusion in the broad contours of activity. Probably, this is due to the fact that these effects are brief and are distributed randomly during the waking period. The useful information for the diagnosis is revealed more in the broad average tendencies, which remain distinct in a distinctive way despite the high frequency noise superimposed. Likewise, a large number of effects of possible confusion do not appear during the sleep period and the pattern of activity during sleep is an important discriminatory characteristic. More generally, and barring unusual effects such as exercise, it seems to be the case that just as psychological phenomena and the like (for example, symptoms of anxiety and depression) dominate the mental state of normally different individuals, so too , the patterns that depend on the mental state of the circadian cardiac regime dominate in the physiological domain, in spite of the differences of age, complexion, etc. However, in the case of GAD for example, it has been found that a 20-year-old man with an athletic build and a 60-year-old woman who is definitely not in good physical condition will both show a similar GAD pattern, even if there are normally baseline differences.

Table 1 shows a summary of the group's data, in terms of the frequency with which particular patterns occur in different diagnoses. The number of subjects (N), the 24-hour mean of the group (X), and the 24-hour differential mean of the group (Xd), have been added for each diagnosis. It can be observed that while all diagnoses, including normal, are associated with more than one circadian pattern, certain diagnoses show a strong association with a particular pattern. Assuming that the seven identified patterns should be equally distributed in each diagnostic group, then the Chi-square tests show a significant predominance (with probabilities> 0.05 and 0.001) of a particular pattern in each of the diagnostic groups. In a very general way, there are indicators of a hierarchical grouping in these correlations. Therefore, normal subjects predominantly showed a ÑOR pattern, and of those who did not, all but two showed an anxiety pattern (GAD). However, no normal subject showed an HSR or SCH pattern, which predominated in the psychotic end of the clinical spectrum. Conversely, Acute Schizophreniform and Delusory Disorder does not show a ÑOR pattern. A similar hierarchical grouping is evident for anxiety subtypes of Panic disorder, Generalized Anxiety and Obsessive-Compulsive Disorder. No subject diagnosed with OCD showed a PAN pattern that predominated in Panic Disorder and no subject with a diagnosis of Panic Disorder showed an HSR pattern that predominated in OCD. However, a significant percentage of both subjects with Panic Disorder (27%) and OCD (36%) showed a GAD pattern that predominated in Generalized Anxiety Disorder. While large samples may show a wider overlap, the findings obtained here suggest that statistically, these subtypes of anxiety are associated with widely different circadian patterns, with OCD showing a pattern that predominates in the psychotic end of the clinical spectrum.

Table 1 also shows the variation in the mean of 24 hours (X) and the differential mean of 24 hours (Xd) between the different diagnoses. Taking the average of 75BPM for normal subjects as the reference, there are statistically significant elevations with p > 0.001 in: GAD, OCD, Delusory Disorder and Acute Schizophreniform Disorder. This shows that statistically, some diagnoses reveal circadian activity that differs from normal both qualitatively and quantitatively, while in others such as Depression, it differs qualitatively, but not quantitatively. However, individual records of any particular pattern may show quantitative variation in terms of "baseline counterpart," as illustrated by the DEP and GAD patterns in Figure 2. The clinical significance of such quantitative variation was not investigated in a manner systematic, but in the case of the GAD pattern for example, there is evidence for the probable explanation that the degree of counterpart of the baseline is related to the severity. Pattern 1 shows that the GAD pattern was found in normal subjects and all diagnoses other than Depression. However, the combined 24-hour mean of the GAD pattern in Normal, Somatoform and Panic Disorder states is significantly lower than it is for the OCD, Dilusive and Schizophreniform Acute states.

