Diagnostic system for determining a clinically relevant severity of a sleep-related breathing disturbance from a patient signal by weighting respiratory events with respiratory events that are close in time

Abstract

The present invention relates to a diagnostic system for determining a clinically relevant severity (S4) of a sleep-related breathing disturbance from a patient signal of a sleeping patient, comprising: a sensor unit (1) which captures a patient parameter and generates a patient signal (S1); a first detector unit (2) which detects respiratory events in the patient signal S1 and generates a corresponding respiratory event signal (S2) therefrom; and a second detector unit (3) which comprises the following: a weighting determination unit (3a) which detects, in the respiratory event signal (S2) at a particular respiratory event (REx) within a time window (ZFx) in which the respiratory event (REx) takes place, respiratory events (REx) that are close in time and, depending on a number and / or energy of the particular respiratory events (REx) that are close in time, generates a weighting signal (G); a weighting unit (3b) which weights the respiratory event signal (S2) with a weighting signal (G); and a severity determining unit (3c) which determines the clinically relevant severity (S4) from an integral via the weighted respiratory event signal (S3).

Description

[0001] Diagnostic system for determining a clinically relevant severity of a sleep-disordered breathing disorder from a patient signal by weighting respiratory events with neighboring respiratory events.

[0002] The present invention relates to a method for determining a clinically relevant severity of a sleep-related breathing disorder from a patient signal, according to the preamble of claim 1.

[0003] The clinically relevant severity of sleep-related breathing disorders is currently determined based on the number of apneas and hypopneas divided by the measurement time in hours. Apneas are defined by a reduction in airflow of at least 90% of the baseline value for at least 10 seconds. Hypopneas are defined by a reduction in airflow of at least 30% of the baseline value for at least 10 seconds combined with a reduction in oxygen saturation of at least 3%. Other definitions require a reduction in oxygen saturation of at least 4% or the simultaneous occurrence of an arousal. Apneas and hypopneas are summarized below as respiratory events. The measurement time is preferably the period from the beginning to the end of the measurement, without taking sleep-wake phases into account. Alternatively, the measurement time is preferably the patient's sleep time.

[0004] The clinically relevant severity of sleep-disordered breathing is usually defined in the state of the art as follows: a) mild sleep apnea with 5-15 apneas or hypopneas per hour of measurement time; b) moderate sleep apnea with 15-30 apneas or hypopneas per hour of measurement time; c) severe sleep apnea with more than 30 apneas or hypopneas per hour of measurement time.

[0005] The clinically relevant severity can be determined in segments or over the entire measurement period. Alternatively or additionally, the severity can also have multiple severity values, for example, determined over different periods of the measurement period.

[0006] To determine the patient's breathing and breathing disorder, a sensor unit records a patient signal representing at least one of the patient's physiological parameters. The patient signal is preferably a microphone signal representing the patient's breathing. The patient signal can also include other and / or additional measurement signals, such as an SpO2 signal from a pulse oximeter, a chest strap signal, and / or a motion sensor signal.

[0007] Typically, an intensity signal is generated from the patient signal, which is, for example, an effective value of the patient signal. The intensity signal is then fed to a threshold detector, which detects respiratory events when the intensity signal falls below a predetermined threshold. The predetermined threshold can be determined, for example, by measuring a periodic amplitude of the intensity signal during an undisturbed sleep period and determining from this the minimum amplitude below which the intensity signal normally does not fall. The predetermined threshold can then be set equal to the minimum amplitude multiplied by a tolerance factor less than 1.

[0008] AT 520 925 A4 discloses a method for detecting a patient's breathing pauses. During the patient's sleep, a respiratory movement measurement of the patient's movements and a patient's oxygen saturation value are determined and correlated. A breathing pause is detected when the respiratory movement measurement decreases and the oxygen saturation value also decreases.

[0009] US 2012_0 071 741 A1 discloses a method for determining an apnea / hypopnea index, in the determination of which both the respective breathing and snoring sounds as well as the respective oxygen saturation value of the patient are taken into account.

[0010] US 2024_0 008 765 A1 discloses a method for determining apneas, wherein the respective ECG signals and their temporal course are evaluated and taken into account in the determination.

[0011] Especially with automatic determination of respiratory events using only one or only a few physiological parameters, respiratory events are often incorrectly counted or even not detected, so that the resulting determination of the severity of the patient's sleep-disordered breathing is also subject to errors. To counter this, in some prior art embodiments, the predetermined threshold for the threshold detector is determined in a complicated way as a respective prediction value. Furthermore, influences such as ambient noise can impair the determination of clinical severity from acoustically determined physiological parameters.The object of the invention, in order to eliminate the disadvantages of the prior art, is therefore to provide a diagnostic system that detects a clinically relevant severity of a sleep-related breathing disorder from a patient signal as accurately and robustly as possible.

