EP4622549A1

EP4622549A1 - Method, computer program and system for characterizing a heartbeat cycle using magnetocardiography as well as method for treatment of inflammatory cardiomyopathy

Info

Publication number: EP4622549A1
Application number: EP23810369.1A
Authority: EP
Inventors: Bettina Heidecker; Jai-Wun PARK
Original assignee: Biomagnetik Park Holding GmbH; Charite Universitaetsmedizin Berlin
Current assignee: Biomagnetik Park Holding GmbH; Charite Universitaetsmedizin Berlin
Priority date: 2022-11-23
Filing date: 2023-11-23
Publication date: 2025-10-01
Also published as: WO2024110599A1

Abstract

The present invention relates to a for characterizing a heartbeat cycle using magnetocardiography (MCG), the method comprising the steps of: i) acquiring magnetic field data with at least one magnetic field sensor for a duration covering at least a portion of at least one heartbeat cycle from a patient; ii) selecting a first and a second reference cycle time in the magnetic field data for each heartbeat cycle defining an evaluation interval, wherein the evaluation interval comprises a P- and a T-wave (and thus the QRS- complex) of each heartbeat cycle, iii) determining an MCG-vector indicative of a magnetic dipole strength and orientation from the magnetic field data for at least the first and the second reference cycle time, iv) determining a distance value corresponding to an absolute value of a difference vector of the MCG-vector at the first reference cycle time and the MCG-vector at the second reference cycle time, v) outputting said distance value as a characteristic of the heartbeat cycle. The invention further relates to a computer program and an MCG-System for characterizing a heartbeat cycle using MCG as well as a method for treatment of inflammatory cardiomyopathy.

Description

Method, computer program and system for characterizing a heartbeat cycle using magnetocardiography as well as method for treatment of inflammatory cardiomyopathy

Description:

The present invention concerns a method, a computer program as well as a system for characterizing a heartbeat cycle using magnetocardiography (MCG) and a method for treatment of inflammatory cardiomyopathy.

Inflammatory cardiomyopathy is a common etiology of heart failure, which may lead to circulatory collapse requiring mechanical circulatory support or heart transplant. It is also one of the most common causes of sudden cardiac death in young adults, as it frequently remains undetected. Patients frequently benefit from immunosuppression in addition to standard heart failure therapy. However, some patients will experience worsening inflammation and clinical trajectory despite optimized therapy and the medication may have to be escalated. Measuring response to therapy has been a major clinical challenge in these patients, as current state-of- the-art diagnostic methods in inflammatory cardiomyopathy have some limitations in detecting early treatment response. Echocardiography provides valuable structural and functional hemodynamic data of the heart, while it identifies signs of inflammation in an indirect way, such as impaired left ventricular function or wall motion abnormalities. Similarly, cardiac magnetic resonance imaging (CMR) provides comprehensive structural and functional data, while it detects consequences of inflammation, such as edema and late gadolinium enhancement. Fluorodeoxyglucose-positron emission tomography- computed tomography (FDG-PET-CT) detects the level of activity and the extent of inflammation through measurement of glucose metabolism. However, it cannot be applied frequently due to the associated radiation exposure. Endomyocardial biopsy (EMB) is the gold standard of definitive diagnosis of inflammatory cardiomyopathy and is crucial for initiation of immunosuppressive therapy, as active viral infection must be excluded. Invasiveness with risk of complications and limited diagnostic yield (diagnostic sensitivity approximately 64%-92% with multiple samples) restrict its applicability for short-term follow- up.

Given these limitations in detecting early treatment response, optimal immunosuppressive therapy and its surveillance in patients with inflammatory cardiomyopathy remains a major 6 clinical challenge. Consequently, some patients may undergo a trial-and-error treatment approach for several months to years and experience worsening of their clinical condition until the lack of response is detected and immunosuppression adjusted. Based on this, it is subject of the present invention to provide a method, a system and a computer program that allows for reliable and comparable determination of characteristics of heartbeat cycles. It is also subject of the present invention to provide a method for treatment of inflammatory cardiomyopathy based on the determined characteristics of the heartbeat cycles.

This task is solved by a method for characterizing a heartbeat cycle using MCG with the features of claim 1 , a method for treatment of inflammatory cardiomyopathy with the features of claim 20 as well as a computer program with the features of claim 22 and an MCG-System for characterizing a heartbeat cycle using MCG with the features of claim 24.

Advantageous embodiments of: the method for characterizing a heartbeat cycle using MCG are given in the dependent claims 2 to 19 the method for treatment of inflammatory cardiomyopathy are given in the dependent claim 21 the computer program are given in the dependent claim 23 the MCG-system are given in the dependent claim 25 and described in the following.