This indicates that the quantitative aspect is also important and it does not seem surprising that in the example of the GAD pattern, the highest means will be found at the psychotic end of the clinical spectrum. It is not known which regime of different baseline counterpart patterns can exist without undergoing a qualitative change and it may be that any particular pattern depends on the relative contribution and hierarchical progression of only a few axes of physiological activation (possibly only Anxiety). and Depression). Therefore, both the qualitative and quantitative aspects of the circadian cardiac regimen can provide potentially useful information. Compared with the initial scalar mean (X), less difference was found in the mean difference (Xd). However, compared to the 4.8 B value for normal subjects, there is no significant reduction (p> 0.05) in Depression, Delusory Disorder and Hypochondriacal Somatoform Disorder. Although not significantly compared with normal subjects, Panic Disorder shows the highest absolute average of 5.1 BPM while depression shows the lowest average of 3.6 BPM. The difference in this respect between Panic Disorder and depression is highly significant being p > 0.001.

The relationship between the subjects without drug intake and those who ingested them varied between the diagnoses as well as the type of medication. Due to such variation, the comparisons were limited to the larger N groups of Depression, GAD and Panic Disorder. Most of the subjects under medication in these three groups had taken or were taking benzodiazepine at the time of registration and some had been taking antidepressants. Surprisingly, no statistically significant differences were found in the type of pattern or means of 24 hours. One could expect at least an average of twenty-four hours less in the subjects with benzodiazepine intake. Possibly these subjects had higher cardiac regimens in the first place although the medication had its effect, it did not decrease the average to a significant degree. The provisional conclusion obtained from these findings is that unless the medication is effective to change the mental state, it does not significantly alter the circadian pattern and can not significantly decrease the average of 24 hours, even if transient effects are observed, especially with the benzodiazepines and the major sedative tranquilizers. This is illustrated in Figures 6a and 6b, which show the transient effects of benzodiazepine.

Figure 6a shows a typical GAD pattern obtained from a subject diagnosed with GAD. Figure 6b shows a typical pattern of DEP of a subject diagnosed with Depression. By chance, both subjects were taking diazepam when the first record was made. The diazepam Depression subject had been prescribed initially due to agitation, it can be observed that the diazepán resulted in a similar transient decrease of the cardiac regimen in both subjects. In none of the cases these transient effects apparently altered the broad circadian pattern to any significant degree and the average of 24 hours had only minimally diminished by the briefly lower regimens. It should be noted that after the brief fall, the heart rate returns to the baseline prior to the medication and even higher regimens after about 40 minutes. Apparently only when benzodiazepines have been effective in the treatment of generalized anxiety, they cause the broad contours of activity to be altered significantly (as illustrated in Figure 4). In contrast to benzodiazepines, antidepressants (including SSRs, TCAs, and RIMAs) do not show any visibly obvious transient effects, but they can lead to more profound changes when they are clinically effective. These illustrate in Figures 7a and 7b that they show significant changes in a DEP pattern, after three weeks of treatment with Sertraline and a clinical improvement no doubt. A comparison of Figures 7a and 7b shows that the presumed effect of sertraline treatment has been to normalize the circadian pattern to the level at which the sleep period resembles the pattern observed in normal subjects and there has been a general reduction in the amount of pulse variation per minute over a large part of the 