[0012] The above object is achieved by a diagnostic system according to the features of independent claim 1. Further advantageous embodiments of the invention are specified in the dependent claims.

[0013] According to the invention, a diagnostic system is provided for determining a clinically relevant severity of a sleep-related breathing disorder from a patient signal of a sleeping patient, comprising the following: a) a sensor unit that detects at least one patient parameter and generates a corresponding patient signal therefrom; b) a first detector unit with a first signal processing unit that generates an intensity signal from the patient signal, and with a second signal processing unit that detects respiratory events in the intensity signal and generates a corresponding respiratory event signal; and c) a second detector unit that is designed to determine the clinically relevant severity from the respiratory event signal; d) wherein the second detector unit has the following: d1) a weighting determination unit that is designedto detect the respiratory events in the respiratory event signal and to determine an associated weighting signal for each respiratory event by determining neighboring respiratory events for the respective respiratory event within a corresponding predetermined time window in which the respective respiratory event also lies, and the associated weighting signal is generated as a function of and at least partially increasing with a number and / or an energy of the respective neighboring respiratory events; d2) a weighting unit that weights the respiratory event signal with the weighting signal and generates therefrom a weighted respiratory event signal with correspondingly weighted respiratory events; and d3) a severity determination unit,which determines the clinically relevant severity as an integral value over the weighted respiratory event signal. A particularly advantageous feature is that the respiratory events are weighted prior to further evaluation for clinically relevant severity in such a way that isolated and / or low-energy detected respiratory events are given a low weighting and contribute little to the clinically relevant severity, whereas respiratory events that occur frequently and / or have higher energy within the time window are given a higher weighting and therefore contribute more to the clinically relevant severity. In this way, falsely detected respiratory events can be effectively attenuated or suppressed as artifacts, which usually occur sporadically, whereas a larger number of detected events that occur in closer temporal successionmore deeply into determining the clinically relevant severity of the respiratory disturbance. The occurrence of a rapid sequence of artifacts is generally rare.

[0014] Due to the weighting of the respiratory events, the method according to the invention is robust against disturbances in the correct detection of the respiratory events, whereby isolated false detections have a correspondingly small impact on the clinically relevant severity.

[0015] Preferably, the weighting determination unit is configured to shift and arrange the respective predetermined time window for the respective respiratory event on the time axis such that the greatest possible number of neighboring respiratory events lie at least partially within the time window. Preferably, the time window is shifted from an event time of the respective respiratory event on the time axis so far to the left and then repetitively incrementally to the right until the event time still lies within the time window, wherein the number of neighboring respiratory events that lie entirely or at least partially within the time window is determined in each case. The largest number of neighboring respiratory events in the respective time window is then used to determine the weighting signal for the respective event time.It is understood that for each arranged time window, the respective number of neighboring respiratory events is first stored and then, across all possible time windows that include the respective respiratory event, the maximum number of these is used to determine the weighting signal.

[0016] Alternatively, the weighting determination unit is preferably configured to shift and arrange the respective predetermined time window for the respective respiratory event on the time axis such that the greatest possible energy of the respective respiratory event and the neighboring respiratory events is present in the time window. Preferably, the time window is shifted from the time of the respective respiratory event on the time axis so far to the left and then repetitively shifted piece by piece to the right that the event time still lies within the time window, wherein the energy of the respiratory event and the neighboring respiratory events that lie entirely or at least partially within the time window is determined. The greatest determined energy is then used to determine the weighting signal for the respective event time.

[0017] For clarity, the energy of the respective respiratory event is the respective product of the amplitude and duration of the respiratory event signal over the respective respiratory event. The energy of the respiratory event and the neighboring respiratory events is understood to be the sum of all energy components of the respiratory events in the respiratory event signal that are located in the respective time window. For binary amplitude curves of the respiratory event signal, in which a respiratory event is either present or not, the energy component is determined from the amplitude, which is 1 or 0, multiplied by the time in the respective time window. In the case of binary amplitude, the energy is representative of the time duration.The respiratory event signal can also be configured with a variable amplitude, which can assume a variety of discrete values, in order to reflect the varying severity of the respective respiratory event. The energy of the respiratory event signal often represents a good indicator of the robustness of the detection of an actual respiratory event. In other words, the greater the energy of the respective respiratory event signal, the lower the probability that the detected event is an artifact and thus a false detection.

[0018] Particularly advantageous is the consideration of the energy of the respiratory event and the neighboring respiratory events in the respective time window for determining the weighting signal, whereby the clinically relevant severity is closely correlated with a GDI (Oxygen Desaturation Index). The clinically relevant severity determined in this way therefore represents a good measure of the severity of oxygen desaturation during sleep, which is clinically relevant.