A first aspect of the invention relates to a method for characterizing a heartbeat cycle using MCG. The method comprises the steps of: i) acquiring magnetic field data with at least one magnetic field sensor for a duration covering at least a portion of at least one heartbeat cycle from a patient; ii) selecting a first and a second reference cycle time in the magnetic field data for each heartbeat cycle defining an evaluation interval, wherein the evaluation interval comprises a P- and a T-wave (and thus the QRS-complex) of each heartbeat cycle, iii) determining an MCG-vector indicative of a magnetic dipole strength and orientation from the magnetic field data of the at least one sensor for at least the first and the second reference cycle time, iv) determining a distance value corresponding to an absolute value of a difference vector of the MCG-vector at the first reference cycle time and the MCG-vector at the second reference cycle time and v) outputting said distance value as a characteristic of the heartbeat cycle. The strength and orientation of the MCG-vector are quantitative measures of the magnetic activity of the heart of the patient. As such, the method according to the first aspect allows for determination of an MCG-vector indicative of the magnetic activity of the heart of the patient and to characterize a heartbeat cycle of the heartbeat cycle of the patient by means of the distance value determined from the absolute value of the difference vector of the respective MCG-vectors at the first and the second reference cycle time. The magnetic field sensor does not need to be in contact with the body of the person but may be arranged in proximity to it, for example in a distance of for example 1cm to 1 m, making MCG a contact-less method. This represents a major advantage compared to methods for characterizing heartbeats requiring a contact between sensors and the body of the patient, such as for example electrocardiography (ECG). Contact-less methods are advantageous in that they do not require the patient to expose the body portion to be investigated, accelerating the time necessary for investigation and its comfort, and in that due to the lack of contact between the sensor and the body, no artefacts related to for example surfaces potentials are affecting the measured signals.

The at least one magnetic field sensor may be configured to measure one, two or three spatially orthogonal components of the magnetic field. A single magnetic field sensor may be used, wherein the single magnetic field sensor is preferably configured to measure at least two of the three components of the magnetic field. In case a plurality of magnetic field sensors is used, they may likewise be configured to measure two or three components of the magnetic field but also multiple sensors configured to measure a single component of the magnetic field may be used.

The magnetic field sensor may be kept at a fixed position relative to the body of the patient during execution of the method such that time-dependent magnetic field data at the respective position may be acquired. However, the magnetic field sensor may also be shifted by a predetermined amount with respect to the body of the person after a pre-determined time. For example, the magnetic field sensor may be shifted by 5cm to 50cm after 10 to 20 heartbeats of the heart of the patient, such that time-dependent magnetic field data may be acquired sequentially at different positions with respect to the body of the patient.

The sensor may acquire magnetic field data for a portion of at least one heartbeat cycle but it may also acquire magnetic field data by continuously acquiring magnetic field data covering multiple subsequent heartbeat cycles. The acquired magnetic field data are thus time dependent and reflect the magnetic activity of the heart as a function of time during at least a portion of at least one heartbeat cycle. Time-dependent deflections of the magnetic field are typically labelled with letters and comprise for example the P-, Q-, R-, S- or T-wave. The time- axis of these deflections is referred to as “cycle time”, as these deflections occur as a function of a portion of or one or more than one heartbeat cycle.

The evaluation interval defined by the selected first and the second reference cycle time comprises the T- and the P-wave and may thus also comprise the QRS-complex occurring temporally between the T- and the P-wave.

The MCG-vector is determined using vector magnetocardiography (VMCG), wherein the acquired magnetic field data is used to construct a spatial representation of the MCG-vector with its magnetic dipole strength and orientation, in particular as a function of time or cycle time. The MCG-vector is determined for at least the first and the second reference cycle time, i.e. two particular points in time and does not need to but can be determined continuously, for example throughout the evaluation interval.

The difference value of the difference vector between the MCG-vectors determined at the first and the second reference cycle time comprises valuable information regarding inflammatory cardiomyopathy of the heart, making it an important characteristic of the heartbeat cycle that can be determined with the method according to the first aspect of the invention. The difference value of the difference vector is further specified below and in the Figures.

According to an embodiment, a temporal course of the MCG-vector is determined during at least a fraction of the evaluation interval. For example, the MCG-vector may be determined at the first and the second reference cycle time and the QRS-complex. The MCG-vector may also be determined throughout the entire evaluation interval and/or during multiple subsequent heartbeats.

In another embodiment, a trace of the MCG-vector along the temporal course is determined as a further characteristic of the heartbeat cycle.

In yet another embodiment, a trace area corresponding to the area enclosed by the trace of the MCG-vector during the evaluation interval or a subinterval thereof is determined. For example, the QRS-complex may represent such a subinterval.

