MX2007013110A

MX2007013110A - Methods and devices for relieving stress.

Info

Publication number: MX2007013110A
Application number: MX2007013110A
Authority: MX
Inventors: Michael Wood; Adam Forbes; Kirstin Rhys
Original assignee: Helicor Inc
Priority date: 2005-04-20
Filing date: 2006-04-20
Publication date: 2008-02-19
Also published as: CA2604741C; WO2006113900A8; WO2006113900A3; EP1874186A4; BRPI0610848A2; KR101303354B1; KR20080011294A; AU2006236216A1; JP2008536648A; CA2604741A1; WO2006113900A2; EP1874186A2

Abstract

Easy to use, cost-effective methods and devices for evaluating and treating stress and thereby disorders caused or exacerbated by stress are provided. More particularly methods and devices for identifying RSA waves during respiration which provide a subject with realtime RSA wave information are provided. These methods and devices also can be used to identify drop points in RSA waves. Such methods and devices provide subjects with the ability to maintain parasympathetic outflow and thereby prevent and/or reduce levels of stress.

Description

METHODS AND DEVICES TO RELIEVE STRESS Related Requests This application is a continuation in part of and claims the priority of and benefit from the US Application. No. 11 / 084,456, published March 18, 2005. This application also claims priority and benefit of the US Provisional Application. No. 60 / 673,148, published on April 20, 2005, the US Provisional Application. No. 60 / 673,627, published on April 21, 2005 and the US Provisional Application. No. 60 / 705,883, published on August 4, 2005. The contents of the applications referred to above are incorporated herein by reference in their totals. Field of the Invention The present invention relates to methods and devices for evaluating and treating stress and stress-related disorders. More particularly, the present invention relates to biofeedback devices and methods for increasing parasympathetic nerve activity by providing information about the patterns of synus respiratory arrhythmia. Background of the Invention Although there are many products and services to reduce stress, stress and stress-related disorders still result in exaggerated economic and non-economic costs. It has been estimated that in the United States alone, work stress represents about $ 300,000 million annually in terms of productivity, absenteeism and turnover. Above the costs related to direct work, attempts to treat stress and stress-related disorders presented more than $ 17,000 million in anti-depressant and anti-anxiety medication in 2002. The upward trend in the annual costs of these treatments continues. pharmacological In addition, stress results in significant but incalculable costs due to concomitant health problems that arise directly or indirectly from underlying stress disorders. For example, studies have shown that people who suffer from stress are more susceptible to viral and non-viral diseases. A common and well-known example of this is the relationship between stress and respiratory infections. In addition, those who suffer from an illness take longer to recover if they also suffer from stress. Chronic stress can impair both the autonomic nervous system balance (ANS) and the effectiveness of ANS, resulting in a large number of stress-related disorders. The damage to the SNA results in degenerative diseases and premature death. For example, a clinical study evaluated a two-minute measurement of ANS of 14,025 healthy men and women between the ages of 45 and 64. After eight years, those with a measurement of the parasympathetic minus had a higher incidence of illness and death. Three other studies (USA, Denmark, and Finland) also evaluated the role of the SNA as it relates to "all-cause mortality". In each study, the low SNA function of the parasympathetic SNA preceded and predicted disease and death. Literally hundreds of studies have evaluated the role of SNA as it is related to individual diseases such as heart disease, diabetes, and stroke. For example, the British government commissioned a study on the role of SNA and heart disease. Those with the lowest parasympathetic function had more than 1,000% increase in the death rate from heart attacks. The noneconomic costs of stress are also important and include harmful effects on relationships with family, friends, neighbors and co-workers. The stress response involves two basic systems: the autonomic nervous system and the endocrine system. SNA usually innervates the smooth muscles of internal organs and consists of sympathetic and parasympathetic divisions. In simple terms, the sympathetic division is responsible for mobilizing energy to respond to emergencies ("fight or flight"), express emotions or carry out arduous activities, while the parasympathetic division acts to exert a calm influence and in this way balance the sympathetic system. As the sympathetic nerves become more active, it increases the heart rate, blood pressure, respiration rate, mental activity (shaking the brain) and other bodily functions. Therefore, stress is maintained by high activity of the sympathetic nerves. The endocrine system is also involved in processes related to stress. In particular, the hypothalamic-pituitary adrenal axis (HPA) plays an important role in the stress response of the endocrine system. The hypothalamus secretes peptide hormones to stimulate the pituitary glands that in turn secrete their own hormones to stimulate other endocrine glands. The adrenal glands secrete Cortisol, which regulates metabolism and energy production and regulates responses in the sympathetic and parasympathetic branches of the autonomic nervous system. Cortisol levels are directly related to the degree of an individual's stress response. In the early 1970s, Dr. Herbert Benson documented the existence of a neurological and physiological state opposite to the "stress response". This state, called the "relaxation response" has been verified by other clinical investigators. From a perspective of the autonomic nervous system, the response to stress is characterized by a high activity of the sympathetic branch while the relaxation response is characterized by the high activity of the parasympathetic branch. The induction of the relaxation response by definition interrupts a stress-activated response. Therefore, frequent activation of the relaxation response can prevent stressors from generating a constant stress (i.e., chronic). In addition, it has been shown that frequent activation of the relaxation response reverses much of the damage, including hypertension, caused by previously encountered chronic stress. The interaction of the two branches of the autonomic nervous system (sympathetic and parasympathetic) can be characterized by examining the small changes in time between each consecutive heartbeat. When an individual is in a resting state, the variation in beat-to-beat time is caused by the parasympathetic branch. This variation will increase and decrease according to an individual's breathing pattern. During inspiration, the parasympathetic branch is inhibited, and the heart rate will begin to increase. During expiration, the parasympathetic branch takes and decreases the heart rate. This relationship between changing heart rate and breathing is called sinus respiratory arrhythmia (RSA). RSA measurements are mathematical calculations of the degree to which the heart rate rises and falls. When the rise and fall are greater, then the activity of the parasympathetic nervous system is also greater. In other words, a higher RSA indicates greater parasympathetic activity. As stated previously, a sufficient increase in parasympathetic activity changes the body to the relaxation response thus disrupting any pre-existing response. Many attempts have been made to activate the relaxation response to treat or control stress, including both invasive and non-invasive techniques and procedures. For example, acupuncture, prescription and non-prescription pharmacological treatments, and psychotherapy have all been used for the purpose of relieving or controlling stress. However, each of these therapies involves significant costs in money and time. In addition, the effectiveness of these treatments is often less complete and sometimes non-existent. Effectiveness is often difficult to evaluate and often only temporary. further, pharmacological treatments frequently have undesirable side effects and some may have risks of addiction. Likewise, even with all available alternatives, stress still represents (either directly or indirectly) more than 80% of the visits to doctors.

As a result, there is a clear need for methods and devices for evaluating and treating stress, wherein said methods and devices are effective, non-invasive, easy to use and inexpressive. In addition, there is a clear need to obtain methods and devices that do not have unwanted side effects or cause addiction risks. In particular, there is a clear need to obtain methods and devices that promote stress reduction by providing high levels of uninterrupted parasympathetic activity and that are capable of immediately stopping the response to stress. Brief Description of the Invention The present invention provides cost-effective, easy-to-use methods and devices for evaluating and treating stress, and therefore disorders caused and exacerbated by stress. More particularly, the present invention provides methods and devices for identifying individual RSA waves and providing a subject with RSA wave information. This information can be used, for example, in biofeedback adjustment to help subjects reduce stress levels and achieve rhythmic breathing. The present invention also provides methods and devices that allow the immediate cessation of the response to physiological stress that prevents the stress response from damaging the body and the mind. The regular use of methods and devices in accordance with the present invention allows the physiological damage caused by prior exposure to stress to be reversed, including the cumulative effects of chronic stress. Also, an exemplary embodiment of the present invention provides portable biofeedback devices to prevent, reduce or eliminate stress in human subjects. Another exemplary embodiment of the present invention provides methods and devices for maintaining a substantially continuous state of high parasympathetic activity over a prolonged period of time. Another embodiment of the present invention provides portable biofeedback devices that contain a photoplethysmography ("PPG") sensor and a display screen for providing subjects with information on their RSA waves. A further exemplary embodiment of the present invention provides methods and devices for training subjects to reduce stress levels by reaching a respiratory rate of about 6 breaths per minute. Another embodiment of the present invention provides methods and devices that promote stress reduction by providing high levels of uninterrupted parasympathetic activity along with real-time feedback of said activity.

Another embodiment of the present invention provides methods and devices that give the user information about the transition in the waves of the RSA from ascending points to descending points where said information can be used to guide the user's breathing. A further embodiment of the present invention provides methods for detecting and correcting erroneous data related to RSA waves and devices using said methods. Another exemplary embodiment of the present invention provides methods for adjusting scales on a display screen of biofeedback devices and devices using such methods. Yet another embodiment of the present invention identifies breathing patterns including depth, frequency and volume when analyzing waves of the RSA and provides a visualization thereof. Brief Description of the Figures Figure 1 illustrates a pattern of heart rate variability (HRV) caused by sinus respiratory arrhythmia (RSA). Figure 2 illustrates an exemplary series of RSA waves and identifies several pulse peaks. Figure 3 illustrates a series of RSA waves and calculates the interval times between beats (IBI) between successive pulse peaks.

Figures 4a-d identify, respectively, a representative upper point, lower point, point of upward transition and downward transition point. Figure 5 illustrates representative consecutive ascending and descending transition points. Figure 6 illustrates an exemplary method for identifying a top point. Figure 7 illustrates an exemplary method for identifying a lower point. Figures 8 (a) - (b) illustrate an exemplary process flow for an exemplary procedure for finding RSA waves within a data group in accordance with an embodiment of the present invention; Figure 9 illustrates an exemplary procedure for identifying RSA waves within a data group. Figure 10 illustrates an exemplary double top wave. Figure 11 illustrates an exemplary method for correcting data from a representative double superior wave. Figure 12 illustrates an exemplary display of a stress meter. Figure 13 illustrates an exemplary method for determining a long-term direction of RSA waves. Figure 14 illustrates an exemplary process flow for an exemplary procedure for determining the wave phase. Figure 15 illustrates an exemplary process flow for determining the wave side. Figures 16 (a) - (b) illustrate exemplary methods for determining the conclusion of a wave. Figure 17 illustrates an exemplary method for delineating wave boundaries. Figure 18 illustrates an exemplary method for evaluating the continuity of parasympathetic activity. Figure 19 illustrates an exemplary method for assessing the continuity of parasympathetic activity. Figure 20 illustrates an exemplary embodiment of a device in accordance with the present invention and identifies a potential location for a power switch.

Figure 21 illustrates a representative location for a PPG sensor that can collect data from a subject's finger. Figures 22 (a) - (b) illustrate alternative methods for a subject to hold an exemplary device while the sweat finger is on the PPG sensor. Figure 23 illustrates an exemplary display of a countdown meter. Figure 24 illustrates an exemplary display of a representative average pulse rate as well as a pulse frequency over time. Figure 25 illustrates an exemplary visualization of an error message.

Figure 26 illustrates an exemplary mode of a countdown meter. Figure 27 provides a representative illustration of RSA waves of a subject whose respiration has moderated over time. Figure 28 provides a representative illustration of RSA waves of a subject who has taken deeper breaths over time. Figure 29 illustrates a representative RSA pattern consistent with rhythmic breathing. Figure 30 provides a representative display of a subject with a wave frequency of six. Figure 31 provides another representative display of a subject with a wave frequency of six. Figure 32 illustrates a representative display of the RSA wave histories of a subject. Figure 33 illustrates a representative display of a subject whose depth of breathing has increased and has generated relatively large waves with a duration of about 10 seconds each. Figure 34 illustrates a representative location of a guided breathing switch to activate a guided breathing function in exemplary devices of the present invention. Figures 35 (a) - (b) illustrate an exemplary display for guided breathing with a breathing bar that increases to guide inhalation and decreases to guide exhalation. Figure 36 illustrates an exemplary display of a session summary screen. Figure 37 illustrates an exemplary display of various types of RSA information that can be displayed by representative devices in accordance with the present invention. Figure 38 illustrates an alternative form factor for exemplary devices of the present invention. Figures 39 (a) - (b) illustrate, respectively, a screen that is large enough to display both the precise data and the erroneous data and a screen of a portable, small device in which only erroneous data can be discerned. Figure 40 illustrates a series of representative pulse peaks. Figures 41 (a) - (b) illustrate, respectively, a representative false positive pulse peak and a representative negative pulse peak. Figure 42 shows an exemplary process flow for an exemplary error correction method employed during a representative error correction mode. Figure 43 illustrates representative wave characteristics that can be used to determine when a subject has achieved rhythmic breathing. Figure 44 illustrates an exemplary system in which a program process according to an exemplary embodiment of the present invention can be implemented. Figures 45-46 show an exemplary process flow for an exemplary top-level method for interacting with a user in accordance with an exemplary embodiment of the present invention. Figures 47-51 illustrate an exemplary process flow for an exemplary method for processing a detected pulse in accordance with an exemplary embodiment of the present invention. Figures 52-54 illustrate an exemplary process flow for an exemplary method for error correction of a sequence of pulses detected in accordance with an exemplary embodiment of the present invention. Figures 55-56 illustrate an exemplary process flow for an exemplary method for error detection of a sequence of pulses detected in accordance with an exemplary embodiment of the present invention. FIG. 57 shows an exemplary process flow for an exemplary method for initializing a scale of pulses detected in accordance with an exemplary embodiment of the present invention.