[0019] In contrast, a clinically relevant severity, which is determined based on the number of the respective respiratory events and the respective neighboring respiratory events, is more closely correlated with an AHI index, also called the apnea-hypopnea index. Alternatively, the weighting determination unit is preferably designed to shift the predetermined time window as a sliding time window along a time axis of the respiratory event signal, thereby determining the number of all respiratory events at least partially contained therein and mapping the number in the weighting signal at a predetermined time within the time window. For clarity, the predetermined time within the time window is a predetermined point in time with respect to the respective time window, such as at the beginning, in the middle, or at the end of the respective time window.Preferably, the number is represented as a value in the weighting signal in the center of the time window. The number is preferably an integer value; alternatively, the number can also be a decimal value.

[0020] Alternatively, the weighting determination unit is preferably designed to shift the predetermined time window as a sliding time window along the time axis of the respiratory event signal and, in doing so, to form an integral value over the energy of all respiratory events at least partially contained therein and to map the integral value in the weighting signal at the predetermined time within the time window.

[0021] The weighting determination unit is preferably designed to determine the weighting signal as a function of the number of respective respiratory events in the respiratory event signal within the predetermined time window such that the weighting signal increases with the number of respiratory events contained in the time window. The weighting signal preferably increases within a predetermined range of the number of respective respiratory events. The weighting signal can be formed as a linear or non-linear function of the number of respiratory events. For example, a lower number threshold can be provided, below which the weighting function is set to zero or a minimum value. Likewise, an upper number threshold can be provided, above which the weighting function is limited.Preferably, the weighting signal is generated as a function of the number of respective adjacent respiratory events in a predominant value range, increasing with the number of respective adjacent respiratory events. The predominant value range is preferably a value range that covers at least 50% of an entire value range of the numbers of respiratory events, or that covers a frequency range of the numbers that constitute at least 50% of all numbers.

[0022] Preferably, the weighting determination unit is designed to determine the weighting signal as a function of the energy of the respective respiratory event and the neighboring respiratory events within the predetermined time window such that the weighting signal increases with the energy. Preferably, the weighting signal increases within a predetermined range of the energy of the neighboring respiratory events. The weighting signal can be formed as a linear or non-linear function of the energy of the respiratory events. For example, a lower energy threshold can be provided, below which the weighting function is set to zero or a minimum value. Likewise, an upper energy threshold can be provided, above which the weighting function is limited.Preferably, the weighting signal is generated as a function of the energy of the respective adjacent respiratory events in a predominant value range that increases with the energy of the respective adjacent respiratory events. The predominant value range is preferably a value range that covers at least 50% of the total energy value range of the respiratory events, or that covers a frequency range of energies that constitute at least 50% of all possible energies.

[0023] The clinically relevant severity is preferably determined by the second detector unit using a corresponding correction or calibration factor. The severity determination unit preferably applies the correction or calibration factor to the clinically relevant severity. However, it is also conceivable for the weighting determination unit to take the correction factor into account in the weighting signal. It is also conceivable to determine a function of the weighting signal as a function of the respective number and / or energy in such a way that the clinically relevant severity comes closest to an actual clinically relevant severity determined by specialist personnel. The weighting signal could, for example, be a function of a first factor multiplied by the respective number plus a second factor multiplied by the respective energy.Optimization of the first and second factors, for example by an LMS method, also called Least Mean Square method, can be viewed as a correction that is applied to the weighting signal, bringing the clinical severity closer to the actual severity.

[0024] Preferably, the correction or calibration factor is determined based on studies with reference values ​​of clinically relevant severity determined by experts in such a way that the clinically relevant severity of the diagnostic system is statistically as close as possible to the respective reference values, and the clinically relevant severity determined by the diagnostic system is therefore preferably close to either the respective ODI or AHI value. Although the ODI value could, for example, be determined with an oxygen oximeter with corresponding equipment expenditure, the present invention, as explained above, enables the ODI value to be determined much more easily, for example via a microphone signal rather than the patient signal. The microphone signal only needs to be recorded, for example, by a mobile phone and transmitted to the diagnostic system in order to determine the corresponding clinically relevant severity of the sleep-related breathing disorder.The sensor unit can be part of an integral diagnostic system or it can be a remote part that is connected to the rest of the diagnostic system, for example wirelessly.

[0025] Preferably, the severity determination unit determines the clinically relevant severity as an integral value over the weighted respiratory event signal in conjunction with a predetermined correction factor.

[0026] Preferably, the correction factor is predetermined and trained as an average from a plurality of respective correction factors by multiplying a plurality of respective integral values ​​from the weighted respiratory event signals from training data sets by the respective correction factors and equating them with the respective actual clinically relevant severities determined by the specialist. The respective correction factors can be determined by forming a respective quotient. The correction factor is preferably determined using data from a clinical study of sleep patients, with the actual clinically relevant severity being determined by a specialist.

[0027] A particularly advantageous feature of the method according to the invention, it should be noted again, is the simplicity of acquiring the patient signal, for example, as a microphone signal, which can be captured at the patient's home using a mobile phone and transmitted to the diagnostic system. Thus, the clinically relevant severity of the sleep-related breathing disorder can be determined easily from home without having to visit a sleep laboratory. Interference in the patient signal that could lead to incorrect detection of respiratory events can be largely suppressed by weighting.