In another embodiment, a baseline of the magnetic field data is determined. For example, the baseline may be determined by acquiring magnetic field data for multiple subsequent heartbeats, wherein an average value of magnetic fields acquired between the last deflections, for example the T- or ll-wave, of one of two subsequent heartbeats and the first deflection, for example the P-wave, of the second of two subsequent heartbeats for some or all recorded heartbeats. Individual heartbeats may be identified, distinguished and/or segmented by means of their respective prominent QRS-complex. The baseline may be used as a reference value for the acquired data and the MCG- and distance vectors determined therefrom.

The baseline may represent an isoelectromagnetic baseline of the heart in which magnetic and/or electric fields generated by the heart are at a minimum during a portion of or one or more heartbeats. Hence, in this representation, the P-, Q-, R-, S- and/or T-waves represent deflections of the magnetic and or/electric fields with respect to their minimum.

According to an embodiment, the first reference cycle time corresponds to a first cycle time at which a magnetic field strength (in magnetic field data exhibiting a P-wave) at the onset of a P-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the P-wave with respect to the baseline.

In an embodiment, the second reference cycle time corresponds to a second cycle time at which a magnetic field strength (in magnetic field data exhibiting a P-wave) at a decay of the T-wave drops below a predefined threshold value, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the T-wave with respect to the baseline.

In another embodiment, the first or the second reference cycle time corresponds to the first or the second cycle minus a predetermined time. The predetermined time may for example be based on a user input.

According to an embodiment, the MCG-vector corresponds to a magnetic dipole generated by electric currents in the heart.

In another embodiment, an electric current is determined based on the acquired magnetic field data and wherein based on the determined electric current, a corresponding electric dipole is determined. As such, electric field data may be determined from the magnetic field data from the known relation of both physical quantities. As a result, temporal ECG-data indicative of the electrical activity of the heart may be determined. The ECG-data may be output or plotted together with MCG-data indicative of the measured magnetic field data and/or the MCG-vector on the same time-axis, for example during a portion or one or more heartbeats. As such, the magnetic field data can be directly compared and associated with the ECG-data. This simplifies the interpretation and analysis of the magnetic field data, as ECG is a well-known and widespread method. According to an embodiment, the temporal course of the MCG-vector and/or the electric dipole is displayed.

In another embodiment, the magnetic field data is acquired from a plurality of heartbeat cycles and averaged over the plurality of heartbeat cycles, generating averaged magnetic field data reflecting an average magnetic field strength as a function of the averaged heartbeat cycle. As such, the signal-to-noise ratio is substantially improved compared to magnetic field data acquired from a portion of or one heartbeat. The data may be cyclically averaged.

In an embodiment, the corresponding distance value of the corresponding difference vector is put out for each heartbeat cycle. In other words, the difference vector is determined for the first and the second reference cycle time for each heartbeat cycle and the corresponding distance value of the corresponding difference vector is put out.

According to an embodiment, from the distance values, an averaged distance value of the difference vectors is determined and output. As such, the signal-to-noise ratio of the difference vector is substantially improved compared to the case of a single heartbeat with a single distance value based on just one first reference cycle time and just one second reference cycle time.

In another embodiment, an average MCG-vector is determined from the averaged magnetic field data, and wherein the averaged distance value is determined from the averaged MCG- vector. Hence, in other words, the average MCG-vector may be first determined from averaging the magnetic field data temporally over multiple heartbeats and/or by taking into account the magnetic field data from multiple sensors. From the determined average MCG- vector and its temporal evolution, an averaged difference vector may be determined by sampling the temporal evolution of the average MCG-vector at the respective first and the respective second reference cycle times of the respective heartbeat. The average distance value is finally determined from the averaged difference value.

According to another embodiment, said fraction of the evaluation interval is delimited at least by a third cycle time at which a magnetic field strength at the onset of a T-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the T-wave excitation with respect to the baseline.

In an embodiment, said fraction of the evaluation interval is delimited at least by a fourth cycle time at which a magnetic field strength of the T-wave-excitation is at its maximum amplitude or at a predefined value with respect to said maximum amplitude, particularly wherein said predefined value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the maximum amplitude of the T-wave excitation with respect to the baseline.

In an embodiment, said fraction of the evaluation interval is delimited by said third and said fourth cycle time, defining a subinterval within said evaluation interval.

As such, the subinterval may comprise the T-wave excitation, particularly the subinterval comprises only the T-wave excitation. Considering the T-wave excitation gives rise to particularly reliable magnetic field data for the MCG- vector, which improves the significance of the outputted distance value as a characteristic of the heartbeat cycle.

Alternatively, the third cycle time and the fourth cycle time define a subinterval enclosing one or more selected from the group consisting of the P-wave excitation, the QRS-complex, the ST-T segment, with the third and the fourth cycle time chosen accordingly.