24-hour period. The examples demonstrate that there are qualitatively different patterns of the circadian cardiac regimen that can not be reduced to merely quantitative variation in the 24-hour average. Evidence has been presented that shows that the qualitative aspect depends in an important way on the mental or psychiatric state and the predominance of the particular patterns in the widely different diagnoses, suggests that the circadian pattern is an indication of the wide physiological differences between these psychiatric states. While some states show a strong association with a particular pattern, in other patterns it is very variable. Conversely, given a record showing a particular pattern (eg GAD), clinical phenomena may vary from generalized anxiety, panic disorder, hypochondriacal somatoform disorder and even normal subjects may show such a pattern. It is likely that widely different circadian patterns, reflecting widely different states of physiological activation, may be associated with different mental state phenomena and in this sense, these data may contribute to a physiological dimension for clinical assessment. Therefore, the information provided by these data contributes significantly to the selection of the most effective medication, the evaluation of treatment and the selection of more homogeneous populations in the research. Examples 1 and 2 show that changes in mental state are associated with variation in both the qualitative and quantitative aspects of circadian activity, so that serial records can provide practically useful rates of clinical change. Patients can serve as their own control and changes in serial registers provide more reliable rates of clinical change than those obtained with subjective rating scales. EXAMPLE 3 The 24 hour heart rate was converted to a complex analytical signal from which the instantaneous frequency was calculated. East Example includes the study of the distribution of "instantaneous frequency" during 24 hours in different disorders and shows clear quantifiable differences between various disorders during the sleep period. This applies particularly to the depression that can be diagnosed very reliably with the measurements obtained from this method of analysis. The analysis focused on the modulation of the ULTRADIAN rhythm [cycle less than 24 hours or "circadian"] of the instantaneous frequency of the signals. The differences with greater discrimination are evident in the cycles around 90 minutes. This is illustrated below in Figures 8 and 9. Figure 8 shows individual examples and Figure 9 shows the comparisons of the group data between the normal state, of anxiety and depression. Figure 8 • in each pair of diagrams, the instantaneous frequency is shown in the upper part, the heart rate in the lower part. • the horizontal lines that go through the circles indicate the average frequency of 24 hours. • Circles emphasize activity during the sleep period. • the arrows are located at the beginning of the dream. The lower graph represents the profile of a normal subject. It can be seen that the instantaneous frequency in the normal subject shows a clear decline at the beginning of the dream and is then modulated by the 90-minute stable phase activity, which ceases its rhythmic fluctuations at the end of sleep. The intermediate graph represents the profile of a subject who suffers from anxiety. It also shows a fall around the onset of sleep, but there is less modulation of the stable phase during sleep. The upper graph represents the profile of a subject suffering from depression. There is a dramatic difference in this case because the lower rhythmic regimes occur before sleep and there is a dramatic increase in frequency from 3-4pm. The second circle before the black arrow is intended to focus on the low-frequency rhythmic activity that occurs earlier rather than after the onset of sleep in the same way as it does in the normal subject. The group data contained in Figure 9 show differences in the instantaneous frequency very clearly and the prominent rise in frequency around 3am in depression fits very well with the number of other findings of abnormal activity around this time.