[0028] Preferred embodiments according to the present invention are illustrated in the following drawings and in a detailed description, but they are not intended to limit the present invention exclusively thereto.

[0029] Fig. 1 shows a preferred diagnostic system for determining a clinically relevant severity of a sleep-related breathing disorder of a patient who generates breathing sounds that are detected by a sensor unit and converted into a patient signal, with a first detector unit that detects respiratory events in the patient signal and generates a corresponding respiratory event signal therefrom, and a second detector unit that determines the clinically relevant severity of the sleep-related breathing disorder from the respiratory event signal;

[0030] Fig. 2 shows a preferred embodiment of the first detector with a first signal processing unit that generates an intensity signal from the patient signal, and with a second signal processing unit that detects the respiratory events in the intensity signal and outputs them as a respiratory event signal;

[0031] Fig. 3 shows a graph of an exemplary intensity signal over time, with a dash-dotted prediction signal of the patient signal in an undisturbed case and a dashed upper and lower threshold signal generated from the prediction signal for threshold detection of the respective respiratory events; and, below, a resulting respiratory event signal generated by the upper intensity signal with the lower and upper threshold signals.

[0032] Fig. 4 shows a preferred embodiment of the second detector with a weighting determination unit that generates a weighting signal from the respiratory event signal, with a weighting unit that weights the respiratory event signal with the weighting signal and generates a weighted respiratory event signal therefrom, and with a severity determination unit that determines the clinically relevant severity of the sleep-related breathing disorder from the weighted respiratory event signal;

[0033] Fig. 5 shows an upper diagram with an exemplary respiratory event signal over time with various dashed time windows; a middle diagram with a weighting signal generated by a preferred weighting determination, which is proportional to a maximum number of respiratory events that can be captured in a respective time window; and a lower diagram illustrating the weighted respiratory event signal as it would be preferentially generated by the weighting unit.

[0034] Fig. 6 shows an upper diagram with the exemplary respiratory event signal over time with various dashed time windows sliding over time; a middle diagram with a different weighting signal generated by a different preferred weighting determination, which is determined from the respiratory event signal proportional to a number of respiratory events in a respective fixed time window; and a lower diagram representing the weighted respiratory event signal as it would be preferentially generated by the weighting unit.

[0035] Fig. 7 shows an upper diagram with the exemplary respiratory event signal over time with various dashed time windows; a middle diagram with a further weighting signal generated by a further preferred weighting determination, which is proportional to an energy of the respiratory event signals that can be captured by a respective time window, and a lower diagram illustrating the weighted respiratory event signal as it would be preferentially generated by the further weighting unit.

[0036] Detailed description of implementation examples

[0037] Fig. 1 schematically shows a preferred diagnostic system for determining a clinically relevant severity S4 of a sleep-related breathing disorder from a patient signal of a sleeping patient. The diagnostic system comprises the following: a) a sensor unit 1, which records at least one patient parameter and generates a corresponding patient signal S1 therefrom; b) a first detector unit 2 with a first signal processing unit 2a and a second signal processing unit 2b, as shown by way of example in Fig. 2. The first signal processing unit 2a generates an intensity signal S1' from the patient signal S1, which is, for example, an effective value signal or an averaged rectified signal. The second signal processing unit 2b is designed to detect respiratory events Rex in the intensity signal ST and to generate a corresponding respiratory event signal therefrom.

[0038] 52; and c) a second detector unit 3, which is designed to determine the clinically relevant severity S4 from the respiratory event signal S2; d) wherein the second detector unit 3 according to the invention has the following: d1) a weighting determination unit 3a, which is designed to detect the respiratory events Rex in the respiratory event signal S2 and to determine an associated weighting signal G for each respiratory event RE by determining neighboring respiratory events Rex for the respective respiratory event Rex within an associated predetermined time window ZFx, in which the respective respiratory event Rex also lies, wherein the associated weighting signal G is generated as a function depending on and at least partially increasing with a number and / or an energy of the respective neighboring respiratory events Rex;d2) 3 a weighting unit 3b which weights the respiratory event signal S2 with the weighting signal G and therefrom generates a weighted respiratory event signal;

[0039] 53 with correspondingly weighted respiratory events; and d3) a severity determination unit 3c, which determines and preferably outputs the clinically relevant severity S4 as an integral value over the weighted respiratory event signal S3. Preferably, the integral value is determined via an integral of the weighted respiratory event signal S3 over a measurement time divided by the measurement time in hours.

[0040] Preferably, the sensor unit 1 is a microphone, a microphone array, and / or another sensor unit 1, as known from the prior art. The patient signal S1 can comprise a single signal or multiple signals. The at least one microphone is preferably an airborne sound microphone for recording the patient's breathing sounds and, if applicable, snoring sounds and at least partially converting them into the patient signal.