Figures 58-59 show an exemplary process flow for an exemplary method for processing RSA waves within a sequence of pulses detected in accordance with an exemplary embodiment of the present invention. Figures 60-62 illustrate an exemplary process flow for an exemplary method for processing RSA wavelengths within a sequence of detected pulses to determine a stress level for a user in accordance with an exemplary embodiment of the present invention. invention. Figure 63 illustrates an exemplary process flow for an exemplary method for assigning RSA wavelengths in accordance with an exemplary embodiment of the present invention. Figures 64-74 illustrate an exemplary process flow for an exemplary procedure for determining the RSA wave phase in real time, using phase changes to detect the point of fall, using the phase changes to detect the conclusion of a wave, and determine the parasympathetic intensity of the newly formed wave. Figures 75-83 illustrate another exemplary process flow for an exemplary procedure for determining the wave phase and delineating the waves on a pulse-by-pulse basis. Figures 84-87 show an exemplary process flow for an exemplary procedure to determine both the drop point and the conclusion of a wave in real time. Detailed Description of the Invention Studies have shown that controlled breathing can change the balance between the sympathetic and parasympathetic branches. Three specific breathing components interactively determine the amounts of parasympathetic innervation. These three components include frequency, tidal volume, and expiration / inspiration index. In general, parasympathetic activity may increase by reducing the respiratory rate, increasing the volume of the tide, and / or increasing the expiration / inspiration index. Therefore, altering these three variables has the potential to increase parasympathetic activity enough to effectively obtain the relaxation response in a non-invasive, simple, economical manner and without negative side effects. In general terms, biofeedback methods and devices involve training processes that allow subjects to facilitate changes in behavior or activity in order to improve or maintain one or more physiological functions. Over time, a subject can be trained with methods and devices to have more control over these functions. In contrast to other forms of therapy in which the treatment is imposed on the subject, biofeedback methods and devices allow the subject to integrate training processes and almost automatic responses. The present invention relates to methods and devices that can provide biofeedback and training information for subjects suffering from stress and stress-related disorders. Such biofeedback and training information can be based on an analysis of respiratory sinus patterns and respiration that can affect these patterns. Methods for identifying individual RSA waves during spontaneous respiration using only the RSA data set are not known. In order to correlate the RSA waves with respiration, normally the heart rate information and the breathing frequency are collected and plotted on a separate plane. One aspect of the present invention includes the identification of the individual waves within a group of RSA data. Other aspects of the present invention include the use of RSA wave patterns to provide subjects with real-time respiratory feedback information based on heart rate data. The means to reduce or adequately control stress levels also they are provided based on the wave pattern analysis and the breathing feedback. Furthermore, there are no known methods for identifying individual RSA waves in real time during spontaneous breathing using only the RSA data set. A further aspect of the present invention allows such identification in real time and uses this information to promote the production of high levels of uninterrupted parasympathetic activity. Exemplary wave pattern identification methods In an exemplary embodiment of the present inventionThe identification and analysis of wave patterns of sinus respiratory arrhythmia begins by measuring the pulse rate of a subject on a beat-by-beat basis. In the medical literature it has been established that the human heart rates, and therefore the pulse frequencies, continuously fluctuate from top to bottom in a wave-like manner (Fig. 1). These waves are known as heart rate variability waves (HRV). When a person is physically immobile and at rest, the waves of HRV waves are related to a person's breathing. These resting HRV waves are medically known as sinus respiratory arrhythmia or RSA waves, since the size and shape of these waves are related to the frequency, rhythm and depth of a person's breathing. As long as a person breathes between 4 and 15 breaths per minute, the frequency of the waves will essentially be equated with the frequency of breathing. Most individuals breathe within this scale, but even when a person is breathing outside of this scale, the wave frequency still provides a close approximation to the frequency of breathing. Although the correlation between waves and respiration has been widely established in the medical literature by visual analysis, there is no automated method to identify individual waves within a group of heartbeat data. An exemplary embodiment of the present invention includes a novel method for identifying each individual wave for a group of heartbeat data. For example, the amount of time (in milliseconds) between two consecutive pulse peaks (the peak-to-peak time) is known as the pp interval (pp) (Fig. 2). In an exemplary embodiment of the present invention, a device records the successive pp intervals. The description of the pp interval also applies at intervals of rr (the interval between consecutive R waves on an electrocardiogram or ECG), any derivative of pp intervals such as pulse frequency points, and any derivative of rr intervals such as rhythm cardiac. Collectively, these intervals can be referred to as "intervals related to heart rate". In addition, the same method for extracting RSA waves from the pp ranges can be applied directly to these other points as well. However, certain preferred embodiments of the present invention analyze waves within groups of pp interval data. The pulse frequency of each registered pp interval (60,000 / pp) can be displayed on the screen each time a new pulse peak is found. The absolute time difference between successive pp intervals (absolute (pp [n] -pp [n-l])) is called the interval time between beats (IBI) (Fig. 3). One aspect of the present invention uses the times of the interval pp to identify individual waves of RSA. The methods described herein can be used for both spontaneous breathing and guided breathing. Each p-p can be cataloged by evaluating its relation to the p-p immediately before (the previous pp) and the p-p immediately after (the next p-p). A p-p can be considered an upper point (tp) if the previous p-p is equal to or smaller than it and the next p-p is also equal to or smaller than it (Fig. 4a). A p-p can be considered a lower point (bp) if the previous p-p is equal to or greater than it and the next p-p is also equal to or greater than it (Fig. 4b). A p-p can be considered an ascending transition point (at) if the previous p-p is less than it and if the next p-p is greater than it (Fig. 4c). A p-p can be considered a downward transition point (dt) if the previous p-p is greater than it and if the next p-p is smaller than it (Fig. 4d). Therefore, a p-p can be categorized as an upper point (tp), a lower point (bp), an ascending transition point (at), or a descending transition point (dt). The term "transition point" can be used to refer to both the ascending transition points and the descending points when it is not qualified with the words "ascending" or "descending". Consecutive transition points refer to a series of consecutive ascending transition points or descending transition points (Fig. 5). The term "upper level" can be used to refer to the relative height of the upper point. The level of a higher point can be computed as follows. L = the number of consecutive points immediately to the left of the top point that are less than or equal to the top point. R = the number of consecutive points immediately to the right of the top point that are less than or equal to the top point. Yes L <; R, then the upper level is equal to L, otherwise the level of the upper part is equal to R. Figure 6 illustrates, using three examples, how the upper point level can be cataloged. The term "lower level" can be used to refer to the relative height of the lower point. The level of the lower point can be computed as follows. L = the number of consecutive points immediately to the left of the lowest point that are greater than or equal to the lowest point. R = the number of consecutive points immediately to the right of the bottom point that are greater than or equal to the bottom point. If L < R then the lower level is equal to L, otherwise the level of the lower part is equal to R. Figure 7 illustrates, using three examples, how the lower point level can be cataloged. Figures 8 (a) - (b) provide an exemplary flow chart illustrating an exemplary procedure for finding the RSA waves within a group of data while Figure 9 illustrates how this procedure can be applied. In an exemplary embodiment of the present invention, the first step is to locate the highest number of consecutive transition points (ctp) in the data group. In Figure 9 the highest number of consecutive transition points begins at point 1. There are 2 consecutive transition points. The wave depth is equal to the number of these transition points. Therefore, the wave depth in this example is 2. In preferred modes, if the depth of the wave is greater than 4, the value of The depth of the wave is adjusted to 4. The next step is to locate the lower point to the right of the consecutive transition points where the lower level is equal to or greater than the depth of the wave. This is the right valley point (v2) of the RSA wave. In the example in figure 9, bottom point no. 8 has a level of 3, which is greater than the depth of the wave. The next step is to locate the lower point to the right of the consecutive transition points where the lower level is equal to or greater than the depth of the wave. This is the left valley point (vl) of the RSA wave. In the example provided in figure 9, the bottom dot no. 0 has a level 4, which is greater than the depth of the wave. The next step is to find the highest point between the left valley point and the right valley point. This is the peak (p) of the RSA wave. In the example in Figure 9, point 6 is the highest point between the two valley points. All the information from the left valley point (vl) to the right valley point (v2) is considered as processed information. The same procedure is repeated in the remaining raw data until all possible waves have been identified. There are numerous variations in the method described above that should be considered within the scope of the present invention. For example, a similar method can be used to find peaks on each side of a series of transition points. The valley between two peak points will therefore be the lowest point between the two peaks. In addition, the depth of the wave may be based on the number of absolute transition points or a number derived based on the number of transition points (e.g., number of transition points x 75%). In addition, point vl could be identified before point v2.

In preferred embodiments, the syntactic analysis method of the wave discussed above is used each time a new lower level point 4 is identified. Therefore, devices in accordance with exemplary embodiments of the present invention "search" for RSA between the points of the lower level 4. In another exemplary mode, the devices can be configured to "search" for RSA waves after each point, or after a certain period of time (every 30 seconds for example), etc. Exemplary modes use lower level 4 points since they have a very high probability of delineating RSA waves. That is, they have a high probability of being valley points (vl, v2) of RSA waves. There are two cases in which the basic RSA waveform analysis methods described above can accurately describe an RSA wave. One can occur when a double superior wave is encountered. Double upper waves can form when a person waits a long time to inhale after they have exhaled. Another may occur when double lower waves are formed. Double lower waves can be formed when a person holds their breath for a period before after inhaling. Double superiors are easily identified by examining the length indices of the two waves (Fig. 10). When (pl-v2) is much smaller than (pi - vl), and (p2 - v2) is much smaller than (p2 - v3), and (pi -v2) is very close to (p2 - v3) then a superior double. In preferred embodiments, double superiors can be defined as situations in which: ((pi - v2) / (pi - v2)) < 0.50 and ((p2 - v2) / (p2 - v3)) < 0.50 y ((pi - vl) / (p2 - v3)) > 0.75. The lower double can be defined as the inverse of double superiors. When double double or double superiors are produced from the basic parsing method, the two waves that form the pattern can be joined together in a single wave. Point vl is the vl of the new wave. Point v3 becomes v2 of the new wave. The highest value between vl and v3 is the peak point of the new wave. This is illustrated by figure 1.1. Exemplary embodiments of the present invention may use the RSA wave information described above to assess the level of mental stress of the user. This measurement of stress mental stress can occur in devices such as a stress meter (Figure 12 (5)). For example, when a person is stressed, breathing normally becomes rapid and irregular, in relation to a non-stress state. This rapid, irregular breathing can cause the formation of short and irregular RSA waves. The methods and devices in accordance with the present invention can be used to determine a user's level of being by determining how far the user's average wavelengths deviate from a level representing a relaxed state. These methods and devices can also compute how irregular (arrhythmic) the user's waves are. These two evaluations can be used individually or combined in a single value to indicate the level of general stress. Studies have shown that when people relax deeply (such as in the state of deep meditation), they tend to breathe at a constant rate of about 6 breaths per minute. This rhythmic breathing causes the wavelengths of RSA to drag on the breathing frequency. Therefore, rhythmic breathing of 6 breaths per minute will result in a series of RSA waves having wavelengths of 10 seconds. Therefore, the exemplary embodiments of the present invention use wavelengths of 10 seconds as the relaxation threshold when evaluating the user's level of stress. Exemplary modes also include methods and devices that compute the average wavelength of the last five waves to determine how far the average is, proportionally, 10 seconds. This is an example of a "wavelength score". Arrhythmic waves can be quantified using a number of standard variance formulas. Exemplary embodiments of the present invention use the sum of the differences of each consecutive wavelength in the last five waves to compute a "variance score". Exemplary modalities can also use the sum of the differences between the successive wavelengths and can use a weighted category order so that the variance of the most recent waves counts more. The level of stress in an exemplary embodiment of the present invention uses 70% of the "wavelength score" + 30% of the "variance score". The user's stress level can be recalculated each time a new RSA wave is identified. Stress can cause a variety of RSA wave behaviors: decrease in peak-to-peak times, increase in peak-to-peak frequency, decrease in wavelength, increase in wave frequency, decrease in amplitude, lengths of Irregular wave, irregular wave frequencies, irregular amplitudes, irregular peak-to-peak time, irregular peak-to-peak frequencies, irregular peak placements or decreased variation. Any of the above variables, or combinations thereof, can be applied to RSA waves and used as an indicator of stress level. The identification of individual RSA waves and the use of any of the above variables alone, in combination with each other, and / or in combination with other variables, to assess stress are within the scope of the present invention and have not been described in the prior art. In addition to using the identified RSA waves to determine stress levels, devices and methods in accordance with the exemplary embodiments of the present invention can also use the RSA wave information to determine and display both the average heart rate and the wave frequency. The average of all pulse frequencies in the last wave can be used to estimate the average heart rate. For example, each time a new wave of RSA is identified, the average pulse rate can be computed and the heart rate can be updated. The wave frequency display can also be updated each time a new RSA wave is identified. Exemplary modes can express the relative frequency to waves (breaths) per minute. In exemplary modalities, the wave frequency and heart rate can be rounded to the nearest inte Exemplary methods of real-time wave pattern identification The present invention also provides methods of identifying RSA wave patterns in real time. In certain modalities said methods involve two processes directed by primary switch.

The first process can be activated each time a new pulse is detected by means of a PPG sensor. This process can provide (1) convert the received pulses to pulse rate values (prv); (2) update the wave display with the new prv; (3) confirm if a new prv marks the beginning of a new wave (indicating that the previous wave has already been completed); (4) delineate the boundaries of the last wave (identifying valley-peak-valley points); (5) evaluate the parasympathetic activity of the wave; (6) display a suitable symbol under the wave; (7) update the wave history; and (8) update the score. A second process is responsible for detecting and marking the drop points in real time. This process can be directed by a clock interruption. In preferred embodiments, this process may occur, for example, every 250 milliseconds. When the process detects the occurrence of a fall point, it can be marked with a drop point indicator, such as a triangle, for example. Any of these processes can be implemented using standard polling methods. Alternatively, the second process can occur each time a pulse is detected. Exemplary modes use clock interrupts so the drop point is more easily detected. Reasonable results can be provided, however, by marking the drop point based on the pulse beats received.

Preferred embodiments of the present invention also provide various methods for the accurate characterization of RSA wave patterns in real time. Such methods include those to which they can conveniently be referred to as "wave phase" methods and "lateral wave" methods. Methods for determining the wave phase The present invention also provides methods for determining the wave phase and devices using said methods. In exemplary embodiments of the present invention, each time a new pulse comes, a long-term wave direction can be estimated. This process is illustrated in Figure 13. For example, the slope of the last six (6) pulse frequency points can be used. The resulting value can, for example, be called the "long slope". Alternatively, the slope of a point window based on times, for example, can be used (such as, for example, the last 12 seconds, the last 5 seconds, etc.), or, for example, another indicator can be used general address Then, for example, the absolute amount of the long-term change can be computed. This provides a degree of exchange value. In exemplary embodiments of the present invention, the absolute value of the long slope may be used, which may be referred to, for example, as the "absolute long slope". Alternatively, for example, the absolute value or similar conversion of any of the long-term wave evaluations may be used. Then, for example, the short-term direction of the wave can be determined. In exemplary embodiments of the present invention, for example, the slope of the last three (3) pulse frequency points may be used. This can be called, for example, the "short slope". Alternatively, for example, any directional indicator may be used on a smaller subset of points than the points selected for the long-term direction evaluation. Then, for example, the short-term direction indicator and the absolute long-term indicator can be used to estimate the actual direction of the wave itself. In exemplary embodiments of the present invention the short slope can be compared with the absolute long slope, for example. If the short slope is greater than, say, 30% of the absolute long slope, then the direction of the wave can, for example, be considered as ABOVE. If the short slope is less than (-1) * 30% of the absolute long slope, then for example, the direction can be considered as DOWN. Yes, both tests fail, then the address can, for example, be considered as FLAT. In alternative exemplary embodiments of the present invention, a different percentage may be selected for these determinations. The percentages can be based on the degree of sensitivity of the desired parasympathetic. The higher percentages may be less sensitive to interruption of the parasympathetic, while the lower percentages may be more sensitive. Around 30% is generally sensitive enough to detect major interruptions although quite indulgent to put the behavior within the control of the user. In addition, in the exemplary embodiments of the present invention, the percentage to determine a UP address may be different from the percentage to determine a DOWN address. Alternatively, other mathematical comparisons of the short slope to the absolute long slope can be used, for example, to determine the relative relationship between two and thus determine the direction of the wave. Alternatively, other mathematical functions can be used instead of the short slope and the absolute long slope to estimate, for example, the short-term direction and the long-term degree of the change. Then, the direction of the wave and the long-term direction can be used, for example, to determine the phase of the wave. In exemplary embodiments of the present invention the direction of the wave and the long slope can be evaluated in order to make an estimate, as illustrated, for example, in the flow diagram of the exemplary process of Figure 14. The process flow begins at 301, where the query "Is the long slope positive?" is evaluated. If, for example, in 301 the long slope is positive, the flow of the process moves to 310, where the query "Is the address is for arriba?" Is evaluated. If in 310 the address is UP, then the process flow advances to 311 and the phase is determined as in INCREASE. If, for example, at 301 the long slope is positive, but at 310 the address is not UP, then the process flow advances to 312 and the phase is determined as at ENCRESTA. If, for example, in 301 the long slope is negative (ie, a "No" is returned in 301 to the query "Is the long slope positive?") Which moves the process flow to 320, and then to 320 a "Yes" is given to the query "Is the address up?", then the process flow advances to 330. If at 330 the address is DOWN, then the process flow is moved to 331 and the phase is determined as in FALL. Alternatively, if in 320 the long slope is negative, but in 330 the direction is not DOWN, then the process flow advances to 332 and the phase is determined as in DEPRESSION. In each of 312, 311, 332 and 331 the result goes to 350, where the phase as determined can be returned to another process for processing or additional result.

Methods for determining the wave side The present invention also provides methods for determining the wave side and devices using said methods. Therefore, in the exemplary embodiments of the present invention an alternative can be implemented to determine the four phases of the wave. This alternative method can, for example, only detect the phases of INCREASE and FALL of a wave using a combination of scale and direction. An exemplary process flow for this method is illustrated in Figure 15. The process flow begins at 401, where the values of "High" - highest prv at a given interval, "Low" - lowest prv at a certain interval, and "Scale" - High - Low, for example. "High", "Low" and "Scale" refers to evaluating the prv values in a sliding window (such as, for example, the last 12 points, the last 12 seconds, etc.). In exemplary embodiments of the present invention, the prv scale of the last 12 seconds can be used. At 410 the address can be evaluated, as described above. Once the Scale and Direction are computed, the Phase can be estimated. This is done by observing the direction of the wave and the current prv in relation to the scale. If the direction of the wave is up at the bottom of the scale, then the phase of the wave has changed to increasing. Alternatively, if the direction of the wave is down at the top of the scale then the phase of the wave has changed to descending. The fact that a wave is at the top or bottom of its scale can be determined, for example, by choosing a fraction or percentage of the total scale, as shown in Figure 15, where to be within 25% The top of the scale is considered to be near the top, and being no more than 25% above the bottom of the scale is considered near the bottom. In alternative exemplary embodiments, other threshold values may be used. With reference to Figure 15, for example, at 420 the current prv can be tested by being at the top 25% of the Scale. If at 420 the last point is near the top of the scale, Le., "Yes" at 420, then the process flow can advance to 430 where the wave direction is analyzed. If at 430 the wave direction is DOWN, then the process flow moves to 431 and it is determined that the wave phase has changed to FALL, and the process goes to 460.