The present invention includes within its scope adaptations and modifications obvious to the person skilled in the art.

Claims (25)

1. CLAIMS 1. A method for diagnosing a psychiatric disorder in a subject, the method is characterized in that it comprises the steps of: measuring the heart rate pattern of the subject and; use such a pattern to diagnose the psychiatric disorder.
2. 2. The method of compliance with the claim 1, characterized in that the heart rate pattern is a pattern of circadian heart rate.
3. 3. The method of compliance with the claim 2, characterized in that a portion of the pattern of circadian heart rate is used to diagnose the psychiatric disorder. .
4. The method according to claim 3, characterized in that the pattern portion of the circadian heart rate is measured for approximately 90 minutes.
5. The method according to claim 3 or 4, characterized in that the portion of the circadian heart rate pattern includes at least a portion of the heart rate pattern exhibited while the subject is asleep.
6. 6. The method according to any of claims 3 to 5, characterized in that the portion of the circadian heart rate pattern includes the portion of the heart rate pattern exhibited during the subject's transition to sleep or outside of sleep.
7. The method according to claim 2, characterized in that the whole pattern of circadian heart rate is used to diagnose the psychiatric disorder.
8. The method according to any of claims 1 to 7, characterized in that the heart rate pattern is measured as beats per minute over time.
9. 9. The method according to any of claims 1 to 7, characterized in that the heart rate pattern is measured as a difference diagram that reflects variations or fluctuations in the heart rate.
10. 10. The method of compliance with the claim 9, characterized in that the difference diagram is a diagram of [heart rate (t + 1) - heart rate (t)], where t is the time in minutes, over time.
11. The method according to any of claims 1 and 7, characterized in that the heart rate pattern is measured in a plurality of ways and the plurality of forms is used together to diagnose the psychiatric disorder.
12. The method according to claim 11, characterized in that the plurality of formats is beats per minute in the course of time and [heart rate (t + 1) - heart rate (t)], where t is the time in minutes, in the course.
13. The method according to any of claims 1 to 12, characterized in that the heart rate pattern of the subject is compared with at least one reference heart rate pattern indicating a psychiatric disorder.
14. 14. The method according to claim 13, characterized in that the reference heart pattern is provided in a computerized database.
15. 15. The method of compliance with claim 13 or 14, characterized in that the reference heart rate patterns are developed by collecting data from a sufficient number of patients against psychiatric disorders to determine a typical pattern.
16. 16. The method according to any of claims 13 to 15, characterized in that the reference heart rate pattern is selected from those illustrated in the examples and in particular those patterns illustrated in Figures 1 to 4.
17. 17. The method of according to any of claims 1 to 16, characterized in that the subject's heart rate is measured with a monitor that is not very striking and allows the person to be a traveling patient.
18. 18. The method according to any of the preceding claims characterized in that the psychiatric disorder is selected from the group comprising; General Anxiety Disorder (GAD), Panic Disorder (PD), Obsessive-Compulsive Disorder (OCD), Non-psychotic Major Depression, Somatoform Disorder (of the hypochondriacal type), Delusory Disorder (of the paranoid and somatic type) Deficit Disorder Attention (ADD) and Acute Schizophreniform Disorder.
19. 19. The method for diagnosing a psychiatric disorder in a subject, the method comprising the steps of: measuring the heart rate pattern of the subject; compare the heart rate pattern of the subject with a record of the subject's activities and; comparing the pattern with at least one reference heart rate pattern indicating a psychiatric disorder in which comparing the heart rate pattern of the subject with the recording of the subject's activities allows to negate the effects of noise on the heart rate pattern of the subject.
20. 20. The method of compliance with the claim 19, characterized in that the recording of the subject's activities comprises a daily newspaper carried by the subject when subjected to the method.
21. 21. A method to assess the effectiveness of a treatment for a psychiatric disorder, the method is characterized in that it comprises the steps of: measuring the heart rate pattern of the subject before and during the treatment and; compare the patterns of the changes to determine the effectiveness of the treatment.
22. 22. The method of compliance with the claim 21, characterized in that the pattern of the subject's heart rate is measured before, during and after treatment to assess the efficacy of the treatment.
23. 23. The method according to claim 21 or 22, characterized in that the treatment is a drug treatment.
24. 24. The method according to claim 23, characterized in that the drug treatment comprises the administration of a drug selected from the group comprising; benzodiazepines; anti-depressants such as Selective Seratonin Reuptake Inhibitors (SSRI), Tri-cyclic Antidepressants (TCA) and Reversible Monoamine Inhibitor (RIMA); and sertraline.
25. 25. A method for diagnosing a psychiatric disorder in a subject substantially as described herein with reference to the examples. SUMMARY A method for diagnosing a psychiatric disorder in a subject, the method comprises the steps of: measuring the pattern of a biophysical parameter of the subject and using such a pattern to diagnose the psychiatric pattern. It has been found that certain clinical states are consistently associated with distinctly different patterns of biophysical parameters. The normal and abnormal profiles are established and the subjects are tested by monitoring the pattern of a given parameter and comparing the parameter with the reference patterns indicative of a psychiatric disorder. The heart rate is a biophysical parameter that can be measured easily and conveniently. Psychiatric disorders can be identified and diagnosed from the analysis of characteristic patterns within portions of the circadian heart rate pattern. In a particular form, the heart rate pattern is measured over a period of at least about 90 minutes. The portion of the circadian heart rate pattern is preferably the sleep portion and in particular, the portion of sleep that includes the subject's transition to sleep and outside of sleep. Preferably, the subject's heart rate is measured with a monitor that is not very striking and leaves the person wandering freely. Heart rate patterns can be affected by a variety of factors. Some factors can produce noise that can impede the interpretation of the heart rate pattern. The interpretation of the heart rate pattern of the subject with the recording of the subject's activities allows to deny the effects of noise in the subject's heart rate pattern.