[0041] As schematically illustrated in Fig. 2, the first signal processing unit 2a generates the intensity signal ST from the patient signal S1 as an effective value signal or as a low-pass filtered rectified patient signal. The first signal processing unit 2a may also contain nonlinear components for processing the patient signal S1, as known from the prior art. Preferably, the first signal processing unit 2a averages the intensity signal ST over a predetermined time constant, such as over 100-200 ms or over 200-400 ms. Further preferably, the first signal processing unit 2a is configured to linearly connect adjacent local maxima in the averaged intensity signal.Even more preferably, the first signal processing unit 2a is configured to always maintain the intensity signal ST at a current hold value until a predetermined decay hold time if the intensity signal determined via the time constant would otherwise be smaller than the hold value. If the intensity signal determined via the time constant exceeds the hold value, the output intensity signal ST again corresponds to the intensity signal determined via the time constant. The decay hold time is set, for example, to 0.2-0.4 s, 0.4-1 s, or 1-3 s.

[0042] The second signal processing 2b detects the respiratory events Rex, for example, using a threshold detector with a lower threshold signal S1"u, which is designed to detect whether the intensity signal ST falls below the lower threshold signal, whereby the corresponding respiratory event signal S2 is generated, as shown by way of example in Fig. 3. The lower threshold signal S1"u is preferably derived from a prediction signal S1" of the intensity signal ST. The prediction signal S1" is generated such that it represents an undisturbed course of the intensity signal ST, as is known from signal predictors known in the prior art. One possible predictor is, for example, simplified averaging, such as a median, over the intensity signal ST over a previous period.Preferably, when determining the prediction signal S1", outliers of the intensity signal ST that exceed a predetermined variance are not taken into account. An undisturbed course of the intensity signal ST means that the intensity signal ST moves around a mean value within the variance fluctuation range, without smaller or larger intensity signal values ​​that are likely to be correlated with a respiratory disorder. The lower threshold signal S1"u can, for example, be generated from the prediction signal S1" multiplied by a predetermined factor less than 1. The lower threshold signal S1"u can also be determined by a respective minimum value of the intensity signal ST, which is determined over a preceding predetermined period of time. The preceding period can, for example, be 1 - 7 minutes.Predictor algorithms, adaptive filters, neural networks and the like are well known in the art for determining the prediction signal S1" and the lower S1"u and the upper threshold signal S1"o, which can be used here.

[0043] It is conceivable for the threshold detector to evaluate an upper threshold signal S1"o by detecting whether the intensity signal S1' exceeds the upper threshold signal S1"o, whereby a respiratory event signal could also be generated. The upper threshold signal S1"o can, for example, be generated from the prediction signal S1" multiplied by a predetermined factor greater than 1. In Fig. 3, an intensity signal S1' is shown as an example with a dash-dotted prediction signal S1" and a lower S1"u and upper threshold signal S1"o. The respiratory event signal S2 shown below becomes positive when the lower threshold signal S1"u is undershot and when the upper threshold signal S1"o is exceeded, whereby a first RE1, a second RE2, a third RE3 and a fourth respiratory event RE4 are shown in the respiratory event signal S2.

[0044] The second signal processing unit can also be configured to suppress respiratory events that are shorter than a predetermined minimum time, or to prevent them from being generated at all, so that they are not represented in the resulting respiratory event signal. This minimum time is preferably 10 seconds. For clarity, the term "represented" also means "output as a signal component." Other first detector units 2 from the prior art for generating the respiratory event signal S2 are also conceivable.

[0045] Preferably, the sensor unit 1 is part of a mobile radio device that can wirelessly transmit the patient signal S1 to the first detector unit 2. It is also conceivable for part of the first detector unit 2 and / or part of the second detector unit 3 to be part of the mobile radio device. Preferably, the second detector unit 3 is at least partially implemented on a stationary microcontroller-supported system, wherein the clinically relevant severity S4 can preferably be sent back to the mobile radio device as an evaluation signal.

[0046] Fig. 4 shows a preferred embodiment of the second detector 3, comprising a weighting determination unit 3a which generates a weighting signal G from the respiratory event signal S2. A weighting unit 3b then generates a weighted respiratory event signal S3 as a function of the respiratory event signal S2 and the weighting signal G. The weighting unit 3b, which is preferably designed as a multiplication unit, generates the weighted respiratory event signal S3 preferably from the respiratory event signal S2 multiplied by the weighting signal G. A severity determination unit preferably evaluates the weighted respiratory event signal S3 by summing the respiratory event signal S3 and dividing it by a measurement time over the summed respiratory event signal S3.The measurement time is preferably specified in hours, with the clinically relevant severity of the sleep-disordered breathing disorder being determined as the number of respiratory events per hour. The clinically relevant severity is preferably calculated repetitively over a progressive measurement period. It is also conceivable to determine and preferably display the clinically relevant severity over one or several measurement periods that are part of an overall measurement period.