However, if at 420 the current prv is not at the top of 25% of the scale, then the process flow advances to 440. At 440 the wave is tested by being at the bottom of 25% of the scale. If the answer is Yes, the process flow moves to 450, where, for example, the wave direction can be estimated. If, for example, at 450 the wave direction is UP, then the process flow moves to 451 where it is determined that the wave phase changed to INCREASE, and the process goes to 460. If at 420 the wave does not it is at the top of 25% of its scale, and at 440 the wave is not at the bottom of 25% of its scale, then the process flow moves to 460 and it leaves the process. The process is also evacuated if at 450 the wave direction is not UP or if at 430 the wave direction is not DOWN. Therefore, in exemplary embodiments of the present invention, any of the methods, Wave Phase (Fig. 15) or Wave Side (Fig. 14), can be used to determine the current phase. In many contexts the Wave Side, for example, can be used as much as the use of the scale adds a degree of precision. However, in other modalities in which it is desirable to track all four phases, the Wave Phase, for example, can be used taking into account that it also identifies DEPRESSION and ENCRYPT. Methods for determining the conclusion of the wave The present invention also provides methods for determining the conclusion of the wave and the devices using said methods. In exemplary embodiments of the present invention, to determine when a new wave has concluded, the current phase can be traced on a beat-to-beat basis, using, for example, the phase determination method as described above. When the current phase changes to INCREASE, it is known that a wave has recently ended, as shown, for example, in Figure 16a. Alternatively, for example, the side of the wave can be traced on a beat-by-beat basis. When the wave side changes to LEFT, then it is known that a new wave has been completed, as shown, for example, in Figure 16b. Methods for delineating the boundaries of waves The present invention also provides methods for delineating the boundaries of waves and devices using such methods. Therefore, once it has been determined that a new wave has been completed, the points between the beginning of the previous wave and the point at which the new wave is rising, as shown, for example, in the figure, can be obtained. 17. The lowest point in the previous wave can be referred to as the left valley point. The lowest point between the left valley and the new rising point can be referred to as the right valley point. The highest point between the left valley point and the right valley point can be referred to as the peak. Alternatively, a lateral analysis of the wave can be performed using, for example, the points on the right side of the previous wave to the end of the right side of the newly formed wave. The lowest point on the right side of the previous wave can be referred to as the left valley point. The lowest point between the left valley point and the right side of the new waves can be called the right valley point. The highest point between the left valley point and the right valley point can be referred to as the peak. Methods for evaluating parasympathetic activity The present invention also provides methods for evaluating parasympathetic activity and devices using such methods. In exemplary modalities, wave boundaries can be used in the estimation of parasympathetic activity. In certain embodiments of the present invention, two parasympathetic parameters can be measured for the resulting wave: the intensity of the parasympathetic response and the continuity of the parasympathetic activity. In one embodiment, the intensity of the parasympathetic response can be determined, for example, by the wavelength (time stamping of the right valley point minus the time stamping of the left valley point). If the wavelength is less than, for example, 6 seconds, the intensity can be considered LOW. If the wavelength is longer than, say, 6 seconds and less than, say, 9.5 seconds, the intensity can be considered MEDIUM. If the wavelength is greater than or equal to, say, 9.5 seconds, the wavelength can be considered HIGH. In alternative modalities the level of parasympathetic activity can also be assessed using traditional RSA measurements, such as, for example, consecutive cardiac period, standard deviation, mean deviation and the like. The continuity of the parasympathetic response can be estimated in two parts. The first, the slope of every three consecutive points can be computed, for example, when starting from the left valley point to the peak. If either of the slopes approaches zero or becomes negative, for example, then the parasympathetic flow was interrupted during the wave increase (Fig. 18). Likewise, the slope of every three consecutive points can be computed, for example, when starting from the peak to the right valley point. If any of the slopes approach zero or become positive, for example, then the parasympathetic flow was interrupted during the decrease of the wave. If the short-term slope on the left side of the wave remains high and positive, for example, and the short-term slope on the right side of the wave remains high and negative, for example, so it is estimated that the parasympathetic flow has been continuous, without interruption (Fig. 19). In exemplary modalities, the threshold for short-term slopes can be variable. Exemplary devices can track the highest positive slope in the last 5 seconds, for example. These devices can take account of the absolute value of the highest negative slope of the last 5 seconds, for example. If the absolute value of the highest negative slope is greater than the highest positive slope, for example, that value can be used to represent the "fastest change". Otherwise, the highest positive slope can be used to represent the "fastest change". In certain modalities, when examining the wave increase, if any of the three point slopes is less than 30%, for example, of the faster change, then the parasympathetic interruption is assumed. Also, during the wave fall, if any of the three point slopes is greater than 30% of (-1) x (faster change), for example, then parasympathetic interruption is assumed. It should be appreciated that other algorithms in accordance with the present invention can be used to estimate whether the short-term slope was interrupted during the rise or fall of the wave. Methods for detecting drop points The present invention also provides methods for detecting drop points and devices using said methods. In exemplary embodiments, the drop point detection routine may run, for example, every 250 ms. Each time you activate, for example, 250 ms of clock interruption, the device can insert a phantom value into a group of received pulse beats. In exemplary devices, phantom values are perceived, for example, as a pulse recently received at the time the interrupt was activated. The routine can then apply a phase determination method with this phantom value in the data group. If said phase determination method estimates that the phase changes to FALL with its phantom value, then the fall point has been detected, since the next real pulse will occur after the fall point. When a drop point is detected, a symbol, such as a triangle, can be displayed immediately by the interrupt routine. If the test is false, a symbol is not displayed. The methods that use such interruption routines allow the detection and marking of drop points in real time. Exemplary devices The following description refers to exemplary embodiments of the present invention in the form of devices that can be used to assess and treat stress in humans. In those embodiments, the RSA waves can be identified and characterized in any of the ways described above and can be used to provide biofeedback to a user. Such exemplary devices include those that provide information to users in real time to promote the production of uninterrupted high parasympathetic output over a substantial period of time. In addition to the particular embodiments described below, it should be apparent that other methods and devices may be within the scope of the present invention. In those parts where the alternative embodiments are not explicitly described, it is not the intention of the applicants to limit the present invention to the exact description provided in this section. In particular, it should be appreciated that various combinations of features described below can be incorporated into a single device and that said device falls within the scope of the invention described herein. Naturally, the full scope of the invention is based on the description in the specification as a whole. The present invention provides, for example, portable hand-held devices with battery that may include a PPG sensor, a display screen, control buttons and a power button (Fig. 20). The user can turn on these devices by pressing a power button. If the devices are used in a dark room, the user can turn on the backlight by pressing the power button a second time and hold it down for a few seconds. Shortly after the device is turned on, it will ask the user to insert a finger into the finger sensor (Fig. 21). Then the user can carefully hold the device with a finger on the top of the sensor during the entire session. The device can be held vertically, recharged on the thumb (Fig. 22a) or at an angle, resting on the curve of the fingers of the hand that holds it (Fig. 22b). Once the finger has been inserted into the finger sensor, the device can begin to calibrate the PPG sensor. A countdown meter can mark the amount of time required for calibration (Fig. 23). After the PPG sensor is calibrated, the device can use the PPG sensor to detect every pulse of blood on the finger. The resulting pulse frequency (60,000 / number of milliseconds between two consecutive pulse peaks) can then be traced on the screen on a pulse-by-pulse basis (Fig. 24 (2)). The screen also shows the user his average pulse rate (Fig. 24 (1)). PPG sensors can be very sensitive to finger pressure. That is, if the user tightens the device, the resulting finger pressure can prevent the device from gathering accurate information about the pulse rate. When the user applies too much pressure, the device may display an error message alerting the user to stop tightening the device and relax his finger (Fig. 25). As soon as the user has successfully relaxed his finger, he can return his attention to the pulse frequency display screen. When the device identifies a new wave of RSA, it can use the wave information to determine and display one or more of the following: the frequency of the last wave, the average pulse frequency of all pulse points in the wave, session score, the remaining time of the session and the stress index - how much mental stress the user is currently experiencing. The device can update the countdown clock of the session after each RSA wave has been identified. Devices may include a session countdown clock that decreases on a regular basis (e.g., once per second, once every fifteen seconds, etc.). In such modalities, the device can be updated after each RSA wave to avoid the unconscious associations that are made between the clock and the desired behavior. In other words, if the watch counts on a base per second, the user could consciously or unconsciously use the seconds as a guide to breathe at a rate of 6 breaths per minute. This association can prevent the user from unconsciously learning how to breathe after 6 breaths per minute every time he is stressed. If the user consciously (or even unconsciously) uses the watch, he or she could always depend on the device. However, updating the clock based on each wave does not prevent such a potential situation, but the clock can reinforce learning. The user will see the exact number of seconds of each breath for the amount that the clock decreases. If the clock were to decrease slowly (e.g., once every 30 seconds), the potential for unconscious associations between time and desired behavior would be avoided. However, in an alternative implementation, the clock would not reinforce knowledge. In exemplary modes, the session countdown timer may begin to decrease once the first wave is identified and the data is displayed (Fig. 26). HoweverOther modalities may begin to reduce the timer when the user begins to breathe rhythmically, or only when good waves are achieved (e.g., waves with a frequency of less than six), or only when the user is performing rhythmic breathing. Another alternative is to not reduce the timer when the breathing button is being used and guidance is provided. Users can alter the behavior of the waves, and in that way the calculated stress level, by changing their breathing pattern. As the user reduces their breathing frequency, the wavelengths increase and the amplitude of the waves also increases (Fig. 27). When a person breathes more deeply, the amplitude of the waves becomes larger (Fig. 28). When a person breathes rhythmically at a stable index, the wavelengths are modulated in the respiration index (Fig. 29). To initiate relaxation, the user can start by inhaling deeply and then slowly, letting the air out and prolonging the exhalation. This will cause the wavelengths to become longer and therefore decrease the frequency of the waves. The user can continue to inhale deeply and reduce exhalation even more until the wave frequency drops to around 6 (Fig. 30). If the wave frequency falls below six, then the user can breathe a little faster - that is, not exhale so long the next time. In certain modalities, once the user has reduced the wave frequency to around 6, he can continue to breathe at the same rate and rate as that produced by a frequency of about 6. If the user's breathing rate increases, the frequency will increase. will increase, indicating that the next breath should have a longer exhalation. If the user's breathing rate becomes very slow, the frequency will fall below around 6, indicating that the exhalation of the next breath should be a bit faster. By paying attention to the wave frequency number, a user can quickly fill the screen with rhythmic waves that are around 10 seconds in length (Fig. 31) corresponding to a frequency of about 6 breathing cycles per minute. The score of the session can be calculated and displayed after each wave of RSA is identified. The score can be based on how close the user is to achieving the desired behavior. The user can accumulate score points and various methods can be used to score the session. In certain embodiments, the user may receive, for example, 3 points if the waves have a frequency of 6 or less. The user can receive two points for wave frequencies of 7 or 8, a point for wave frequencies of 9 or 10 and zero points for frequencies of more than 10. The score of the accumulated session can be displayed numerically. Alternatively, each individual score can be presented. Another alternative is to show the current score next to a group of previous scores (either numerically or graphically). Certain preferred embodiments can graphically display the current score and a group of previous scores (Fig. 32). In this way, the user can tell when he is breathing rhythmically. When the scoring screen is uniform, the user is breathing rhythmically. Once the user has filled the screen with rhythmic waves, he can focus on inhaling a little deeper, and exhaling a little more completely. That is, the user can try to inhale and exhale a larger volume of air (called "tidal volume"). As the user carefully increases the depth of their breathing, the size of their waves will increase (Fig. 33). The user can continue to fill the screen with large waves that have wavelengths of around 10 seconds each until the session timer is finished. The user may discover that he or she has achieved a very deep state of relaxation. In certain modalities, if a user has difficulty breathing deeply and rhythmically at an index of about 6 breaths per minute they can get guidance by activating a breathing guidance function (Fig. 34). In such modalities, as soon as the user presses the breathing button, a breathing guide may appear on the screen. The user can receive inhalation instructions as the breathing bar increases (Fig. 35a) and exhale as the breathing bar decreases (Fig. 35b). In exemplary embodiments, the breathing guide marks the respiration rate of a user at about 6 breaths per minute with, for example, a ratio of 1: 2 inhalation to exhalation. In alternative embodiments, the breathing guide can be programmed to provide other proportions (e.g., 1: 3) at an index of about 6 breaths per minute (e.g., 4-8 / minute). The breathing guide can remain active, for example, for about a minute and then automatically turn off. By having a temporary, rather than constant, breathing guide, the user is encouraged to use the biofeedback protocol to achieve a breathing pattern of about 6 breaths per minute. If the user only depended on the breathing guide, it would be more difficult to learn to achieve the pattern on their own. Therefore, by unaccustoming the user of the breathing guide, the user is able to use biofeedback to create unconscious learning. Alternative modes invite the user to turn off the breathing pattern after a period of time has elapsed. Other frequencies and breathing rates can also be used. The devices according to the present invention can return the user to the regular screen after the breathing guide has been completed. The user can then adjust their breathing in the manner previously described to reduce the wave frequency to around 6, maintain rhythmic breathing, and increase the size of the waves by breathing more deeply. The user can continue this process until the session timer reaches 0:00, at which time the summary screen of the session can be displayed (Fig. 36). Exemplary modes also include devices that allow the user to use up / down arrows to select the number of large waves that he wants to produce during a session. For example, a user may choose to generate 10 large waves during a session. The credit area can be increased or decreased to accommodate the number of session waves selected. Devices in accordance with the present invention can continuously identify individual waves of RSA one at a time. The moment in which a new wave is identified can be categorized, for example, as "small", "medium" or "large". If the wave is small, a single point can be displayed, for example, to mark it as a small wave. If the wave is medium in size, then two points can be displayed, for example, to mark it as a medium-sized wave. If the wave is large, then three points will be displayed, for example, to mark it as a large wave. A user can receive a credit, for example, in the credit area whenever a large wave is identified, and half a credit, for example, in the credit area each time a medium-sized wave is identified. Of course, other values can be assigned for waves of different sizes as long as the user has information about the nature of the waves he is producing. In exemplary modalities, at the beginning of the peak (peak) of each wave, a beep can indicate the size of the previous waves. If the previous wave was small, a high tone beep may be generated, for example. If the previous wave was of medium size, then a mid-level tone can be generated, for example. If not, a low tone can be generated. The sound can be controlled by a switch such as a "(o)" button. This button can switch the sound of, for example, low volume, high volume and off. A breathing feature can temporarily activate a breathing metronome to show the user a way he can breathe to generate large waves. In some modalities, once the user has accumulated enough credit points, the session can be considered as concluded and then a summary screen of the session can be displayed. In addition, a new trace entry can be added to the tracking system. In certain embodiments of the present invention described below, the biofeedback credit is based on achieving two equally important objectives: a high level of parasympathetic intensity and a sustained parasympathetic flow. The present invention can detect every wave of parasympathetic activity in real time. When a new wave is completed, the device can estimate both the intensity and the continuity of the parasympathetic output that produced that wave. As shown in Figure 37, if the wave was produced by a continuous level of moderately strong parasympathetic activity, a two-point synod (Fig. 37a) can be placed, for example, under the wave. If the wave was produced by a very strong, continuous level of parasympathetic activity, then a three-point symbol may be placed (Fig. 37b), for example, below the wave. If the wave was interrupted and / or weak, a symbol of a point can be placed (Fig. 37c), for example, below the wave. Two squares from side to side can represent, for example, a broken wave (Fig. 37d). These symbols may reflect the activity of a person's parasympathetic nervous system (the stress recovery system) at the time the wave was made: very active (long wave), active (medium wave), not active (short wave) and interrupted (broken wave). These representations can be displayed in real time and provide information about the last previous waves (Fig. 37e). For example, the screen may show representations of the last twenty waves or a larger or significantly smaller number. Exemplary modes can also record and display wave representations much earlier in the breathing session or in previous sessions. The screen can also show a cumulative total score for a specific amount of time (e.g., 24 hours) (Fig. 37f). A cumulative total score can be generated, for example, by assigning an individual point for a long wave, a half point for a median wave, and no points for a short wave. The screen can continue to update the cumulative total until, for example, a predetermined goal is achieved, a pre-set time period elapses or the cumulative total score is re-established. A subject may try to achieve an objective to achieve, for example, 100 points per day. Particularly preferred embodiments of the present invention also provide novel forms of biofeedback information that can be used to induce the desired physiological state by unique breathing exercises. Said modalities avoid disadvantages of the techniques that create one or more interruptions of the parasympathetic branch based on respiration. For example, said breathing techniques consist in prolonging exhalation for a long period of time. In general, a longer exhalation is beneficial; but when it extends for a long period of time, long exhalations may interrupt the parasympathetic response immediately after the depression of the wave. Expecting to inhale for a long time may cause an interruption just before the crest of the wave. Also, holding your breath for a long time or if inhaling is too long or too short, for example, can tire the parasympathetic nervous system, causing temporary inhibition of the parasympathetic flow. The embodiments of the present invention counteract the disadvantages described above by, for example, guiding the user in finding a respiration rate and rhythm that produces an intense level of sustained parasympathetic flow. The feedback provided by the methods and devices in accordance with the present invention allows a user to maintain a substantially continuous state of parasympathetic flow, thereby suppressing sympathetic activity. Preferred embodiments of the present invention may also indicate to the user the point at which an RSA wave passes from the increase to the decrease. Said drop points can be identified, for example, by marking said point with a marker that is easily visible by a user. These visible indicators can be in the form of a triangle, for example (Fig. 37g). Visible indicators can have other forms. The drop point is an ideal time to start a prolonged exhalation. Alternatively, the indicators can be audible. By combining the feedback of the drop point with the measurement of the composite parasympathetic, a user can quickly learn to extend exhalation to a suitable length to create the highest scoring waves (e.g. a three point wave). The user may also receive guidance to indicate when the exhalation has been greatly extended, since the wave will break resulting in a low-score wave (e.g., a one-point wave). Therefore, methods and devices in accordance with certain embodiments of the invention allow users to find their unique window of exhalation lengths that produce a sustained outflow of intense parasympathetic activity. In use, a person simply exhales every time a new indicator appears, such as a visible triangle, and then inhales until the next indicator appears. By adjusting the length of exhalation, users learn to generate perfect waves that appear during a physiological state of sustained sustained parasympathetic activity. In another exemplary embodiment, the screen may provide exhalation numbers corresponding to a number that the user can count while exhaling. Once the breathing session starts, the subject can inhale until, for example, a drop point is indicated and then exhale while counting (preferably calmly and silently) the Exhalation Number. A timer bar can descend into an Exhalation Number column for a fixed amount of time (e.g., 30 seconds, 60 seconds, etc.). The Exhalation Number column may display a score of, for example, 1 to 9 corresponding to the effectiveness of the subject's breathing in that Exhalation Number for a fixed length of time. A longer wave indicates a effective breath than a shorter wave and can therefore receive a higher score. The score can be based on a single wave, all waves, or a subset of waves. The screen also allows the selection of alternative Exhalation Numbers allowing the subject to experiment with different Exhalation Numbers to find one or that provide the best scores. As discussed previously, in an exemplary mode the best scores are produced by the longest waves. In certain embodiments, portable handheld devices in accordance with the present invention may be used as follows: a subject turns on a device by pressing a power button; when requested, a subject inserts, for example, his left index finger into a pulse detection portion of the device; while the subject becomes comfortable (e.g., the subject sits up straight with the feet flat on the floor), the sensor adjusts to the frequency of the user's pulse; the subject selects an objective number of long waves (e.g., 5 to 100 depending on the level of stress perceived by the subject); the subject observes the wave of the pulse frequency on the screen of the device as he breathes at a pace that is natural and effortless; While breathing slowly and deeply, preferably through the nose, the subject can observe the effect of depth and frequency of breathing on wave patterns; the subject creates long waves when exhaling slowly and for approximately twice the duration of inhalation; long waves are counted on the device's screen, while waves that are not long are not counted; beginners can press a breath button on the device to help the subject synchronize to create long waves (e.g., a synchronizer may appear for a certain number of breaths that may or may not be taken into account); an intermediate user can see the drop point indicator on the monitor and exhale at the drop point and inhale during the increase of the next wave; advanced users can press a sound button and use the device with closed eyes, exhale every time the device makes some sound at the point of fall, while the tone of the sound can also indicate if the previous wave was accredited and added to the account. Various aspects of the present invention can be combined together to create numerous alternative exemplary embodiments. For example, the device can present a meter that can be used as an amplitude feedback meter instead of a stress meter. The meter could also have a target bar. Therefore, the device could graphically display how deep a person is breathing to learn to take deeper breaths. If a target bar is used, users may try to breathe deeply enough to cause the meter to rise above the target bar. Any numerical or graphic feedback (visual or not) of the amplitude would be within the scope of this alternative modality. Other alternative modalities could use wave information (eg, wavelength, amplitude, and peak placement) to determine and provide feedback related to the degree to which the user is following a prescribed breathing protocol (eg, 6 breaths per minute with an inhalation: exhalation ratio of 1: 3). Alternatively, the user may receive a breathing guide at the same time that a simultaneous audit or visual feedback is provided on how close they are to the guided breathing pattern. In addition, an objective level could be deployed for a user to be considered in compliance if it were above the target level and not in compliance with the breathing protocol if it was below that level. Alternative modes can also use the variance of one or more parameters to detect rhythmic breathing. Then, the degree of rhythmic breathing can be visually displayed graphically or numerically, or in some other way. Optionally, audible feedback can be provided. For example, in an exemplary modality a tone may increase as the breath becomes more arrhythmic and decrease as it becomes more rhythmic. Alternatively, a single beep may indicate rhythmic breathing, a double beep may indicate almost rhythmic breathing, and a triple beep may indicate arrhythmic breathing. Naturally, any of the techniques of feedback mentioned or derived from said techniques can be used independently, in combination with each other, or in combination with other techniques, or in conjunction with themselves and other techniques. Such an implementation can be used, for example, to practice yoga style rhythmic breathing patterns. For example, if the yoga student was practicing rhythmic breathing at a rate of inhale: sustain: exhale of 1: 1: 1, he could use the device to ensure that rhythmic breathing was maintained. In other embodiments of the present invention, a pre-programmed breathing guide can be provided on the device so that the user can follow the breathing guide while receiving visual feedback and / or audit feedback about the rhythm capability of their breathing. In addition, the breathing guide can be programmable. Optionally, feedback can be provided not only on the rhythm of breathing, but also on the frequency. For example, if the user would like to practice breathing at a ratio of 1: 1: 1 at 5 breaths per second, visual feedback and / or auditing can indicate the degree to which a user is breathing rhythmically at five breaths per minute. Breathing to another frequency and / or arrhythmically will reduce the score. Another exemplary modality provides feedback on the depth of breathing. During rhythmic breathing, a measurable phenomenon used by the aforementioned methods, the main difference in wave amplitudes is the volume of the tide (the depth of breathing). Therefore, amplitude measurements can be used for visual feedback and / or auditing to indicate the depth of a person's breathing. As previously established, deep breathing is a useful way to relieve stress. Exemplary modalities can provide feedback on the depth of a user's breathing to help the user learn to breathe deeply and thereby relieve stress. In summary, the exemplary embodiments of the present invention can provide audit and / or visual feedback for the following: respiration frequency, respiration rate, depth of breath, adjustment of respiration at a prescribed frequency / rhythm, points of transition from increase to decrease (eg, drop points) and the like. An evaluation of each of these can be done, alone or in combination. Feedback can be provided on one or more of these assessments. Any implementation that identifies two or more waves of RSA and obtains frequency, rhythm, depth, and / or adjustment is within the scope of the present invention. Exemplary form factors Exemplary embodiments of the present invention incorporate numerous features in addition to those described above. One characteristic is the design of the positive form factor. Prior to the present invention, biofeedback programs used PPG sensors on the finger, PPG sensors on the ear, and / or heart rate ECG sensors that were connected to a computer via a cable. Although the PPG sensors are sensitive to finger movement and pressure, previous devices did not have to deal with the many artifacts created by movement or excessive pressure since they used PPG sensors on the finger that were often placed on tables or desks In this situation, users could support their hands and fingers on the desk that stabilized the hand and finger, preventing excessive movement and pressure of the finger.