Particularly, in case of the P-wave excitation, said fraction of the evaluation interval can be delimited by: a third cycle time at which a magnetic field strength at the onset of a P-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the T-wave excitation with respect to the baseline and a fourth cycle time at which a magnetic field strength of the P-wave-excitation is at its maximum amplitude or at a predefined value with respect to said maximum amplitude, particularly wherein said predefined value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the maximum amplitude of the P-wave excitation with respect to the baseline.

Particularly, in case of the QRS-complex excitation, said fraction of the evaluation interval can be delimited by: a third cycle time at which a magnetic field strength at the onset of a Q-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the Q-wave excitation with respect to the baseline and a fourth cycle time at which a magnetic field strength at a decay of a S-wave drops below a predefined threshold value, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the S-wave with respect to the baseline.

Particularly, in case of the ST-T segment, said fraction of the evaluation interval can be delimited by: a third cycle time at a magnetic field strength at a decay of a S-wave drops below a predefined threshold value, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the S-wave with respect to the baseline and a fourth cycle time at which a magnetic field strength at an onset of a T-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the T-wave excitation with respect to the baseline.

In another embodiment, a number of magnetic poles of the spatial magnetic field is determined at least during said fraction of the evaluation interval as a further characteristic of the heartbeat cycle. The spatial magnetic field can be measured for example by SQUID-sensors and may be represented in one, two or three spatially orthogonal dimensions. Preferably, the spatial magnetic field is determined within a sagittal plane of the patient. Typically, for healthy subjects, a single positive and a single negative pole is found in the spatial magnetic field measured during at least the fraction of the evaluation interval within the sagittal plane, particularly if the fraction is chosen such that it defines a subinterval consisting of the T-wave excitation. In contrast, for patients with cardiomyopathy, multiple magnetic poles of the same polarity are found in the spatial magnetic field. For example, if two magnetic poles of positive polarity and one magnetic pole of negative polarity are found in the spatial magnetic field, this points to inflammatory cardiomyopathy of the subject.

According to another embodiment, a direction of the difference vector of the MCG-vector at the third cycle time and the MCG-vector at the fourth cycle time is determined as a further characteristic of the heartbeat cycle. Typically, for healthy subjects, said difference vector points into a first quadrant of the sagittal plane, i.e. towards the top and the back of the head, wherein for patients with cardiomyopathy, the vector points into a different quadrant other than the first quadrant. Considering both the absolute value and the direction of said difference vector as characteristics of the heartbeat cycle significantly improves the differentiation between subjects with and without inflammatory cardiomyopathy. For example, if the difference vector points into the second or the third quadrant and the absolute value of the MCG-vector exceeds a predetermined threshold value, for instance 0.051 , this points to a subject with inflammatory cardiomyopathy. A second aspect of the invention relates to a method for treatment of inflammatory cardiomyopathy. This method comprises the method according to the first aspect of the invention, wherein if a normalized distance value based on the distance value or the averaged distance value is greater or equal to a predetermined threshold value, a patient is treated with a predetermined amount of a medication against inflammatory cardiomyopathy.

For example, patients with a distance value greater or equal to the predetermined threshold value are treated with Prednisolone 1mg/kg total bodyweight per os (PO) daily for two weeks, with subsequent reduction of the total daily dose by 10mg every two weeks.

The method of treatment may comprise an adaption of the predetermined amount and/or an adaption of the medication with time. For example, the method for treatment of inflammatory cardiomyopathy may be executed two or more times within predetermined intervals, for example after 7 and/or after 30 days. Based on the distance value determined for each execution of the method for treatment of inflammatory cardiomyopathy, the predetermined amount and/or the medication may be adapted. For example, if at a first execution of the method for treatment of cardiomyopathy a distance value exceeding the predetermined threshold value is determined, the patient is treated with said predetermined amount of a medication. If at a second subsequent execution the corresponding distance value still exceeds the predetermined threshold value, the amount or dose of the medication may be increased with respect to the amount or dose applied after the first execution. Alternatively or additionally, a different medication compared to the medication used after the first execution may be used. For example, after the first execution, Prednisolone may be used and after the second execution Methotrexate or Methotrexate in combination with Prednisolone may be used.

The normalization of the distance value may be performed by normalizing the determined distance value or the average distance value to a peak value of a deflection of the acquired magnetic field or averaged magnetic field as a function of cycle time. For example, the average magnetic field detected at the R-deflection within the QRS-complex may be used for normalization.

In an embodiment, the predetermined threshold value is 0.051. This value is supported by a medical study disclosed in the Figures.

As such, the determined difference value represents an important measure of the condition of the heart with respect to inflammatory cardiomyopathy. A third aspect of the invention relates to a computer program comprising computer program code, that when executed on a computer, executes the method according to the first aspect of the invention.