[0047] Fig. 5 shows a determination of the weighted respiratory event signal S3, in which the weighting determination unit 3a is preferably designed to shift and arrange the respective predetermined time window ZFx for the respective respiratory event REx on the time axis in such a way that the greatest possible number of neighboring respiratory events REx lies entirely or at least partially within the time window ZFx. For the first respiratory event RE1, for example, a first time window ZF1 is arranged in such a way that it captures the first RE1, the second RE2, the third RE3, and the fourth respiratory event RE4, resulting in a number of 4, which is represented in the correspondingly generated weighting signal G.Up to a second time window ZF2, the four respiratory events RE1-RE4 can still be captured from a respective time window ZFx, so that up to a time at the end of the second time window ZF2, the weighting function is 4. From the second to a third time window ZF3, only the second RE2 to the fourth respiratory event RE4 are in a respective time window; the number and weighting value G is therefore 3 over this time period. With a fourth time window ZF4, five respiratory events can be captured; the number and weighting value is 5. At a time point with the respiratory event REx, no neighboring respiratory event can be captured; therefore, the weighting signal G at this time point is 1.The resulting weighted respiratory event signal S3 is shown at the bottom of the image, with the resulting first weighted REg1 and the fourth weighted REg4 having the value 4, which corresponds to the corresponding weighting signal G. It can be seen that isolated respiratory events are less significant when summed across the respiratory events and therefore contribute less to the clinically relevant severity. As previously mentioned, the correction or calibration factor can also be included in the weighting signal G.

[0048] Alternatively, the weighting determination unit 3a can be designed to shift and arrange the respective predetermined time window ZFx for the respective respiratory event REx on the time axis such that the greatest possible energy of the respective respiratory event REx and the neighboring respiratory events REx are captured in the time window ZFx. Fig. 6 shows another determination of the weighted respiratory event signal S3, in which the weighting determination unit 3a is preferably designed to shift the predetermined time window ZFx as a sliding time window along a time axis t of the respiratory event signal S2 and, in doing so, to determine the number of all respiratory events REx contained entirely or at least partially in the time window ZFx and to map the number in the weighting signal G at a predetermined time within the time window ZFx.The respective time window ZFx has a fixed reference to a point in time on the time axis t of the weighting signal G, for example in that the time window lies half or a whole time window length before the respective point in time on the time axis t of the weighting signal G. In contrast, in the method according to Fig. 5, the time window ZFx is shifted back and forth as long as the point in time is still within the time window ZFx, whereby the number and / or the energy of the respiratory event signal is determined in each different time window around the point in time on the time axis t of the weighting signal G.

[0049] In the example of Fig. 6, the time window is centered around the respective time point and is thus offset by half the time window length around the time point. The first RE1, the second RE2, the third RE3, and the fourth respiratory event RE4 are located at least partially in the first time window ZF1, with the number of respiratory events being 4 and the corresponding weighting signal G being determined accordingly with the value 4. Accordingly, the weighting signal G is determined as a sum of the respective number of respiratory events REx in the respective time window ZFx.Below, the resulting weighted respiratory event signal S3 is shown, where the first weighted respiratory event signal REg1 is partially weighted with a value of 3 and partially with the value 4; the fourth respiratory event RE4 is generated according to the weighting signal G as a weighted respiratory event signal REg4 partially weighted with the value 4, partially with the value 3, and partially with the value 2. An integral formation when determining the clinically relevant severity takes into account such weighted respiratory event signals S3 that change over time.

[0050] Fig. 7 shows a preferred determination of the weighted respiratory event signal S3, in which the weighting determination unit 3a is designed to shift the predetermined time window ZFx as a sliding time window along the time axis t of the respiratory event signal S2 and, in doing so, to form an integral value over the energy of all respiratory events REx at least partially contained therein and to map the integral value in the weighting signal G at the predetermined time within the time window ZFx. Preferably, the predetermined time within the time window ZFx is at the beginning, in the middle, or at the end of the time window. In other words, the time window ZFx is arranged by half a time window length to the left, right, or centered at the predetermined time.It is also conceivable that during the respective time window ZFx both the energy and the number of respiratory events in the time window are evaluated and the weighting signal is determined accordingly depending on the energy and the number of respiratory events.

[0051] Preferably, the weighting determination unit 3a is designed to determine the weighting signal G by a function such that the weighting signal G increases with the number and / or the energy of the associated respiratory event REx within the predetermined time window ZFx.