Because external cables are generally so unacceptable, the exemplary embodiments of the present invention integrate a PPG sensor directly into portable devices and eliminate external cables. As a result, the devices in accordance with the exemplary embodiments of the present invention can be conveniently used in a public place. However, the integration of a PPG sensor into a portable device requires innovative form factors. For example, because the sessions can last between 5 - 15 minutes, or that the users of the device will be holding the device, without a stabilizing structure like a desk, for a prolonged period of time. Also, the present invention provides devices that can be grasped comfortably, while allowing the user to rest his finger on the finger sensor simultaneously. The present invention also provides comfort-mode factors while minimizing artifacts caused by movement and pressure over extended periods of time (e.g., 10-15 minutes). Two exemplary form factors meet these objectives. In the first, the finger sensor may be on top of the device near one of the edges. Ergonomically, the height from the bottom of the device to the top can be between 1.5 inches and about 3.5 inches and is preferably around 2.5 inches. This allows the device to be supported either by the thumb when held vertically (Fig. 22a), or supported by the curve of the fingers when tilted (Fig. 22b). In the second, the finger sensor is located in the rounded back part of the device with the screen in front, allowing the device to rest, for example, in the palm of the hand during use (Fig. 38). The particularly preferred form factor is that which is described in the first instance since it allows the design of products with a scientific and medical appearance and structure. Detection methods and error correction The present invention also provides methods for detecting and correcting errors in previously described devices and devices using said methods. Although the shape factors described above minimize the artifacts, the equipment's form factor may not eliminate every possible artifact. Because there is no support structure such as a table or desk, the hand and finger will move at different times during the session. The remaining artifacts could be serviced by the program. Exemplary embodiments of the present invention not only detect when an error has occurred, but can also correct it. In general, the screens of small portable devices are much more sensitive to errors since such screens are very small compared to that of a desktop computer, for example. When an error occurs in a desktop computer, the screen has sufficient resolution to show both the precise data and the errors (Fig. 39a). However, in a small portable device, an error can cause all the correct data to become indiscernible due to its low resolution (Fig. 39b). There are numerous statistical methods for detecting errors in a data stream in the art. However, these methods require a large sampling of data before they provide a high degree of accuracy. As mentioned earlier, devices that have small screens can be adversely affected even by a single error. Therefore, errors must be detected quickly and accurately and then corrected. The devices in accordance with an exemplary embodiment of the present invention implement a novel method of error detection and correction and requiring only a small amount of data (approximately 10 seconds) before they become highly accurate. To facilitate the understanding of the error detection and correction methods of the present invention, a brief explanation of how the PPG sensors are used to obtain pulse information under ideal, error-free conditions is provided. The PPG sensors detect the amount of blood pressure in the finger on a constant basis. Each time the heart beats, the corresponding blood pulse results in a rapid increase in blood pressure in the finger, which then drops rapidly. The PPG sensor continuously seeks to identify the time in which the blood pressure increases (Fig. 40). This is the peak of the pulse. As discussed previously, the amount of time (in milliseconds) between two consecutive pulse peaks is known as the pp interval (pp). The devices according to the present invention can record each successive pp interval. The pulse rate of each recorded pp interval (60,000 / pp) can be displayed on the screen each time a new pulse peak is found. The difference in absolute time between successive pp intervals (absolute (pp [n] - pp [n-l])) is called the interval time between beats or IBI. Two types of errors occur when a PPG sensor is trying to correctly identify the next pulse peak (Fig. 41). One type of error can occur when the PPG sensor incorrectly identifies an artifact as a pulse peak. That is, the PPG sensor determines that a pulse peak occurs where there is not really one (Fig. 41a). This type of error is known as a false positive error.

The second type of error occurs when the PPG sensor does not identify a pulse peak that does not exist (Fig. 41b). This is known as a false negative error. Both false and false positives result in large IBIs. Error-free data may or may not result in large IBIs. However, erroneous data always produces a large IBI. Therefore, wherever there is a large amount of consecutive data that does not contain a large IBI, one can safely assume that this data is free of errors. When large IBIs occur, it may be due to an error or they may be good data; the device will need to determine what the case is. In accordance with preferred exemplary embodiments of the present invention, the first step in the error detection strategy is to wait for a certain number of intervals related to the heart rate (eg, ranges of 10 pp) where each IBI time is less than 200ms. . These data points are considered as error-free; the number of consecutive intervals may be less than 10 but needs at least to be 2, preferably at least 3 and still most preferably at least 5. Another alternative is to wait for a group of consecutive data points at which each IBI time is less than 1/3 of the interval related to the lowest heart rate, such as a ppl interval, in the consecutive data group (eg, 5 consecutive pp intervals). The scale of these data points can be computed. As used herein, "scale" can refer to the absolute scale (ie, pp min to pp max), a derivation of the scale (eg ((pp min - 10%) - (pp max + 10%)) , or as a computed variation (eg average deviation, standard deviation, etc.) Any suitable mathematical description of the scale can be used The preferred embodiments in accordance with the present invention use pp min - ((pp max - pp min) x 25%) as the bottom of the scale The preferred mode uses pp max + ((pp max - pp min) x 25%) as the top of the scale.The scale can be derived from the full data set or a subgroup Once the scale has been established, each new pp is tested to determine if it is "on the scale." In exemplary modes, a new pp value is considered "on the scale" if it is greater than the lower volume and lower than the upper volume, however, "on the scale" can also refer to any determination mathematical proximity to the current p-p to the scale as determined by the calculation of the selected scale. For example, if the scale was calculated using the standard deviation, "on the scale" could refer to the statistical determination that the current p-p is 80% or more likely to be within the computed variation.