In an embodiment, the computer program comprises a user input for selecting and adjusting the first and second reference cycle times and/or the baseline.

A fourth aspect of the invention relates to an MCG-System for characterizing a heartbeat cycle using MCG. The MCG-System comprises:

- at least one sensor configured to measure signals indicative of a magnetic field generated by a heart of a patient and to generate magnetic field data from said measured signals during at least a portion of at least one heartbeat cycle from the patient, and a processor unit configured to receive the magnetic field data and to execute the computer program according to the third aspect of the invention.

In an embodiment of the invention, the MCG-System further comprises a display for displaying the distance value.

A fifth aspect of the invention relates to a method of diagnosis of inflammatory cardiomyopathy, wherein the method according to the first aspect of the invention is executed and wherein if the normalized distance value is greater or equal to the predetermined threshold value, the patient is diagnosed with inflammatory cardiomyopathy and wherein if the normalized distance value is less than the predetermined threshold value, the patient is not diagnosed with inflammatory cardiomyopathy.

Exemplary embodiments are described below in conjunction with the Figures. The Figures are appended to the claims and are accompanied by text explaining individual features of the shown embodiments and aspects of the present invention. Each individual feature shown in the Figures and/or mentioned in the text of the Figures may be incorporated (also in an isolated fashion) into a claim relating to the methods, the computer program and/or the system according to the present invention.

Fig. 1 shows an MCG-system with multiple sensors configured to measure magnetic fields for determination of the MCG-vector, Figs. 2a and 2b shows time traces of MCG- vectors of a healthy patient (Fig. 2a) as well as a patient with cardiac pathology,

Fig. 3 shows an overview over patients participating at a study for identifying cardiomyopathy based on MCG,

Fig. 4 depicts an analysis yielding the threshold value used to discriminate between patients with any type of non-ischemic cardiomyopathy and subjects without cardiomyopathy,

Figs. 5a and 5b show time traces of MCG-vectors of a patient with inflammatory cardiomyopathy (Fig. 5a), and a patient who developed myocarditis after an mRNA vaccine against COVID-19 (Fig. 5b),

Fig. 6 shows the evolution of the distance value determined for patients with inflammatory cardiomyopathy as a function of time (top plot) compared to measurements on the persons based on left ventricular ejection fraction, wherein the patients were given a treatment with medication,

Fig. 7 shows the data of Fig. 6 normalized to their respective starting value at the beginning of the treatment,

Figs. 8 to 10 show analyses of the difference value and measurement of ejection fraction by echocardiography obtained in all 3 control groups on day one of admission and seven days later and

Fig. 11 shows an interface of an MCG-display with multiple different panels for visualization of the magnetic field data acquired by the magnetic field sensors and subsequent determination of the MCG-vector.

The following depicts a clinical study in which the difference value obtained from the method for characterizing a heartbeat cycle using MCG was applied to screen for inflammatory cardiomyopathy and to detect early treatment response during immunosuppressive therapy as compared to echocardiography. In addition, the findings were tested in 3 control groups: 1) Patients without inflammatory cardiomyopathy receiving immunosuppression; 2) Patients with inflammatory cardiomyopathy without immunosuppressive therapy; 3) Patients with Post- COVID-19 condition with neither inflammatory cardiomyopathy nor immunosuppressive therapy. Furthermore, the method was applied with respect to its capability in detecting inflammatory cardiomyopathy in a patient with confirmed myocarditis after COVID- 19 vaccine. MCG is a non-invasive method with the ability to detect the cardiac magnetic field generated by electrical currents of the heart. Action potentials of the heart muscle cells create ion currents that cause voltage fluctuations between energized and de-energized tissue and thereby create a magnetic field. These biomagnetic signals are in the range of 10^-15 to 10'¹¹ Tesla. Thus, they are weaker than the earth's magnetic field by order of 10^-6. These signals can be measured by sensitive magnetic field sensors, called SQUIDs (Superconducting Quantum Interference Sensors). These SQUIDS reach their supra-conducting habit at a temperature of 4,2 K (-269°), which needs a cooling with liquid helium in a vacuum jacket vessel, called Dewar.