[0052] The severity determination unit 3c preferably determines the clinically relevant severity S4 as an integral value over the weighted respiratory event signal S3 in conjunction with a predetermined correction factor. The correction factor is preferably predetermined or trained as an average value from a plurality of respective correction factors by multiplying a plurality of respective integral values ​​from the weighted respiratory event signals S3 from training data sets by the respective correction factors and equating them with the respective actual clinically relevant severities S4, which a person skilled in the art has determined from the respiratory event signals S2 with the respective respiratory events REx.The respective correction factors can be determined by forming a respective quotient, from which an averaged or otherwise determined final correction factor can be determined, which is implemented in the diagnostic system, such as in the severity determination unit 3c.

[0053] Preferably, the diagnostic system also comprises an output unit, such as a monitor, which displays the clinically relevant severity.

[0054] For clarity, the weighting signal G is shown to be temporally synchronous with the respiratory event signal S2. The respective respiratory event REx, to which the respective time window refers, always lies at least partially within the time window. The time window preferably begins half a time window duration before the middle of the time window and ends half a time window duration after the middle of the time window. The time window has a predetermined length of preferably 1-5 minutes or 5-10 minutes.

[0055] The weighting signal G, the patient signal S1, the intensity signal ST, the prediction signal of the patient signal S1", the lower threshold signal S1", the upper threshold signal S1", the respiratory event signal S2, and the weighted respiratory event signal S3 are preferably all variable signals dependent on time t. The clinically relevant severity S4 can be determined and output as a single value at one end of the measurement time; however, it is also conceivable that the clinically relevant severity is determined in segments over time t and is thus also time-dependent.

[0056] Weighting of the respiratory event signal S2 with the weighting signal G can be understood as a multiplication or any other linear or non-linear function of the respiratory event signal S2 and the weighting signal G. Weighting functions known in the art can be applied, preferably linear functions or quadratic functions.

[0057] Recognition means detection.

[0058] For the sake of clarity, the terms “top” and “bottom” are understood to mean relative locations in a vertical direction, as shown in the figures.

[0059] For clarity, it should also be noted that indefinite articles in connection with an object or numerical expressions, such as "ein" Objekt, do not limit the object numerically to exactly one object, but rather mean that at least "one" object is involved. This applies to all indefinite articles such as "ein," "eine," etc.

[0060] It is understood that if an element is described as “on 1 attached to another element, to be ‘connected’, ‘coupled’ or ‘in contact’ with it, the element may then be located directly on the other element, connected or coupled with it, or there may also be intermediate elements which either only lie between them or connect or couple the element to the other element or keep it in contact. On the other hand, if an element is described as ‘directly on 1another element, thereby referring to being "directly connected," "directly coupled," or "directly in contact," it is understood that no intervening elements are present. Similarly, when a first element is referred to as being "in electrical contact" with a second element, or thus "electrically coupled," an electrical path is present that allows current to flow between the first element and the second element. The electrical path may include capacitors, coupled inductors, and / or other elements that allow current to flow even without direct contact between the conductive elements.

[0061] Although the terms "first," "second," etc., may be used herein to refer to various elements, components, regions, and / or sections, these elements, components, regions, and / or sections are not limited by these terms. The terms are used only to distinguish one element, component, region, or section from another element, component, region, or section. Therefore, a first element, component, region, or section discussed below may be referred to as a second element, component, region, or section without departing from the teachings of the present invention.

[0062] Embodiments of the invention are described herein with reference to cross-sectional views that are schematic representations of embodiments of the invention. Therefore, the actual thickness of the components may differ therefrom, and deviations from the shapes in the representations, for example, due to manufacturing processes and / or tolerances, are to be expected. Embodiments of the invention are not to be understood as limited to the specific shapes of the regions illustrated herein, but are intended to include variations in the shapes resulting, for example, from the nature of the manufacturing. A region illustrated or referred to as square or rectangular typically also has rounded or curved features due to normal manufacturing tolerances.Therefore, the portions shown in the figures are schematic in nature and their shapes are not intended to represent the exact shape of any portion of a device or to limit the scope of the invention.

[0063] Relational terms such as "inner," "outer," "upper," "above," "below," and below, and similar expressions may be used to denote a relationship of one layer or other region to another layer or region. It is understood that these terms are intended to encompass various orientations of the device in addition to the orientation illustrated in the figures.

[0064] With regard to the term "comprise", for the sake of clarity, when a first device part comprises a second device part, this means that the first device part "comprising" the second device part and does not necessarily enclose it in terms of arrangement, unless, for example, it is a description of a positional and shape-related arrangement; the same applies to a method which may comprise one or more method steps.

[0065] Further possible embodiments are described in the following claims. In particular, the various features of the above-described embodiments can also be combined with one another, provided they are not technically mutually exclusive. The reference symbols mentioned in the claims serve only to improve comprehensibility and in no way limit the claims to the forms illustrated in the figures.