As new pp intervals arrive, the new IBI can also be computed (new absolute pp - previous pp). The new EBI can be tested to determine if it is "large". In preferred embodiments, the device tests whether the IBI is greater than half the lower value of the scale. If it is greater, the IBI is considered large. In other exemplary embodiments, the JBI time of the new pp interval minus the previous interval can be computed. Instead, other times of IBI could be used, such as the EBI of the new p-p compared to the average of p-p of the last number n of the pp intervals. In addition, different implementations may use a different threshold to distinguish large IBIs from non-large IBIs. In accordance with the embodiments of the present invention, any implementation using the difference of pp intervals or the difference of a derivative of pp intervals (such as the average) in order to detect an error can be used. To summarize the above, when the device is started in accordance with the exemplary embodiments of the present invention, you may not enter an error detection mode until 10 consecutive pp intervals are located where all IBI times are less than 200ms. Then, the device can calculate the scale of these pp intervals and start an error detection mode. In the error detection mode, the device can test each new pp to determine if it is "on the scale" and the device evaluates each new D3I to determine if it is "large". Any other suitable method for determining if either or both of these properties for use in detecting errors are also within the scope of the present invention. If the next p-p is "on the scale" and if the p-p is not "large", then the new p-p can be considered as error-free. If the p-p is not "on the scale" and if the IBI is not "large", the new p-p can be considered as error-free and the scale is recalculated to include the newly found pp value. If the new p-p is "on the scale" but the IBI is "large", the new p-p can be considered as error-free. However, when the new p-p is "out of scale" and the IBI is "large", then the new p-p can be considered as the result of an error. Once an error has been detected, it must be corrected. Therefore, each time an error is detected in the error detection mode, the device switches to the error correction mode. The device can be kept in error correction mode until the erroneous condition has been resolved. Figure 42 provides a flow diagram showing an exemplary error correction methodology employed during an error correction mode. The error correction includes adding each successive pp interval as defined until the sum of the pp intervals is "on the scale" or the sum can be divided by an integer so that the result of the division is "on the scale" . When the sum by itself is "on the scale", all the pp intervals that make up the sum can be combined into a single value equal to the sum. When the sum is divided by an integer on the scale, the erroneous values can be substituted with n (where n = the integer denominator) number of values equal to the result of the division. The following discussion provides examples of how errors can be corrected in accordance with an exemplary embodiment of the present invention. For example, if the scale is 600ms - 1,000 ms, and the time of the erroneous pp interval is 200 ms. The next pp interval is 100 ms. The sum is now 300 ms. It is not "on the scale". The next pp interval is 400 ms. Therefore the sum is now 700ms. It is "on the scale" and therefore 700ms is the corrected value. The three pp intervals (200ms, 100ms, and 400ms) will be combined at a value of 700ms. The device then returns to error detection mode. As another example, if the scale is 700ms - 1,000 ms, and the wrong pp interval is 1,300 ms. There are no integers among which one can divide 1,300 ms that result in a value "on the scale". Therefore, the following pp interval (300ms) is added together to produce 1,600 ms. At this time, there is an integer that can be used in a division to produce a value "on the scale". The integer 2 results in a value that is "on the scale" (1600/2 = 800 ms). Therefore, the two erroneous values (1,300 ms and 300 ms) will be replaced with two (the integer) values of 800 ms (the result of the division). In exemplary embodiments, the devices in accordance with the present invention will be able to generate corrected values within one or two additional pps intervals. However, it is possible for a device to enter an error correction mode indefinitely. Therefore, the present invention can include a security mechanism to solve this situation should it occur. For example, if the device remains in the error correction mode for a long time, then the device recalculates the scale by applying a statistical method to the original data points found. That is, every unprocessed pp interval received from the PPG sensor is used. The scale is computed with a scale calculation based on statistics, for example, a formula of standard deviation. In exemplary embodiments, the average pp interval is determined from all the unprocessed pps intervals found (either true or erroneous). The scale is defined as 15 beats per minute below the median of up to 15 beats per minute above the median. The interval of pps in the error queue is reprocessed in accordance with the new scale. Note that the scale can also be computed with a subset of the raw data points (e.g., the last 50 data points). The present invention may also include any method for recalculating the scale to solve the extended error condition. As stated previously, PPG sensors are sensitive to movement and pressure with the fingers. They are also sensitive to bright light and cold fingers. Therefore, there are numerous factors that can cause multiple errors. In certain embodiments of the present invention, when the ratio of signal to sound for ten seconds drops below 25%, the device may cycle an error message screen (such as that shown in FIG. 18) until the device exit the error correction mode. As a result, the user will receive information about changes that can be made to help the device gather accurate pulse information. The present invention also provides alternative methods for detecting and correcting errors in a group of heart rate interval data. For example, there are numerous implementations that will allow the scale and / or thresholds of IBI to change dynamically as heart rate range values are detected. Such implementations can provide a marginal increase in accuracy in certain circumstances. For example, the scale can be evaluated continuously using a pop-up window. The scale can be started after receiving the first 10 seconds of the pps interval in such a way that each consecutive IBI is less than 200ms. After this point, the scale could be reevaluated continuously using a drop-down window of the last 10 seconds of reliable data. The last 10 seconds of reliable data may or may not be consecutive. For example, the top of the scale (r_superior) could be the highest pp in the last 10 seconds of reliable data and the bottom of the scale (r_inferior) could be the lowest pp in the last 10 seconds of reliable data . Another alternative is to reduce the rate at which the scale can expand and contract dynamically. For example, each time a new pp value is detected, the scale could be updated in three steps. First, the upper data group (upper_dfs) and the lower data group (lower_dfs) are identified from the last 10 seconds of reliable data. Second, the ds_superior and ds_inferior are adjusted in such a way that they do not change significantly from the previous superior ds (p_ds_superior) and from the previous ds_inferior (p_ds_inferior). For example, if the p_ds_superior is greater than the ds_superior, then the ds_superior could be re-established at p_ds_superior ((p_ds_superior - ds_superior) / 25 + l). If the upper p_ds is lower than the higher_d_s then the upper_d_s could be reset to p_ds_superior + ((ds_superior p_ds_superior) / 4 + 1). If the p_ds_inferior is greater than the ds_inferior then the ds_inferior could be reestablished to the p_ds_fiorior - ((p_ds_inferior - ds_inferior) / 2 + 1). If the lower p_ds is smaller than the ds_inferior then the lower_d_s can be reestablished to ((ds_inferior - p_ds_inferior) / 25 + l). As a result, the upper r_ would be equal to the higher_d_superior and the r_inferior would be equal to the set_superior_ds. A p-p would be considered "on the scale" if it is between the lower r and the upper r. The methodology described above can achieve three objectives. The first allows the scale to increase and decrease dynamically. Second, the scale can expand faster than it contracts. The third, the bottom of the scale can expand faster than the top of the scale. There are numerous ways to implement these methods and any implementation that achieves any of these three objectives is within the scope of the present invention. Another alternative includes converting the converted pp scale to a scale of pulse rate values (prv) and comparing each new detected prv (60,000 / pp) to the scale of the pulse frequency. "On the scale" it could be determined whether the new prv was lower or not at the maximum prv (maxjprv) and greater than the minimum prv (min_prv). Or, "on the scale" can refer to whether the new prv was close enough or not to the scale of the prv values. For example, the upper scale and the lower scale could be expanded by a certain number of beats (i.e. max_prv = max_prv + 9 and min_prv = min_prv - 9). As a result, any new prv that is within 9 bpm of the data group scale can be considered 'on the scale'. Like the pp scales, the calculations of the prv scale can also be dynamic. That is, as new prvs arrive, the scale could be recalculated if the new prv is considered reliable (e.g., IBI is not very large). Another method to increase error detection capabilities is to use two threshold values to determine how close a new one is IBI of the previous IBI. For example, if the new IBI is lower than the low threshold, it can be considered a "small jump". If the new IBI is between the two thresholds, it can be considered an "important jump". And if the new IBI is higher than the second threshold, it can be considered a "big jump". Therefore, as the new values arrive, it can be estimated whether the new value is "on the scale" or "off the scale", and whether the new EBI is a small jump, an important jump, or a large jump. Decisions about whether to display the value, use the value to update the scale, and / or correct the value can be based on those estimates. Any interval related to heart rate can be used to determine the importance of IBI levels. For example, the difference in the interval between beats of two prvs (the prv IBI) could be used when evaluating the proximity of the new pulse value to the previous pulse values. As a result, the IBIs can be computed and evaluated for the pps interval, the prv values, the rr intervals, the hr values and the like. Still another alternative includes using the IBI change direction to determine if the jump is small, important or large. When a person is physically quiet, the pulse rates may increase or fall in different indices. As a result, different thresholds may be used depending on the direction of change. For example, a prv IBI that is greater than the prv IBI above can be considered a small jump if it is less than 8 bpm, an important jump is between 8 and 15 bpm, and a large jump is greater than 15 bpm. And a prv IBI that is less than the previous IBI prv can be considered a small jump if it is less than 8 bpm, a major jump is between 8 and 12 bpm, and a large jump if it is greater than 12 bpm.

Another exemplary mode includes basing the prv IBI thresholds on the location of the previous prv on the scale. If the previous prv is already at the top of the scale, the threshold could be set lower, since in theory, one would want the next prv to jump very far outside the scale. Also, if the previous prv is already towards the bottom of the scale, the prv thresholds to jump down may decrease. Therefore, examples of prv IBI thresholds based on the location of the previous prv on the scale can include: ((r_superior - ant_prv) (l / 3)) + 10 for a small hop down, ((r_superior - prev_pr) (2/3)) + 15 for a large jump up, ((prev_prv - r_inferior) (l / 2)) + 10 for a small jump down, and ((prev_prv - r_inferior) x (2/3)) +15 for a big jump down. Yet another exemplary modality is to add a third test such as direction in determining if a new interval point of the heart rate needs to be corrected. For example, if the point fails the IBI and scale tests, but is closer to the scale than the interval point of the previous heart rate, then it can be considered acceptable. In certain circumstances and implementations, a marginal improvement may be obtained by combining the dynamic scale method, the double IBI threshold method with different thresholds based on the direction, and the heart rate interval address method. An example of such combination is as follows. Because each new prv is calculated (60,000 / pp), it can first be evaluated whether or not it is 'immediately deployable'. If the prv is a small hop or small hop down (using the appropriate thresholds) it is 'immediately deployable' and therefore is immediately deployed. If it is an important leap but it is 'on the scale' then it is 'immediately deployable' and therefore it is immediately deployed. If not, it could be re-evaluated by address to see if it is deployable. If the current prv is closer to the scale than the previous prv, then it is still displayed. If not, it is not deployed and must be corrected. Combinations of the methods described above can also be used to determine whether the value was 'reliable' or not. That is, these methods can be used to determine if a new prv should be used to recalculate the dynamic scale. For example, if the new prv is a small jump, it would be considered 'reliable'. If the new prv is an important jump, but it is 'on the scale' then it can be considered 'reliable'. And if the new prv is an important jump and 'out of scale' but is close to the scale of the previous prv, then it can be considered 'reliable'. When deciding which methods to use to detect and correct errors in a group of data, one must consider the stability of the equipment, the environment of use, and other factors used to detect and correct errors in a group of data, one must consider the stability of the equipment, the environment of use, and other factors to determine whether the degree of potential statistical advantages of complex combinatorial methods offers a greater practical utility over the methodology of IBI / basic scale. In most situations, the IB I / basic scale strategy is sufficient. However, if significant movement is expected, sunlight, pressure and similar factors are quite sufficient. However, if significant movement, sunlight, pressure, and similar factors are expected, the additional statistical methodology described above may be implemented to provide greater accuracy in correcting and detecting errors in a data set. Resolution of scaling problems and identification of rhythmic breathing The methods and devices described above can also use RSA wave information to innovatively scale the area of the screen on which the waves are displayed. The wave amplitude of RSA can vary significantly from person to person. As described before, the amplitude of RSA depends on the age, sex, level of condition, the breathing pattern of the subject, among other factors. While large screens can accommodate large waves or small waves, small screens on portable devices require sophisticated scaling. Therefore, if the scale on a small screen is very small, then large waves will not fit on the screen. If the scale is very large, then the shape and size of the small waves will become indiscernible. And if the scale is very dynamic and fits very frequently, then it will appear that the large waves and small waves are of the same size, and the user will not be able to discern whether or not his breathing pattern has changed. The devices in accordance with exemplary embodiments of the present invention can solve the scale problem by adjusting the scaling of the screen differently during two stages. The first stage lasts from the moment the device is turned on until the user begins to breathe rhythmically. The second stage lasts from the moment the device detects rhythmic breathing until the device is turned off. During stage 1, a very basic scaling technique can be implemented. During stage 2, an innovative approach can be used so that the user can accurately estimate when breathing has become more shallow (less deep). For example, when the device is turned on, the escalation preferably approaches a predefined, small value. Then, the device moves away when a pulse frequency point is found that is greater than the highest value or less than the lowest value that can be plotted using the current zoom level. The scale is moved away so that the new pulse point is plotted at the edge of the screen area of the device. To give the user an idea of scale, the device only moves away, does not approach, at first. The screen only comes back closer after the big waves have left the screen, so the full height of the screen is used from the top to the bottom. The screen continually moves away and in such a way that the data points displayed consume the full range of the screen at all times until the user begins to breathe rhythmically. Once the user begins to breathe rhythmically, the device seeks to encourage him to take a deep breath. If the device continued to approach automatically when the small waves appeared, then the small waves produced by surface breathing will appear the same size as the large waves produced by deep breathing. This will not allow the user to visually discern their depth of breath the size of the waves.

The devices in accordance with the exemplary embodiments of the present invention use the wave information to detect rhythmic breathing. Rhythmic breathing produces waves with uniform wavelengths, frequencies, amplitudes, peak-to-peak times, and peak placement times (Fig. 43). By measuring the variance of one or more of these wave parameters, rhythmic breathing can be identified. Exemplary modes calculate the variation of the wavelengths and amplitudes of the last three waves. When both variations are low, then it is considered that rhythmic breathing began. A method to determine the variance, and therefore establish when the variance is small, can be based on the percentage relative deviation. This method is useful for comparing the variation of two or more values (e.g., peak to peak times, wavelengths, frequencies, etc.). This can be done as described below. First, the average (average) values can be determined. Then, the sum of the difference (sun \ _dif) of each value of the average can be computed. The sum can be divided by the average x number of values. For example, consider four wavelengths: 10, 8, 10, 8 seconds. The average is 9. The sum of the differences of the mean is 4 (10 is 1 far, plus 8 is 1 far, plus 10 is 1 far, plus 8 is 1 far). Therefore, 4 is divided by the mean x the number of values (4 / (9 x 4)). As a result, the percentage relative average deviation is 11.1%. Consider four amplitudes: 30, 28, 30, 28 bpm. Although the deviation is also 4 as in the previous example, the relative percentage deviation is only 3.4%. Therefore, the percentage relative average deviation is automatically scaled to the range of values that are being analyzed. The variance of any of the wave characteristics can be analyzed alone or in combination using numerous methods. The preferred modality uses the percentage relative percentage deviation. The higher the percentage that results, the greater the variance. A threshold of variance could be established to determine if rhythmic breathing has begun. For example, if three or more waves have a variation in a wave characteristic of less than 20%, one can conclude that rhythmic breathing has begun. In a preferred embodiment, rhythmic respiration is considered to have started when the variation of the wavelength and amplitude of the last three waves is less than 10% each. Once rhythmic breathing has begun, the largest amplitude (maximum amplitude) formed by the resulting rhythmic waves can be traced. The device continues to determine if the user is still breathing rhythmically with, for example, each wave. As long as the user continues to breathe rhythmically, the device will continue to search for the largest amplitude (maximum amplitude). If a newly formed rhythmic wave has a higher amplitude than the current maximum amplitude, then the maximum amplitude can be readjusted to be equal to the new amplitude. In general, the screen does not approach beyond the maximum amplitude. That is, the scale of the screen can be set in such a way that a wave with an amplitude equal to the maximum amplitude will completely consume the screen from the top to the bottom. The zoom level can be set so as not to exceed this point. As a result, the device can move away, but can get closer beyond the set point determined by the maximum amplitude. In this way, users will notice when their breathing is shallow, since they will see the relatively smaller waves (in relation to the maximum amplitude) on the screen. Sometimes an erroneous wave (a wave with corrected errors that is incorrectly reconstructed) may have the greatest amplitude. This large amplitude may be erroneously high. In addition, the larger amplitude of a person can degrade over time until the lungs become accustomed to rhythmic breathing. That is to sayAs the lungs become tired, they will not be able to reproduce waves with amplitudes equal to the maximum amplitude. Since the device should not frustrate the user, but encourage him to produce the largest waves he can comfortably, the device can reduce the maximum amplitude value over time if a series of successive waves does not get close enough to the maximum amplitude. In preferred embodiments, if three consecutive rhythmic waves have amplitudes less than 80% of the maximum amplitude, the maximum amplitude can be readjusted using the following formula: (largest amplitude of the last three waves) x (100/85). Another alternative is to continuously decrease the maximum amplitude until the waves are close enough to occupy the screen from the top to the bottom. For example, the maximum amplitude could decrease by 5% each time a newly formed rhythmic wave has an amplitude of less than 80% of the current maximum amplitude. Another way to use the amplitudes would be to take the highest average amplitude. For example, the average amplitude of the last three waves could be calculated each time a new wave is found. The highest average amplitude can be used as the minimum fixation point. The use of high amplitudes that occur in rhythmic respiration to establish fixation points is a novel and useful component of the described invention. It is intended that any scaling based on amplitude, range, variance, or deviation is within the scope of this invention. For example, the standard deviation of the data set, or subgroup of data, could be determined. The maximum zoom level can be set in such a way that the values with an accurate probability related to the deviation consume the screen. For example, all values that have a 80% probability of being within the standard deviation would fill the screen from top to bottom. System and additional program processes by way of example The methods and devices described above can be implemented, for example, as a process stored in a memory of a data processing device, such as, for example, a computer. Said process may, for example, be in the form of a program and may, for example, be executed by a data processor or CPU and the results displayed on the screen, such as, for example, a CRT, plasma or other screen of computer as is known in the art. As a result, for example, said software can be implemented in a system comprising a CPU, a memory, and a screen, all connected by one or more buses or data paths. Figure 44 illustrates said exemplary system. With reference to this, an I / O or input / output interface 5501, a CPU 5505 and a memory 5510 are provided. The three components of the exemplary system can be connected to communicate through a 5520 system bus. As noted , the system bus 5520 is a logical component, and in any modality, it can comprise a plurality of interconnections between the elements of the system. In view of such exemplary system, a program process can be loaded into memory 5510 and executed on CPU 5505. In addition, a user can provide input to the process through I / O 5501, and output to the user by visual, auditory means , tactile or other means may provide for provision to a user who also uses l / O. Said I / O may comprise a physical interface device, which contains one or more sensors, or may, for example, comprise one or more of a microphone and one or more speakers, a keyboard, a mouse and a screen, and a mechanism input and tactile output. Additionally, said program process may, for example, be expressed using any suitable computer language or combination of languages using known techniques, and may, for example, be implemented as an established system or an instruction program conventionally stored using known techniques. Said program process can be implemented, for example, in a device that can be used to assess stress in humans, as described above. Said exemplary program process may have, for example, a higher-level process that interacts with a user when displaying messages to a user and by, for example, continuously searching and responding to various user actions, such as, for example, a user who presses a breathing guide button or pulse that emanates from a user's finger. Said exemplary program process is illustrated in Figures 45-63, as described below. It is noted that Figures 8 (a) - (b), described above, are integrated with this exemplary program process, and therefore the "process_wheel" subroutine, described below in connection with Figure 58, calls the "get-round" subroutine illustrated in Figures 8 (a) - (b). Figures 45-46 show an exemplary top-level process, which can control what is displayed to a user and can, for example, respond to the user's actions. This higher-level process essentially initializes the variables and then waits for interruptions to which it responds. With reference to figure 45, in 3601 the variables can be initialized. This initialization can include, for example, setting the mode of the device to "Spontaneous" and setting the values for the following variables to zero: number of unprocessed time jumps, number of time jumps, number of pps intervals, number of intervals between beats, error_sum, number of waves, number of the interval of pps and interval number of temporary hops of pp, as well as to establish the status of the variable to UNSUPPORTED. This initialization can, for example, be implemented according to the following pseudo code: n_rt = 0; n_ts = 0; n_pp = 0; n_i bi = 0; state = UNSURPOSE; err_sum = 0; n_wings = 0; n_val4 = 0; n_ppts = 0. Continuing with the reference to figure 45, in 3602, for example, a "Insert Finger" message can be displayed to a user. In 3603, the process waits for an interruption, without doing any other action until one occurs. In 3604, if a user inserts a finger, then in 3610, for example, the device starts the calibration, updates the message on the screen and clears the switch, returning to 3602. The process flow for this exemplary top-level process it continues as illustrated in Figure 37. With reference to Figure 46, at 3710, if a user presses the breathing button, as described above, this may trigger an Oppressed Breathing Button Switch. The process flow moves to 3720, for example, when the device mode is set to "Guided", the Start variable is set to be the current time and the switch is removed. The process flow can be moved to 3721, where the clock switch can be, for example, set to 100 milliseconds. The process flow can be moved to 3730, where the Guided Mode screen is presented to the user. The process flow then returns to breakpoint 2 in Figure 37 and back to 3603 of Figure 45, where the upper level again waits for another switch to occur. This brings the process flow back to Figure 46 through the breakpoint 1 where, at 3711, for example, if a clock switch occurs, the process flow moves to 3703, and it tests whether they have elapsed. less than two minutes from the time the user clicked the Breathe Button on 3710 and entered Guided Mode. If it is still less than two minutes, the process flow can be moved through 3731 to 3730 in which the Guided Mode screen can, for example, be updated. If in 3703, for example, it has been more than two minutes since the user clicked the Breathe Button, then the process flow can be moved to 3702, the Mode variable is reset to "Spontaneous", and the process flow moves to 3701 where, for example, the Spontaneous Mode screen is restored. Finally, with respect to Figure 46, in 3712, if a pulse is detected, a Detected Pulse Switch occurs, and the process flow moves, for example, to 3713, where the pulse subroutine is called. Process. This terminates the exemplary top-level process illustrated in Figures 45 and 46. Figures 47-51 illustrate the process flow of an exemplary master routine in accordance with an exemplary embodiment of the present invention, entitled Process Pulse. The Process Pulse calls the subroutines error_correct (Figs 52-54), error_detection (Figs 55-56), initialize_scale (Fig. 57) and process_probes (Figs 58-59). At the same time, process_probes calls the subroutines get_probes (Figs 8 (a) - (b)) and determine stress (Figs 60-62). As a result, all subroutines are called, directly or indirectly, by the Process Pulse. With reference to Figure 47, in 3802, the unprocessed time step rt [n_rt], which is rt [O], in view of the initialization in 3601 of Fig. 45, is set to the current time in milliseconds, and n_rt, or the number of unprocessed time steps, is pre-incremented. Then, for example, in 3803, 3804 and 3805, the state of the variable can be proven to be UNSETTING, DETECTING or CORRECTING to determine whether the data is assumed to be error-free, suspicious or erroneous, and along which path the Flow of the process. If the state = CORRECTION the data path starting at 3805 will be taken, calling a subroutine of error_correct at 3805. If the state = DETECTION, the data path starting at 3804 will be taken, eventually calling a subroutine of error_detection at 3910 of Fig. 48. These two data paths finally arrive at 4011 of Fig. 49. If the state = UNASSESSED, the process flow can continue directly to 3901 of Fig. 48 where the time variables are initialized, including pre-increment n_ts, a variable that dredges the number of time steps, and to 3902 where n_ts is verified as greater than one. That is the case, in 3903, for example, n_val, the interval number of pps to be assigned, can, for example, be set equal to 1, and the process flow can continue through breakpoint 9, to 4010 of Fig. 49, and to 4011. When the process flow reaches 4011 there are one or more pp values that need to be assigned. Therefore, at 4011, each value of pp is assigned a value and if there is more than one value of pp (i.e., n_val >; 1) then the real-time steps can be generated, and the instantaneous pulse frequency is displayed, which is the frequency of the pp interval determined by (60000 / pp [n_pp-l]). From 4011 the flow of the process continues to 4110, where if there is more than one value of pp, the calculation of intervals between beats (IBFs) is possible. In 4110 the process proves its condition, and if so, the IBI values can, for example, be calculated in 4111. If not, the process flow can return to 4010. In 4111, once the values of IBI, the process flow moves to 4201 to test how many pp values exist. If there are more than 8, i.e., at least 9, then there is enough data to identify a level 4 valley. Once there are at least two level 4 valley points, Le., Num_val4 > 1 in 4212 the exemplary process can look for RSA waves, as described previously. Therefore, a yes in 4212 can, for example, cause the process flow to call a subroutine to process_waves in 4213.