The measurement requires a magnetically shielded room (MSR) and a 1st order gradiometer to avoid electromagnetic interference with the magnetic field of the human heart. This field is measured by using 64-channels (CS-MAG III, Biomagnetik Park GmbH, Hamburg, Germany). Fig. 1 shows the used MCG-system 10 with various magnetic field sensors 1 configured to measure the x- and the y-component of the magnetic field as well as various magnetic field sensors 2 configured to measure the z-component of the magnetic field. The magnetic field sensors 2 for the z-component are arranged around the magnetic field sensors 1 for the x- and the y-component. As such, the MCG-system 10 can detect three independent components (x, y, z component) of the magnetic field. The z-axis is perpendicular to the chest plane. With respect to the z component, the x-y axis follows the right-hand rule. The y-axis for example is orientated to the patient's head (Figure S1). The MCG-system 10 comprises 16 magnetic field sensors 2 for the z--component and 48 tangential (24 in x and 24 in y direction) magnetic field sensors 1 for the x- and the y-component. Both types of magnetic field sensors 1 ,2 are superconducting quantum interference device (SQUID) sensors. The MCG-system 10 has a sensitivity of <6.5 fTrmsA/ Hz over 100 Hz.

The data from the 64 magnetic field sensors 1 ,2 is averaged and filtered with a bandpass. By using an adjustment of the zero lines and evaluating the R-wave position automatically, it ends up having an interference-free and high-resolution MCG.

The software (Cardio Expert 2.5) automatically calculates minimum-, maximum- and summation-value from all 64 magnetic field sensors 1 ,2 for the different parameters.

Fig. 2 depicts an MCG-display 11 as a component of the MCG-system 10. The MCG-display 11 displays data indicative of the measured magnetic fields. In a normal heart (cf. Fig. 2a), the heart’s magnetic field defines an MCG-vector 12 at fixed orientation and strength typically located in the 1st quadrant of an MCG display. Hearts with pathologies (cf. Fig. 2b) are characterized by fluctuations in both the magnitude and orientation of the heart’s magnetic field, which usually positions the vector inside a diffuse cluster in the 2nd and 3rd quadrant. The objective of the MCG measurements reported here is to screen for inflammatory cardiomyopathy and to monitor and quantify improvement to the heart’s MCG-vector 12, in terms of its pointing, magnitude, and stability, during the course of immunosuppressive therapy.

MCG offers many practical advantages as compared to other diagnostic methods. First, it can detect the magnetic field of the heart in a contact-less manner without exposing the patient to radiation. Patients do not need to be undressed and no electrodes are required. Second, it is less affected by conductivity variations caused by lungs, skin, and muscles as compared to ECG. With the help of multichannel-systems, the entire thoracic magnetic field can be assessed in one measurement. The data can be obtained at rest within 60 seconds of measurement time.

In clinical applications, MCG has been mainly used for localization of cardiac arrhythmias and for early diagnosis of myocardial ischemia. Furthermore, early reports in literature have described its potential in detecting heart transplant rejection, suggesting a potential role in detecting myocardial inflammation.

In this study, MCG was used to characterize heartbeats as well as as a screening method to for inflammatory cardiomyopathy and to predict therapy response at an early stage of treatment.

In this study, the feasibility of using MCG was evaluated as screening method to detect patients with inflammatory cardiomyopathy and to evaluate its ability to monitor early treatment response in this population compared to a current state-of-the-art approach - measurement of left ventricular ejection fraction (LVEF) by echocardiography. This study was approved by the ethics committee of the Charite Universitaetsmedizin Berlin, Germany. All participants provided their written informed consent.

In Fig. 3, an overview over the patients participating at the present study is shown. Patients admitted to a hospital during the period from January 2019 to January 2021 with newly diagnosed non-ischemic cardiomyopathy and a control group without cardiomyopathy were enrolled. The group of patients without cardiomyopathy included healthy individuals as well as patients receiving medications for hypertension, e.g. beta-blocking agents or ACE-inhibitors. enrolled three additional control groups: 1) Patients without inflammatory cardiomyopathy receiving immunosuppression; 2) Patients with inflammatory cardiomyopathy without immunosuppressive therapy; 3) Patients with Post-COVID-19 condition with neither inflammatory cardiomyopathy nor immunosuppressive therapy. Enrolled patients with COVID- 19 condition did not show any signs or symptoms of inflammatory cardiomyopathy and relevant laboratory parameters were within normal limits (high sensitivity troponin T, C-reactive protein, NT brain natriuretic peptide, leukocytes). Post COVID-19 condition was defined according to the official definition by the WHO: “Post COVID-19 condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID- 19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis. Common symptoms include fatigue, shortness of breath, cognitive dysfunction but also others [...] which generally have an impact on everyday functioning.”