[0066] List of reference symbols

[0067] 1 sensor unit, e.g. microphone unit

[0068] 2 first detector unit

[0069] 2a first signal processing, preferably RMS filter

[0070] 2b second signal processing, preferably respiratory signal detector

[0071] 3 second detector unit

[0072] 3a Weighting determination unit

[0073] 3b Weighting unit

[0074] 3c Gravity determination unit

[0075] G Weighting signal from weighting values

[0076] REx, RE1 - RE4 respiratory event, first - fourth respiratory event REgx, REg1 - REg4 weighted respiratory event, first - fifth

[0077] 51 Patient signal, e.g. microphone signal

[0078] ST intensity signal

[0079] S1" prediction signal of the patient signal

[0080] STu lower threshold signal

[0081] STo upper threshold signal

[0082] 52 respiratory event signal

[0083] 53 weighted respiratory event signal

[0084] 54 clinically relevant severity t timeline

[0085] ZFx, ZF1 - ZF4 time window, first - fourth time window

Claims

Claims 1. A diagnostic system for determining a clinically relevant severity (S4) of a sleep-related breathing disorder from a patient signal of a sleeping patient, comprising: a) a sensor unit (1) that detects at least one patient parameter and generates a corresponding patient signal (S1) therefrom; b) a first detector unit (2) with a first signal processing unit (2a) that generates an intensity signal (S1') from the patient signal (S1), and with a second signal processing unit (2b) that detects respiratory events (REx) in the intensity signal (S1') and generates a corresponding respiratory event signal (S2); and c) a second detector unit (3) that is designed to determine the clinically relevant severity (S4) from the respiratory event signal (S2);characterized in that d) the second detector unit (3) has the following: d1) a weighting determination unit (3a) which is designed to detect the respiratory events (REx) in the respiratory event signal (S2) and to determine an associated weighting signal (G) for each respiratory event (REx) by determining adjacent respiratory events (REx) for the respective respiratory event (REx) within an associated predetermined time window (ZFx) in which the respective respiratory event (REx) also lies, wherein the associated weighting signal (G) is generated as a function of and at least partially increasing with a number and / or an energy of the respective adjacent respiratory events (REx);d2) a weighting unit (3b) that weights the respiratory event signal (S2) with the weighting signal (G) and generates therefrom a weighted respiratory event signal (S3) with correspondingly weighted respiratory events; and d3) a severity determination unit (3c) that determines the clinically relevant severity (S4) as an integral value over the weighted respiratory event signal (S3).

2. Diagnostic system according to claim 1, wherein the weighting determination unit (3a) is designed to shift and arrange the respective predetermined time window (ZFx) for the respective respiratory event (REx) on the time axis in such a way that the greatest possible number of adjacent respiratory events (REx) lies at least partially in the time window (ZFx).

3. Diagnostic system according to claim 1, wherein the weighting determination unit (3a) is designed to shift and arrange the respective predetermined time window (ZFx) for the respective respiratory event (REx) on the time axis such that the greatest possible energy of the respective respiratory event (REx) and the adjacent respiratory events (REx) is present in the time window (ZFx).

4. Diagnostic system according to claim 1, wherein the weighting determination unit (3a) is designed to shift the predetermined time window (ZFx) as a sliding time window along a time axis (t) of the respiratory event signal (S2) and in doing so to determine the number of all respiratory events (REx) at least partially contained in the time window (ZFx) and to map the number in the weighting signal (G) at a predetermined time within the time window (ZFx).

5. Diagnostic system according to one of the preceding claims 1 or 4, wherein the weighting determination unit (3a) is designed to shift the predetermined time window (ZFx) as a sliding time window along the time axis (t) of the respiratory event signal (S2) and in doing so to form an integral value over the energy of all respiratory events (REx) at least partially contained therein and to map the integral value in the weighting signal (G) at the predetermined time within the time window (ZFx).

6. Diagnostic system according to claim 1, wherein the weighting determination unit (3a) is designed to determine the weighting signal (G) as a function of the number and / or energy of the respective respiratory event (REx) and the adjacent respiratory events (REx) within the predetermined time window (ZFx) such that the weighting signal (G) increases with the number and / or energy.

7. Diagnostic system according to one of the preceding claims, wherein the severity determination unit (3c) determines the clinically relevant severity (S4) as a Integral value determined via the weighted respiratory event signal (S3) in conjunction with a predetermined correction factor.

8. Diagnostic system according to claim 7, wherein the correction factor is predetermined and trained as an average value from a plurality of respective correction factors by equating a plurality of respective integral values ​​from the weighted respiratory event signals (S3) with the respective correction factors with respective actual clinically relevant severities (S4) from training data sets, which a person skilled in the art has determined from the respiratory event signals (S2) with the respective respiratory events (REx), wherein the respective correction factors are determinable by respective quotient formation.

9. Diagnostic system according to one of the preceding claims, wherein the weighting determination unit (3a) is designed to generate the weighting signal (G) as a function of the number and / or the energy of the respective adjacent respiratory events (REx) in a predominant value range of the number and / or the energy of the respective adjacent respiratory events (REx) increasing thereto.

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