Figures 52-54 illustrate an exemplary process flow for an error correction subroutine. As described above in conjunction with the exemplary Process Pulse routine, in 3805 of FIG. 38, an error correction subroutine is called. With reference to Figure 52, the process flow starts at 4301, where the subroutine begins. In 4302, for example, a variable err_sum, which accumulates a current interval of pp times, has the most recent pp interval added to it. Additionally, the variable n val is set to 0. The process flow continues in 4303, where the new value for err_ sum is tested to see if it is on the scale. If so, the process flow can be moved, for example, to 4310, where the n_val variable is set to 1, representing a correct pp interval that is being identified, and the value of that pp interval is set! equal to the number of milliseconds in err sum, and the process flow returns to 4320 at the Process Pulse. On the other hand, if at 4303 the tentative time interval pp is not within the scale, the process flow can be moved to 4304, where, for example, the subroutine tests whether the current time interval pp is less than scale. If yes, the process flow returns to 4302 and an additional time interval pp is added to the variable err_sum. If it is not, then the current sum is considered to be very high and an adequate integer must be found with which to divide it to create two or more pp intervals "on the scale". The process flow continues from 4304 to the breakpoint 20 to 4401 of Figure 53. There, integer_test = 2 is set as a test splitter and the process flow can be moved, for example, to 4402 where a time variable tmp_val is set to retain the quotient of err_sum / try_end, representing a range of current possible corrected pp. The process flow can then be moved to 4403, where, for example, tmp_val is evaluated to see if it is above the scale. If so, then in 4410, for example, the variable of integer_test increases and the proposed division occurs once again in 4402. On the other hand, if in 4403 tmp val is not above the scale, that in 4404, by example, tmp_val can again be tested to see if it is within the scale, and if so, the process flow can be moved (via breakpoint 21) to 4501 of Figure 54. In 4501 of Figure 54, a count variable can be set to 1, and in 4502, for example, the subroutine can check if the count is less than the current value of try _ integer. Otherwise, the flow of the process can be moved, for example, to 4510, and the variable n_val can be set equal to test_entero and 4520, for example, return to the Process pulse, at the break point 6 of the figure 47. On the other hand, if the count is less than the integer test in 4502, then the process flow can, for example, surround 4503, 4504, and 4502, increasing the count value of each rodeo (in 4504) until the count it is to prove_entero, at which time the process flow can return to the Process Pulse. Next, an exemplary error detection subroutine is described with reference to FIGS. 55-56. With reference to Figure 55, the process flow begins at 4601 and continues at 4602, where a current interval of pp is loaded in a time interval (in the sense of tentatively correct) of pp tmp_pp. In 4603, the tmp_pp is tested to see if it is within the scale. If so, then the value of n_val is set to 1 and val [0] is set equal to tmp_pp in 4610 and in 4620 the process flow returns to the calling program, Process Pulse, in particular to 3911 in Fig. 48. However, if 4603 tmp_pp is outside the scale, then in 4604, the variable of the interval between temporary beats tmp_ibi is generated to use it to detect any error as described previously. The flow of the process may continue later (to breakpoint 22) at 4701 of Figure 56, in which tmp_ibi is tested to see if it is greater than the lower end of the scale, which is a test to detect if it is very large, as described above. If yes, it is assumed that there is an error, and the flow continues to 4702, where the variable err_sum is equal to tmp_pp (err_sum is an input to the error correction subroutine described above), the "state" is set to be CORRECTION, and the process flow can be moved, for example, to 4703 where the n_val is set to 0 and the process flow returns to the Process Pulse, which can, based on n_val = 0 and the state = CORRECTION, return to 3911 from Fig. 48 to 3820 of Fig. 47, and finally flow to an error-correction subroutine at 3805. If at 4701 tmp_ibi it is not greater than half the lower end of the scale , in which case it is not considered large and therefore there is no error in the pp data data interval, the process flow can continue to 4710, and, for example, test if tmp_pp is greater than the top of the scale. Because tmp_íbi was not found large in 4701, and therefore it is not assumed that there is a present error, if in 4710 the tmp interval of pp is still larger than the existing upper of the scale, the scale needs to be recalculated using the new pp interval as max_pp, which retains the value for the maximum possible pp interval that is not the result of an error in the data. In 4711, for example, max_pp can be set equal to tmp_pp and, using this new value, in 4712, for example, the upper and lower ends of the scale are recalculated. Then the flow can continue, for example, to 4713 where n_val is set equal to 1 and val [0] is set equal to the current interval of pp, tmp_pp. In 4714, for example, the process flow can return to the Process Pulse routine. If in 4710 the current interval of pp is not greater than the existing upper end of the scale, then for example, in 4720 the minimum possible interval of pp is set equal to the current interval of pp. Then the flow of the process continues as described above to 4712, 4713 and 4714, where the process flow returns to the calling program. With reference to figure 57, the flow of the process for the initialize subroutine _ scale is described below. This subroutine can be used in exemplary embodiments of the present invention to calculate the scale for the range of pps within which the data is assumed to be error-free, for use in error detection and correction routines. Starting at 4801 in the subroutine call, the process flow moves, for example, to 4802, where the variables min_pp and max_pp are set using the following pseudo code: min_pp = lowest pp in data series; max pp = pp higher in data establishment. Then, for example, at 4803, the upper and lower ends of the scale of data points used for error detection and correction, as described above. This can, for example, be implemented using the following pseudo code: high_scale = max_pp + ((max_pp - min_pp) * 0.25; low_scale = min_pp - ((max_pp - min_pp) * 0.25). Using these exemplary values, the scale is now established, and in 4804 the process flow returns to the calling routine, Le., Process Pulse. In particular, the process flow returns to 4102 in Figure 50. Figures 58-59 illustrate an exemplary process flow for a wave processing subroutine. In an exemplary embodiment of the present invention, such a subroutine may be called, for example, by a pulse acquisition processing routine such as a Process Pulse, as described above. After the subroutine is called at 4901, for example, the process flow may continue at 4902, where the above-described subroutine of get-ahead may be called to enter the waves identified from the pulse data. The flow of the process continues, for example, to 4903, where, in view of the acquired waves, a score indicative of the stress level of a user reflected in the waves identified can be assigned using an exemplary subroutine to determine stress. The flow can continue, for example, to 4904 where the waves are drawn and the instantaneous frequency calculated based on the current pp interval using the expression frequency = 60000 / (ppts [v2 [n_ondas-l] J - ppts [ vl [n_ondas-l]]), where ppts [v] is the pulse point time at the data point v. From there, for example, the process flow can continue to 5001 in Figure 59, where a score between 0-3 can be assigned to a user based on the frequency of the current wave, in which a higher score indicates a lower level of stress. In 5002, for example, the subroutine can, for example, display to the user each of its stress levels (i) (obtained from the call to determine stress in 4903); (ií) the frequency (of 4904); and (iii) the score (of 5001), at which point, for example, in 5003, the process flow can return to the call routine, Process Pulse. Figures 60-62 illustrate an exemplary subroutine to determine a stress score. What you measure is how relaxed a given user is, by operating the wavelengths of their RSA waves. With reference to Figure 60, in 5104 the stress-determining subroutine describes assigned wavelengths, which assign a wavelength between wl_low and wl_high (set at 5102) to each wave. Using these wavelengths and the number of waves that there are (ie, the value of n_waves), Figures 60-61 illustrate the process flow for each value of n_wings between 1 and 4. A score is determined on each 5110, 5201 , 5202 and 5203, which is a weighted sum of the differences between each wavelength of the wave and w_baja, which measures how far away from the baseline that particular wave is. Therefore, a perfect relaxation score would have an a_w [n} = w_low for all n, and each score would equal zero. In exemplary similar alternative embodiments of the present invention, the score can be calculated without weighting the sums of the differences, and if this is the method described above. The score is what was described as the "wavelength". As can be seen in each of 5110, 5201, 5202 and 5203, a score of "variance", the score2 is also computed. The score and score2 can be combined in 5302 using a relative contribution factor of 70/30 to obtain the score3. Other relative weights may be used in alternative exemplary embodiments in accordance with the present invention as may be of use. The score3 can be used to calculate the stress level using, for example, the equation stress_ level = (score3 - 21) * (100 / (100-21)). The stress level returns to process_waves at 4903. With reference to figure 63, an exemplary subroutine is illustrated for assigning the wavelengths to acquired waves. This subroutine can be used, for example, in the stress-determining routine illustrated in Figures 60-62, as described above, which takes wavelengths as inputs. In an exemplary embodiment of the present invention, the process flow can start at 5401 with a call to the subroutine. In 5402 a count variable n is set equal to zero, and in 5403, for example, a current wavelength wl is calculated by subtracting the time stamp of the current v2 from that of the current vl, using the expression wl = ts [ v2 [n]] - ts [vl [n]]. In 5404 and 5405, for example, the value of wl is compared with the wl_baja and wl_alta, which can be set in the calling subroutine in 5102 of Figure 60 (where, for example, they are set to 3 and 10, respectively) . If wl is less than wl_ low or higher than wl_alta, a _wl [n] is truncated at either wl_baja or wl_baja, as the case may be, and the flow continues at 5407 where the value of n is pre-incremented. However, if wl has a value between wl_baja and wl_alta, then, for example, at 5406, a_wl [n] is set to wl, and the process flow continues to 5407. At 5408 the value of n is compared to n_wings, to ensure that each wave acquired has been assigned a wavelength. If they are equal, in 5410, for example, the process flow ends for this subroutine, and returns to 5105 in Figure 60. If they are not equal, then the flux surrounds 5403 for each wave acquired until all the waves acquired have been assigned wavelengths. Exemplary embodiments of the present invention also provide, for example, methods and devices that can determine the RSA wave phase in real time, using phase changes to detect the drop point, using the phase changes to detect the conclusion of a wave, and determine the parasympathetic intensity of the newly formed wave. Figures 64 to 74 illustrate exemplary flow processes for an exemplary procedure for determining the RSA wave phase on a pulse-by-pulse basis, using the phase changes to detect the drop point, using the phase changes to detect the conclusion of a wave, and determine the parasympathetic intensity of the newly formed wave. This exemplary embodiment describes a process directed by an individual switch that is executed each time a new pulse is received. The exemplary processes and process flows illustrated in Figures 64 through 74, as well as other exemplary functions that implement said processes, including any auxiliary function and / or process called or utilized by said exemplary flow processes, are presented for purposes of illustration . Those skilled in the art will recognize that each exemplary process or function, either at an identified function or process level, or at a general level for all a higher level process, can be implemented in a variety of functionally equivalent ways, and that the description of the following figures 64 to 74 are not constructed as limiting to a wide variety of possible implementations in real systems or devices, or which require that the exemplary illustrative process flow be followed literally. Taking this into account, for economy of expression as well as for elegance of illustration, the process flow in each of figures 64 to 74 will be described below without continuous reference to the exemplary nature of each step or step in the flow of the process, being understood that in exemplary embodiments of the present invention, functionally equivalent implementations may, for example, use different processes, as well as different sequences of processes and organizations of the process flow, of which is illustrated in figures 64 to 74, for achieve equivalent functionalities. All alternative embodiments and equivalent functional implementations are understood to be within the methods and techniques of the present invention. This exemplary process starts at 6000 (Fig. 64). The first step, 6001, in the process is to clear all the counters: num_points (tracks the number of pulses received), num_valle (tracks the number of identified wave valleys), num_pics (tracks the number of identified wave peaks), anti_phase (keeps record of the previous wave phase), ant_direct (keeps record of the previous wave direction), ant_late (keeps record of the side of the previous wave), and wave_size (keeps record of the length of the last wave). The process then flows to 6002 where it waits for the next pulse to arrive. When a new pulse is detected, the process flows to 6003, where the pulse is processed. After the pulse is processed, the flow returns to 6002 where it waits for another pulse to arrive. Figure 65 describes an exemplary process for processing a pulse. This process starts at 6004. The first step in the process, at 6005, is to obtain and record the time stamp in ms of the new pulse beat, which is stored as point [num_points] .ts. Then the process flow goes to 6006 where it evaluates if there are at least two points inside the record. If not, the process flows to 6007 where the num_points counter increases. The process still continues to 6012 and returns. However, if there are at least two points in the record, the process flows to 6008. In 6008 the peak-to-peak (pp) time of the last two points is computed and recorded as point [num_points] .pp. In addition, the pulse-index-value represented by the time of pp is computed and recorded as a point [num points]. prv. Then the flow continues to 6010. In 6010 the process evaluates whether there are at least 8 points in the record. If not, the flow continues to 6011, and the count of num_points increases. The process continues to 6012 and returns. However, if there are at least 8 points in the record, the process flows to 6009, which calls the Process Wave process. After the Process Wave process returns, the process in Figure 65 flows to 6012 and returns. Figure 66 describes a process for processing the wave information. The process begins at 6013. The first step, in 6014, consists of computing and storing the long_dependent, abs_large_pendant, and the_pendent_cut. The flow then continues to 6015 where the direction is determined by the Get Direction process. After the Get Direction process returns, the flow continues to 6016 where the wave phase is determined by the Get Phase process. After the Get Phase process has returned, the flow continues to 6017 where the wave side is determined by the Get Side process. After the Get Side process returns the flow continues to 6018. In 6018 the process determines whether or not there has been a change in the sides. If the wave has not changed sides, the process flows to 6020. Otherwise, the process flows to 6019 where the peaks and valleys are evaluated by the Get Peaks and Valleys process. After the process returns, the flow continues to 6020. In 6020, the process establishes an indicator showing whether or not a wave has been completed through the Veri process if the Wave was Completed. After this process returns, the flow continues to 6021 where the Completed Wave indicator is verified. If a wave has not been completed, the flow continues to 6023. Otherwise, the flow continues to 6022. In 6022 the Parasympathetic Wave Resistance Marking process delineates the newly formed wave, evaluates its parasympathetic activity, and marks the activity under the wave using visual symbols. After that process returns, the flow continues to 6023. In 6023, an indicator is set to show whether the drop point has crossed or not. The process of Verifying if the Falling Point is occurring makes this determination and sets the indicator in the same way. After this process has returned, the flow continues to 6024 where the indicator is analyzed. If the fall point indicator has not been established, the flow continues to 6026. Otherwise, the flow continues at 6025. In 6025 the Mark Drop Point process places a visual symbol on the wave at the point of fall, and provides an auditory signal of the point of fall. After this process returns, the flow continues to 6026. In 6026, the anti-phase, anti-dollar, and anti-direction markers are assigned. Then the flow continues to 6027, where the functions of the Process Wave return. Figure 67 describes the Get Address process. The first step in 6029 verifies if the short-term slope is greater than 30% of the absolute long slope. If so, the flow continues at 6030. Otherwise, the flow continues at 6032.