In general, MCG can be applied frequently with a low threshold, as there are no known side effects of MCG measurements. However, in patients with metal implants close to or within their heart, such as mechanical valves, internal cardioverter defibrillators (ICDs) or pacemakers, MCG measurements cannot be interpreted. Such devices could interfere with the magnetic field of a patient and compromise accurate measurements. Therefore, patients with such devices or implants were excluded from the study. Diagnostic workup included history and physical exam, ECG, comprehensive laboratory testing based on differential diagnoses (complete blood count with differential, complete metabolic panel, C-reactive protein, ferritin, thyroid stimulating hormone, antinuclear antibodies, antineutrophil cytoplasmic antibodies, soluble interleukin 2 receptor, serum electrophoresis, immunofixation serum and urine), genetic testing for M. Fabry or transthyretin amyloidosis variant (ATTRv), echocardiography, cardiovascular magnetic resonance imaging (CMR), scintigraphy or FDG-PET-CT. All patients diagnosed with inflammatory cardiomyopathy underwent coronary angiography. Patients with coronary artery disease were excluded from the study.

EMB was performed in accordance with recommendations of the European Society of Cardiology. Echocardiographic measurements were performed with the Vivid 8 Echocardiography Machine from GE.

All patients underwent their first MCG and echocardiographic measurements at the time of diagnosis (i.e., baseline measurement). If indicated, standard heart failure therapy with or without immunosuppression was initiated after baseline measurements based on current recommendations of the European Society of Cardiology. Patients treated with immunosuppression had MCG and echocardiographic measurements on day 7 and 30. Immunosuppressive therapy consisted of Prednisolone 1 mg/kg total bodyweight per os (PO) daily for two weeks, with subsequent reduction of the total daily dose by 10mg every two weeks. Positive treatment response under immunosuppressive therapy was defined as an improvement of LVEF from baseline to day seven by at least 10% similar to what has been used in prior literature. Finally, the results were compared to subjects without cardiomyopathy and the three control groups. Diagnosis of cardiomyopathy was excluded based on patient history and physical exam, as well as echocardiography.

Prior to undergoing an MCG measurement, patients had to take off any items that could potentially interfere with their intrinsic magnetic field such as a wearable cardioverter defibrillator or jewelry. Patients were placed in a supine position on a stretcher integrated into the MCG-system 10, cf. Fig. 1. A standard 12-lead ECG was placed with magnetically compatible electrodes for rhythm monitoring.

The system used to measure the magnetic field was positioned in a contactless manner approximately 2 cm above the patient’s thorax. Similar to an ECG-measurement, the patient should not move for 60 seconds of measurement.

Descriptive data are reported as mean (standard deviation of the mean), change rates are reported with 95% confidence intervals.

First, the threshold value of the distance value or the average distance value of the difference vector was determined to discriminate between patients with any subtype of non-ischemic cardiomyopathy or cardiomyopathy subtypes versus subjects without cardiomyopathy. The difference vector is determined from the difference of the MCG-vector at the first reference cycle time and the MCG-vector at the second reference cycle time. The area under the receiver operating curve (ROC) and Youden index were calculated to assess predictive performance of that threshold value. Then, the pathological threshold of the difference vector was validated in various control groups, as well as in a patient with vaccine-related myocarditis.

Then, the ability of the method for characterizing a heartbeat cycle to monitor treatment response was evaluated. We interpreted a decrease of the distance value towards normal as treatment response. More specifically, a decrease of the distance value by 10% on day seven compared to the baseline was used as an indicator for response to therapy. Given that there have not been any other studies on the detection of treatment response by MCG, the estimation of effect size between groups could not be derived from prior literature. Therefore, the standard of 10% improvement was used as for LVEF, which is well established in the literature. It was estimated that a sample size of at least eight subjects is necessary to detect a difference in the distance value and LVEF of 10%, respectively, with a power of 80% and a significance level of 0.05. A two-tailed P-value of <0.05 was considered statistically significant.

To assess the effect of immunosuppressive therapy over time on outcomes (i.e. distance value and LVEF at baseline prior to immunosuppressive therapy as compared to day seven and day 30 after immunosuppressive therapy), linear mixed-effect models were used with an identity link function for normally distributed probability, as previously described. Similarly, for the control groups, measurements were performed on day one of admission and day seven.

Each mixed model included time of repeated measurement as fixed effects whilst allowing intercepts to vary for each study subject (random-intercept model). Results are displayed including F-statistics and degrees of freedom. Post hoc pairwise comparisons were Bonferroni corrected. Statistical data analyses, tables and graphs were generated using R (R Core Team, 2017).

Table 1 shows the patient characteristics of healthy subjects and patients with inflammatory cardiomyopathy participating in the study. In total, 233 patients were enrolled in this study. For the primary proof-of-concept study, 209 adult subjects were enrolled between January 2019 and January 2021 , out of which 66 subjects were diagnosed with inflammatory cardiomyopathy. Of the patients diagnosed with inflammatory cardiomyopathy, 13 patients were initiated on immunosuppressive therapy after baseline measurement and consequently qualified for serial MCG measurements (baseline, day 7 and day 30). On average, patients with inflammatory cardiomyopathy were 43 (±16) years old, had a BMI of 25.2 (±4.0) kg / m2 and were predominantly men (69.2%). Subjects without cardiomyopathy were on average 36.3 (±14.5) years old with a BMI of 23.9 (±3.3) kg / m2 and 50.5% were men.