At 6030 the address is registered as ABOVE. Then the flow continues to 6034. In 6032 the process verifies whether the short slope is less than 30% of -1 x absolute long slope. If so, the flow continues to 6033. If not, the flow continues to 6031. In 6033 the address is recorded as DOWN. Then the flow continues to 6034. In 6031 the address is registered as FLAT. Then the flow continues to 6034. In 6034, the process checks if the ant address has not been established before. If it has not been established, the flow continues to 6035. Otherwise, the flow continues to 6036 where the process returns. In 6035, ant_direction is recorded as the actual address (address). The flow continues to 6036 where the process returns.

Figure 68 describes a process of Obtaining an Exemplary Phase. The first step in 6038 verifies if the long slope is positive. If so, the flow continues to 6040. If so, the flow continues to 6044. In 6040 the process verifies if the address is UP. If so, the flow continues to 6039. If so, the flow continues to 6041. In 6039 the phase is recorded as IN INCREASE. The flow then continues to 6045. In 6041 the phase is recorded as ENCRESTA. The flow then continues to 6045. In 6044 the process verifies if the address is DOWN. If so, the flow continues to 6042. Otherwise, the flow continues to 6043. In 6042 the phase is recorded as FALLING. Then the flow continues to 6045. In 6043 the phase is recorded as IN DEPRESSION. Then the flow continues to 6045. In 6045 the process checks whether the anti-phase is not yet registered. If not, the flow continues to 6046. If yes, the flow continues to 6047. In 6046, the antiphase is recorded as the actual phase (phase). Then the flow continues to 6047. In 6047, the Get Phase process returns. Figure 69 describes the Get Side process. In 6049, the process checks if the phase is IN FALL. If so, the flow continues to 6050. If not, then the flow continues to 6052. In 6050 the side is recorded as RIGHT. The flow continues to 6053. In 6052 the process verifies if the phase is IN INCREASE. If so, the flow continues to 6051. Otherwise, the flow continues to 6053. In 6051 the side is recorded as LEFT. The flow continues to 6053. In 6053 the process verifies if the offset has already been recorded. If it has not been, then the flow continues to 6054. Otherwise the flow continues to 6055. In 6054 the flow is recorded as the real side (side).

The flow then continues to 6055. In 6055, the Get Side process returns. Figure 70 describes an exemplary Get process Peaks and valleys. In the first step in 6058, the process verifies whether the wave is currently forming the RIGHT side. If positive, the flow continues to 6059. If not, the flow continues to 6057. In 6057 the peak is identified as the highest prv value during the INCREASE and CROSS phases. This value is recorded as peak [num_pícos]. Then the flow continues to 6060. In 6060, the num_picos counter increases. Then the flow continues to 6062. In 6059 the valley is identified as the lowest prv value during the previous phases of FALLING and DEPRESSION. This value is registered as valley [num_valles]. Then the flow continues to 6061. In 6061 the num_valles counter increases. Then the flow continues to 6062. In 6062, the Get Peaks and Valleys process returns. Figure 71 describes the process of Verifying if a Wave has been Completed. In the first step in 6064, the process checks if the actual phase is IN INCREASE. If so, then the flow continues to 6066. Otherwise, the flow continues to 6065. In 6065 the completed wave indicator is set to false. The flow then continues to 6069. In 6066 the process verifies if the previous_phase was IN DEPRESSION. If so, the flow continues to 6067. Otherwise, the flow continues to 6068. In 6067 the completed wave indicator is set to true. The flow then continues to 6069. In 6068 the process verifies if the previous wave phase was IN FALL. If so, the flow continues to 6067. Otherwise, the flow continues to 6069. In 6069 the process of Verifying if the Wave has Completed returns. Figure 72 describes an exemplary process of Mark the Parasympathetic Wave Resistance. In the first step in 6071 the process verifies if there are two valleys in the record. If not, the flow continues to 6077. If so, the flow continues 6072. In 6072, the wavelength is computed and recorded as wave_length. Then the flow continues to 6074. In 6074, the process checks if the wavelength is less than 6 seconds If so, the flow continues to 6073. If not, the flow continues to 8000. In 6073 the wave size is determined as SMALL. This indicates very little parasympathetic activity when the wave was formed. The process can visually mark with an appropriate symbol indicating the parasympathetic activity. In the preferred embodiment, a symbol of a point is placed below the wave. The flow then continues to 6077. At 8000 the wavelength is checked to see if it is less than 9 seconds in length. If so, the flow continues to 6075. Otherwise, the flow continues to 6076. In 6075 the wave size is marked as MEDIUM. A median level of parasympathetic activity possibly formed the wave. The process can then visually mark the wave with a suitable symbol indicating parasympathetic activity. In the preferred embodiment, a two-point symbol is placed below the wave. The flow continues to 6077. In 6076 the wave size is marked BIG.

A high amount of parasympathetic activity is represented by said waves. The process can then visually mark the wave with a suitable symbol indicating parasympathetic activity. In the preferred embodiment, a three-point symbol is placed below the wave. The flow continues to 6077. In 6077, the process of Marking the Parasympathetic Resistance of the Wave returns. Figure 73 describes an exemplary process of Verifying if the Fall Point is Occurring. In the first step in 6080, the process checks if the actual phase is ENCRYPTED. If so, the flow continues to 6082. Otherwise, the flow continues to 6079. In 6079 the drop point indicator is set to false. Then the flow continues to 6083. In 6082 the process verifies if the previous phase is IN INCREASE. If so, the flow continues to 6081. Otherwise, the flow continues to 6079. In 6081 the drop point indicator is set to true. Then the flow continues to 6083. In 6083 the exemplary process of Verifying if the Fall Point Occurs. Figure 74 describes a process of Marking Drop Point. The first step in 6085, a triangle is placed on the wave. Then the flow continues to 6086 where the process checks if the sound is ON. If so, the flow continues to 6087. If not, the flow continues to 6092. In 6087 the process verifies if the wave size is SMALL. If it is, the flow continues to 6088. If it is not, the flow continues to 6090. At 6088 the device generates a high-pitched beep. This indicates in an auditory way that the point of fall has been found, as well as indica of hearing that the previous wave was formed by a low level of parasympathetic activity. The flow then continues to 6092.

In 6090 the process verifies if the wave size is MEDIUM. If it is, the flow continues to 6089. If it is not, the flow continues to 6091. At 6089 the device generates a mid-tone beep. This indicates in an auditory way that the point of fall has been found, as well as indicates in an auditory way that the previous wave was formed by an average level of parasympathetic activity. The flow then continues to 6092. At 6091 the device generates a low tone beep. This indicates in an auditory way that the point of fall has been found, as well as indicates in an auditory way that the previous wave was formed by a high level of parasympathetic activity. The flow then continues to 6092. In 6092 the Marker Drop Point process returns. Exemplary procedures to determine, for example, the RSA wave phase in real time, using the phase changes to detect the drop point, using the phase changes to detect the conclusion of a wave, and determine the parasympathetic intensity of the wave Recently formed wave may also be implemented using the following followed code which substantially corresponds to the flow processes illustrated in Figures 64-74. num_points = 0; num_valle = 0; num_pícos = 0; ant_phase = NULL; ant_direction = NULL; antjado = NULO; wave_size = NULL; Function Process_Pulse point [num_points] .ts = current time in me yes (num_points < 1) then. { + + num_points; to return; } point [num_points] .pp = point [numjpoints] .ts point [num points-I]. ts point [num_points] .prv = 60000 / point [num_points] .pp if (num_points < 7) then. { + + num points; to return; } long_pendent = slope of the last 6 points. prvs abs_large_pendiente = absolute (long_dependent) short_pendent = slope of the last 3 points. prvs si (short_pendent> (0.30) * (abs_large_pendent)) then address = UP or if (short_pendant <(-0.30) * (abs_large_pendent)) then address = DOWN or address = FLAT; if (ant_direction = NULL) then ant_direction = address if (long_dependent &0; 0) then. { if (address == TOP) then phase = IN INCREASE or phase = ENCRYPT; } O well } if (address == DOWN) then phase = IN FALL or phase = IN DEPRESSION; } if (ant_phase == NULL) then ant_phase = phase; if (phase = IN FALL) then side = RIGHT; or if (phase == IN INCREASE) then side = LEFT; if (ant_late == NULL) then ant_late = side; Yes (anti_late <> side). { yes (side == RIGHT) then. { valley [num_valles] = point index. lowest prv during the previous phases IN FALL / IN DEPRESSION + + num_valles; } O well . { peak [num_pics] = point index. highest prv during the previous phases of IN INCREASE / ENCRYPT + + num_pics; } } if ((phase = IN INCREASE) & ((previous_phase = IN DEPRESSION) | | (previous_phase = IN FALL)) then { si (num_valles > = 2) then { wave_length = point [valley [ num_valles-l]]. ts point [valley [num_valles- 2]]. ts; if (wave_length <6000) then size_onda = SMALL or yes (onda_longítud <9000) then onda_size = MEDIAN or wave amaño = LARGE; display wave points below the previous wave} yes ((phase = ENCREST) & (previous_phase == IN INCREASE)) . { yes (sound == ON). { if (size_wave == SMALL) then it generates a beep HIGH TONE or if (wave_size = MEDIUM) then it generates HALF TONE beep or it generates a LOW TONE beep} } ant_phase = phase; ant_late = side; ant_address = address; Return of Function Exemplary embodiments of the present invention also procrete the identification of peaks and valleys in real time without first identifying the TD4 segments. Therefore, the values can be processed sequentially (e.g., one by one). Figures 75-83 describe exemplary flow processes for an exemplary method for determining the wave phase and delineating the waves on a pulse-by-pulse basis. This exemplary mode uses a global directional indicator and the position of the points on the scale to provide, for example, greater accuracy of phase determination and wave delineation. The exemplary processes and flow processes illustrated in Figures 75 through 83, as well as any exemplary function that implements said procedures, including any function and / or auxiliary process called or utilized by said exemplary flow processes, are presented for purposes of illustration. . Those skilled in the art will recognize that each exemplary process or function, either to a so-called function or process level, or to an interval level for an entire higher level process, can then be implemented in a variety of functionally equivalent ways, and the following description of Figures 75 to 83 should not be considered as limiting a wide variety of implementations in current systems or devices, or requiring that exemplary illustrative process flow be followed literally. Taking this into account, for economy of expression as well as elegance of illustration, the process flow in each of Figures 75 to 83 will be described below without continuous reference to the exemplary nature of each step or step in the flow of the process; it should be understood that in exemplary embodiments of the present invention, functionally equivalent implementations may, for example, use different processes, as well as different sequences of processes and process flow organizations, of which are illustrated in Figures 75 through 83, to achieve the equivalent functionalities. It is understood that all alternative embodiments and functional implementations are within the methods and techniques of the present invention. As shown in Figure 75, the process begins at 6093. The first step is 6094. In 6094, the process sets the PERCENT UP to 30 and THE PERCENTAGE DOWN to 15. Then the flow continues to 6095 where the counters start and markers. Then the flow continues to 6096 where the process waits for 15 seconds of valuable pulse information to arrive. Then the flow continues to 6097, where the process waits for the next pulse beat. After the next pulse beat is received, the flow continues to 6098 where the global address indicators are determined by the Quick Information Get process. When the process returns, the flow continues to 6099 where the current slope is computed and recorded. Then the flow continues to 6100 where the lowest prv of the last 12 seconds and the prv of the last twelve seconds are computed and recorded. This provides the prv value scale for the last 12 seconds. Then the flow continues to 6101. In 6101 the process of Determine Direction determines the direction of the wave. When this process returns, the flow continues to 6102 where the value of peak to peak (pp) of current points, the value of the pulse frequency (prv), the time stamp (ts), the direction, and the index knit are computed and recorded.

Then the flow continues to 6103 where the process of Obtaining Point Position determines where on the scale the current point is. A position of 100 means that the point is at the very top or above the scale. A position of 0 means that the point is in the very lower or lower part of the scale. A value between 0 and 100 indicates the percentage height of the point within this scale. When the process of Obtaining Point Position returns, the flow continues to 6104. In 6104 the wave pattern is updated on the screen. That is, the newly received prv value is plotted on the screen. Then the flow continues to 6105. In 6105 the process verifies if the address had changed when the last prv was received. If the address did not change, the flow continues to 6097 where the process waits for the next pulse. If the address changed, the flow continues to 6106 where the change of address is handled by the Change of Address process. When this process returns, the flow continues to 6097 where the process waits for the next pulse to arrive. Figure 76 describes an exemplary Get Quick Information process. In the first step at 6108, the highest positive slope during the last 5 seconds is recorded as fast_auto. Then the flow continues to 6109. At 6109 the highest negative slope during the last 5 seconds is recorded as fast_fall. Then the flow continues to 6111. In 6111 the process checks whether the rapid_aumento is greater than the absolute value of the rapid_fall. If so, then the flow continues to 6110. If not, then the flow continues to 6112. In 6110, the rapid_increment is recorded as the fastest_change. Then the flow continues to 6113. In 6112 the absolute value of fast_fall is recorded as the fastest change. Then the flow continues to 6113. In 6113, the Get Quick Information process returns. Figure 77 describes an Exemplary Address Determination process. In the first step of the process in 6115 the process verifies if the current slope is higher then PERCENT UP ABOVE% of the faster change. If so, then the flow continues to 6116. If it is not, then the flow continues to 6117. At 6116 the current address is registered as UP. Then the flow continues to 6123. In 6117 the process checks if the current slope is less than -1 x PERCENT DOWN% of the fastest change. If so, then the flow continues to 6119. If it is not, then the flow continues to 6118. At 6118 the current address is recorded as DOWN. Then the flow continues to 6123.

In 6119 the process verifies if the current address is UP. If so, then the flow continues to 6120. If not, then the flow continues to 6121. In 6120 the current address is registered as PICO_MESETA, also known as CRESTA. Then the flow continues to 6123. In 6121 the process checks if the current address is DOWN. If so, then the flow continues to 6122. If it is not then the flow continues to 6123. In 6122 the current address is recorded as VALLE_MESETA, also known as DEPRESSION. Then the flow continues to 6123. In 6123 the Determine Direction process returns. Figure 78 describes an exemplary process of determining point position. In the first step in 6125 the process verifies if the prv of the current point is less than the lowest prv in the scale. If so, the flow continues to 6126. If it is not, then the flow continues to 6127. In 6126 the point position is recorded as 0. Then the flow continues to 6130. In 6127 the process verifies whether the point prv current is greater than the highest prv on the scale. If so, then the flow continues to 6128. If it is not, then the flow continues to 6129. At 6128 the position of the point is recorded as 100.

Then the flow continues at 6130. At 6129 the relative position of the point on the scale is computed and recorded. Then the flow continues to 6130. In 6130 the process of Determining the Position of the Point returns. Figure 79 describes an exemplary Process Process for Change of address. In the first step in 6134, the process checks if the current address is UP. If so, then the flow continues to 6135. If not, then the flow continues to 6138. In 6135 the process verifies if the previous address was DOWN. If so, then the flow continues to 6136. If not, then the flow continues to 6132. In 6132 the process verifies if the previous address has been recorded. If it has not been, the flow continues to 6133. If affirmative, the flow continues to 6144. In 6133 the oscillation above the wave is handled by the process of Oscillation Process Above Null. After this process returns, the flow continues to 6144. In 6136 the process checks whether the position of the current point is in the bottom 25% of the scale. If so, then the flow continues to 6137. If not, then the flow continues to 6144. In 6137 the oscillation above the wave is handled by the process of the Regular Oscillation Process Above. After this process returns, the flow continues to 6144.

In 6138 the process verifies if the current address is DOWN. If so, then the flow continues to 6139. If not, then the flow continues to 6144. In 6139 the process checks if the previous address was UP. If so, then the flow continues to 6140. If not, then the flow continues to 6143. In 6140 the process verifies if the position of the current point is at the top 75% of the scale. If so, then the flow continues to 6141. If not, then the flow continues to 6144. In 6141 the oscillation below the wave is handled by the Regular Oscillation Down Process. After this process returns, the flow continues to 6144. In 6143 the process verifies if the previous address has ever been recorded in life. If not, then the flow continues to 6142. If it is affirmative, then the flow continues to 6144. In 6142 the oscillation below the wave is handled by the Null Down Oscillation Process. When this process returns, the flow continues to 6144. In 6144 the Change of Address Process returns. Figure 80 describes a Regular Top Oscillation process. In the first step at 6146, the lowest prv since the previous address started is recorded as the newest valley point. Then the flow continues to 6147.

In 6147 the process verifies if there are at least two valley points in the record. If so, the flow continues to 6148. If not, then the flow continues to 6150. At 6148 the wavelength of the last wave is computed and recorded. Then the flow continues to 6149. In 6149 the stress index is computed, and the score is computed. In addition, the stress index, wavelength, history, score, and other wave-based metrics are displayed on the screen. Then the flow continues to 6150. In 6150 the previous address is recorded as UP.

Then the flow continues to 6151. In 6151 the previous address index is recorded as it happened two points ago. Then the flow continues to 6152 where the process of the Regular Oscillation Process Above returns. Figure 81 describes an exemplary Process process Regular of Oscillation Above. In the first step at 6154, the highest prv since the last change of direction began is recorded as the next peak point. Then the flow continues to 6155. In 6155 the process verifies if there are at least two registered peaks. If there are, then the flow continues to 6156. If not, then the flow continues to 6157. At 6156 the time stamp of the last two peaks is subtracted to compute and record the last peak-to-peak time. Then the flow continues to 6157.

At 6157 the previous address indicator is set to DOWN. Then the flow continues to 6158 where the previous address index is recorded as it happened two points ago. Then the flow continues to 6159 where the process of the Regular Oscillation Process Above returns. Figure 82 describes a process of the Null Up Oscillation Process. In the first step in 6161 the address indicator is set to UP. Then the flow continues to 6162, where the previous address index is set to two points. Then the flow continues to 6163 where the process of the Null Oscillation Regular Process returns. Figure 83 describes an exemplary Null Down Oscillation process. In the first step in 6164 the previous address indicator is set below. Then the flow continues to 6165, where the previous address index is set to two points. Then the flow continues to 6167 in the process of the Null Up Oscillation Process. Exemplary procedures for determining, for example, the wave phase and the delineation of waves on a pulse-by-pulse basis which can also be implemented using the following pseudo-code which corresponds substantially to the processes of the flow illustrated in Figures 75-83. // point_structure pp, prv, ts, address // define #define PERCENTAGE ABOVE 30 #definit PERCENTAGE_ABAX 15 // initialize ant_direction = NULL index_point = 0 num_valles = 0 num_points = 0 Wait 15 seconds of valuable data. Use a drop-down window of 15 seconds. Indexing each point with point index the pending term refers to the slope of three prv points // main circuit rapid_aumento: highest positive slope during the last 5 seconds rapid_fall: highest negative slope during the last 5 seconds if (rapid_aumento > absolute (fast_fall)) then faster_change = fast_auto or change_more fast = absolute (fast fall) current_pendent = pending of the last three points prv [Start New Code] low_prv = lowest prv during the last 12 seconds high_prv + highest prv during the last 12 seconds [End New Code] // determine direction if (current_dependent> (faster_change * (PERCENTAGE_HIGH / 100))) then current_address = UP or if (current_dependent> (-1) * (faster_change) * (PERCENT_ADB / 100))) then. { if current_address = UP then current_address = PICO_MESETA or if current_address = DOWN then current_address = VALLE_MESETA} or current_address = DOWN point. pp = pp point. prv = 60000 / pp point. ts = current_time point. address = address [Start New Code] if (period, prv <low_prv) then period. position = 0 or if (point prv> high_prv) then point. position = 100 or point. position = ((point prv - low_prv) / (high_prov - low_prv)) * 100 [End New Code] update prv screen if (current-address! = ant_address). { [Start New Code] if ((current_address == UP) & (ant_direction = = DOWN) & (point. Position < 25)) then [End New Code]. { valley [num_valles + +] = lowest prv point and point [ant_direction_indice] if (num_valles > = 2) then. { last_wavelength = valley [num_valles-l] .ts - valley [num_valles-2] .ts Update Stress Index, Wavelength, History, Punctuation} ant_direction = UP ant_direction_index = index_point - 2 [Start New Code] if ((current_direction = DOWN) & (ant_direction = UP) &&(point position > 75)) then [End New Code]. { peak [num_pics + +] = highest prv point and point [ant_direction_index] if (num_pics > = 2) then last_pico_a_pico = peak [num-peaks-I]. ts - pico [num_pícos-2] .ts ant_direct = DOWN ant_direction_index = index_point - 2} yes ((current_address = UP) & (ant_address == NULL)) . { ant_address = UP ant_address_index = index_point - 2} yes ((current_address == DOWN) & (ant_direction = NULL)). { ant_dírección = DOWN ant_dirección_index = punto_index - 2} } The present invention also provides processes to determine both the point of fall and the conclusion of RSA waves in real time rather than on a pulse-by-pulse basis. Figures 84-87 describe two exemplary processes that run simultaneously. The first process, Process 1 in real time (in 6168) is carried out on a pulse-by-pulse basis. The second process, Process 2 in real time (in 6171), runs every 250 ms. The two processes work together to allow detection of the drop point in real time and detection of real-time termination of the real wave. The exemplary processes and flow processes illustrated in FIGS. 84 to 87, as well as any exemplary function for implementing said processes, including auxiliary functions and / processes called or utilized by said flow processes as an example, are presented for illustration purposes. . Those skilled in the art will recognize that each example process or function, either at a function level or called process, or at a general level for a whole process of superior, can be implemented in a variety of functionally equivalent ways, and the description of the Figures 84 to 87 below is in no way deduced as limiting the wide variety of possible implementations in systems or devices, or requiring that the exemplary illustrative process flow be followed literally. Taking this into account, for economy of expression as well as elegance of illustration, the process flow in each of the figures 84 to 87 will be described below without continuous reference to the exemplary nature of each step or step in the flow of the process; it should be understood that in the exemplary embodiments of the present invention, functionally equivalent implementations may, for example, use different processes, as well as different sequences of processes and organizations of process flow, of which are illustrated in figures 84 to 87, to achieve the equivalent functionalities. All alternative embodiments and functionally equivalent implementations are understood to be within the methods and techniques of the present invention. Figure 84 describes the switch nature of the two exemplary processes. Real-time process 1 starts at 6168. In the first step at 6169, the process waits for the next pulse to be received. When a pulse is received, the flow continues to 6170, where the pulse is handled by the Pulse Peak Management process. When this process returns, the flow continues back to 6169, where the process waits for the next pulse. Meanwhile, Process 2 in real time, which starts at 6171, operates simultaneously. The first step in this process in 6172 is to set the clock switch to 250 ms so this process is called every 250 ms. Then the flow continues to 6173 where the process sleeps until the clock switch occurs. When the clock switch occurs, the switch is operated by the Clock Switch Management process. When this process returns, the flow continues to 6172, where the clock switch is reset. Figure 85 describes a Peak Management process of Exemplary pulse. In the first step at 6176, the address is determined by the Get Address process. (The process of Get address and related procedures have been described in detail above, and therefore are not repeated here). After the process returns, the flow continues to 6177. In 6177 the process verifies if the address is UP. If so, then the flow continues to 6181. If it is not, then the flow continues to 6178. In 6178 the process verifies if the address is PICO_MESETA. If so, then the flow continues to 6182.

If it is not, then the flow continues to 6179. In 6179 the process verifies if the address is DOWN. If so, then the flow continues to 6183. If it is not, then the flow continues to 6180. In 6180, the Handle VALLE MESETA process handles the case in which the wave is currently IN DEPRESSION.

When this process returns, the flow continues to 6187. In 6181 the valley plateau indicator is set to false. Then the flow continues to 6184, where the indicator arrives is set to true. Then the flow continues to 6187. In 6182 the valley plateau indicator is set as false. Then the flow continues to 6185, where the indicator above is set to true. Then the flow continues to 6187. In 6183 the valley plateau indicator is set as loser. Then the flow continues to 6186 where the indicator above is set to false. Then the flow continues to 6187. In 6187 the process of Management of Pulse Peak. Figure 86 describes an exemplary Clock Handling switch process. In the first step in 6190 the process verifies if the indicator of the valley plateau is true. If it is then the flow continues to 6191. If not, then the flow continues to 6189. In 6189 the process verifies if the above indicator is true. If so, then the flow continues to 6193. If it is not, then the flow continues to 6199. In 6191 the process verifies whether the actual time has passed the end of the computed plateau. If so, then the flow continues to 6192. If it is not, then the flow continues to 6189.

In 6192 the actual address is registered as ABOVE. Therefore, the UP oscillation was detected in real time. As a result, the conclusion of the previous wave was detected in real time. If desired, wave delineation, stress measurements, parasympathetic metering, and the like can be computed, recorded, displayed, and more at this point. The flow then continues to 6189. In 6189 the process checks if the UP indicator is set to true.

If so, then the flow continues to 6193. If not, then the flow continues to 6199. In 6193 a phantom value prv is computed named tmp_prv. Then the flow continues to 6194 where the actual slope is computed based on the two previous real prv and the phantom prv. Then the flow continues to 6195. In 6195 the process checks if the current slope is less than -1 x PERCENT DOWN% of the fastest change. The computation of the fastest change has been described in previous examples. If the actual slope is lower, then the flow continues to 6196. If it is not smaller, then the flow continues to 6199. In 6196 the actual address is recorded as DOWN. In other words, the transition to DOWN was detected in real time. The process did not need to wait for the next pulse beat. The next pulse beat will occur after the point of fall. The flow continues to 6197 in which the above indicator is set to false. Then the flow continues to 6198 where the drop point can be processed. The point of fall can be indicated visually, aurally, or both. After the fall point information is used, the flow continues to 6199. In 6199 the Clock Handling Switch process returns. Figure 87 describes an exemplary VALLE PLATE management process. In the first step in 6201, the above indicator is set to false. Then the flow continues to 6202.

In 6202 the process verifies if the plateau valley indicator is false. If it is false, then the flow continues to 6203. If it is not false, then the flow continues to 6212. In 6203 the process checks if the previous point is a lower one. If it is, then the flow continues to 6204. If it is not, then the flow continues to 6206. In 6204 the time stamp of the previous point is recorded according to the term of the plateau. Then the flow continues to 6208. In 6206 the process verifies if the previous point is an ascending transition point. If it is, then the flow continues to 6205. If it is not then the flow continues to 6207.

In 6205 the time stamp of two points is recorded as the end of the plateau. Then the flow continues to 6208. In 6207 the time stamp of the current point is recorded as the end of the plateau. Then the flow continues to 6208. In 6208 the process verifies whether the time stamp of the last known valley is less than the time stamp of the last known peak. If so, then the flow continues to 6209. If not, then the flow continues to 6210. In 6209 a third of the time between the last peak and the last valley is added to the end of the plateau. Then the flow continues to 6211. In 6210 a third of the time between the last peak and the second to last valley is added to the end of the plateau. Then the flow continues to 6211. In 6211 the valley plateau indicator is set to true. Then the flow continues to 6212. In 6212 the process of Handling VALLE TABLES returns. Exemplary procedures for determining, for example, both the drop point and the RSA wave termination in real time can also be implemented using the pseudo codes substantially corresponding to the flow processes illustrated in Figures 84-87. // index_point = the index of the last point < per pp > change (address) case: TOP valle_meseta_indicador = FALSE; arpba_ind¡cador = TRUE; case: PICO_MESETA valle_meseta_indicador = FALSE; up_indicator = TRUE; case: DOWN valle_meseta_indicador = FALSE; up_indicator = FALSE; case: VALLE_MESETA arriba_indicador = FALSE; yes (lvalle_meseta_indicator). { if (if_inferior_pt (index_point - I)) then the plate_end = point [index_point-1] .ts // use the midpoint yes (is_ascent_trans (index_point - I)) then the plate_index = point [index_point-2] .ts / / use the first point si (valle [valle-valle-l] .ts < peak [num_picos-l] .ts) then plate_extremo + = (1/3) * (peak [num_picos-l] .ts - valle [ num_valles- 1] .ts) // last peak - last valley before the peak or else plateau_extreme + = (1/3) * (peak [num_leaks-l] .ts - valley [num_valles- 2] .ts) valle_meseta_indicator = TRUE; } < every 250ms > yes (valle_meseta_indicator) then. { if (real_time> extreme_table) then the current_address = UP } yes (above_indicator) then . { tmp_prv = (60000 / (real_time -point [num_points-l] .ts)) pending_current = pending the last two prvs & tmp_prv yes (current_dependent <(-1) * (change_more fast) * (DOWN_ PERCENT / 100)) then. { Current address = DOWN indicator up = false Go to display screen update.

} } The present invention has been described with reference to particular embodiments. However, it should be noted that variations and modifications can be made without departing from the spirit and scope of the invention. In particular, it should be appreciated that the various flow processes described herein may be modified to provide substantially equivalent functional implementations and as such is understood to be within the spirit and scope of the invention.

Claims

CLAIMS 1. A portable, hand-held biofeedback device for reducing stress in a human subject comprising: a housing: a PPG sensor, in which the PPG sensor generates information from the human subject; a control system fitted with the PPG sensor sensor; and a display screen, in which the control system is configured to process data of the human subject for output on the display screen, in which the output data provide the human subject with information related to the drop point of at least a wave of RSA. 2. The device according to claim 1, wherein the information is visual. 3. The device according to claim 1, wherein the information is auditory. 4. The device according to claim 1, wherein the information is used to request the subject to begin the exhalation. 5. The device according to claim 1, wherein the information is provided to the subject in substantially real time. The device according to claim 1, wherein the device further comprises a breathing metronome capable of being activated by a subject, wherein the breathing metronome is programmed to deactivate after a predetermined period of time. The device according to claim 1, wherein the device is configured to extract information related to the breathing of a subject. 8. The device in accordance with the claim 7, in which information related to breathing includes frequency, rhythm and volume. 9. The device in accordance with the claim 1, that the accommodation includes a power source. 10. The device in accordance with the claim 1, where the energy is provided by means of an A / C source. 11. A method for generating a parasympathetic outflow in a human subject that consists of providing the subject with information about a drop point of at least one RSA wave. 12. The method according to claim 11, wherein the information is visual. 13. The method according to claim 11, wherein the information is auditory. 14. The method according to claim 11, wherein the information is used to request the subject to begin to exhale. 15. The method according to claim 11, wherein the information is provided to the subject in substantially real time. 16. A portable, hand-held biofeedback device for reducing stress in a human subject comprising: (a) a housing; (b) a PPG sensor, in which the PPG sensor generates data from the human subject; (c) a control system coupled to the PPG sensor; and (d) a visualization screen, wherein said device functions while held between the thumb and index finger of the subject. The device according to claim 16, wherein the PPG sensor is configured to contact the index finger of the subject. 18. The device according to claim 17, wherein the control system is configured to process data of the human subject for output to the display screen, in which the output data provide the human subject with information related to the level of the subject's stress. The device according to claim 17, wherein the control system is configured to process data of the human subject for output to the display screen, in which the output data provide the human subject with information related to the point of fall of at least one RSA wave; 20. The device according to claim 19, wherein the information is visual. 21. The device according to claim 19, wherein the information is auditory. 22. The device in accordance with the claim 19, in which the information is used to request the subject to start the exhalation. 23. The device according to claim 19, wherein the information is provided to the subject in substantially real time. The device according to claim 19, wherein the device further comprises a breathing metronome capable of being activated by a subject, wherein the breathing metronome is programmed to deactivate after a predetermined period of time. 25. The device according to claim 19, wherein the device is configured to extract information related to the breathing of a subject. 26. The device according to claim 19, wherein the information related to respiration includes frequency, rhythm and volume. 27. The device according to claim 19, wherein the housing includes a power source. 28. The device according to claim 19, wherein the energy is provided by means of an A / C source.