Table 1: Patient characteristics of healthy subjects and patients with inflammatory cardiomyopathy.

Healthy subjects Inflammatory Total

(N=91) cardiomyopathy _(N=104)

Age (years) 36.3 (14.5) 43.3 (15.6) 37.1 (14.8)

Sex

Male 46 (50.5%) 9 (69.2%) 55 (52.9%)

Female 45 (49.5%) 4 (30.8%) 49 (47.1%)

Body mass index (kg m ²) 23.9 (3.31 ) 25.2 (3.97) 24.1 (3.40)

MCG vector 0.0291 (0.0145) 0.104 (0.0573) 0.0385 (0.0345)

Ejection fraction (percent) 61.7 (2.75) 42.2 (14.2) 59.3 (8.50)

Left ventricular end-diastolic 46.4 (3.59) 51.6 (8.93) 47.0 (4.86) diameter (millimeter) \ > \ > \ /

Numbers are displayed as mean (SD) or frequency (percent).

With an exploratory intent, the threshold value for the distance value to discriminate between patients with any type of non-ischemic cardiomyopathy (N = 118) and subjects without cardiomyopathy (N = 91) was obtained through analyses of area under the receiver operating curves (ROC), which is shown in Fig. 4. The threshold value for this specific distance value was found at 0.051 after normalization of the acquired magnetic field data to the R-peak of the averaged QRS-complex (area under the curve (AUC): 0.78, sensitivity = 0.59, specificity = 0.95). It was evaluated if the distance value was specific for a subtype of cardiomyopathy: In sub-groups of patients with inflammatory cardiomyopathy (N = 66) and cardiac amyloidosis (N = 17) optimal threshold values for the distance value were calculated at similar levels, that is 0.051 and 0.052, respectively. All patients with inflammatory cardiomyopathy had distance values greater than the threshold value.

Ultimately, broad applicability of the findings was tested in a 25-year-old man, who developed myocarditis after COVID- 19 vaccine. Myocarditis was confirmed based on clinical presentation, high sensitivity troponin T, and cardiac magnetic resonance imaging. Fig. 5a and Fig. 5b depict MCG-vectors 12 of a patient with inflammatory cardiomyopathy (Fig. 5a), and a patient who developed myocarditis after an mRNA vaccine against COVID-19 (Fig. 5b). Both vectors are pathological.

The MCG-vectors 12 represent the measured area from the beginning of the T-wave until the maximum of T-wave. A wide surface area as well as a vector position in the second quadrant between 90° and 180° are considered pathological. Indeed, the determination of the distance value correctly identified pathological distance values beyond 0.051 for both patients, similar to the distance value found in other patients with inflammatory cardiomyopathy of the cohort.

As shown in Fig. 6 (top plot), the distance value changed significantly over time after administration of immunosuppressive agents in patients with inflammatory cardiomyopathy (F(2, 24) = 17.612, P < 0.001). In particular, the distance value decreased from 0.10 [95% Cl, 0.08-0.13] to 0.07 [95% Cl, 0.04-0.09] within seven days after administration of immunosuppressive agents (P = 0.010). Within four weeks of administering immunosuppressants, the distance value decreased further to 0.03 [95% Cl, 0.01-0.05]; P < 0.001.

LVEF (lower plot in Fig. 6) also changed significantly over time after administration of immunosuppressive agents (F(2, 24) = 19.31 , P < 0.001). However, there was not a significant change in LVEF within seven days of administering immunosuppressive agents: 42.2% [95% Cl, 34.2%-50.1%] vs. 45.2% [95% Cl, 37.2%-53.1%] after seven days (P = 0.414). However, after four weeks of immunosuppressive therapy, LVEF improved significantly to 53.8% [95% Cl, 45.9%-61.8%]; P < 0.001. As Fig. 7 demonstrates and of note, the change in the distance value as compared to change in LVEF differed significantly from each other after seven days of administering immunosuppressive agents: change distance value -30.38% (±24.21%) vs. change in LVEF +8.66% (±14.17%), P < 0.001. Similarly, change in distance value compared to change in LVEF between 7 days and 30 days of immunosuppressive therapy were significantly different: change in distance value -43.29% (±25.28%) vs. change in LVEF 26.45% (±37.12%), P < 0.001.

To confirm that the observed findings based on MCG in fact represent treatment response to immunosuppressive medications in inflammatory cardiomyopathy, the findings were evaluated in three control groups as listed in Table 2. The control groups included patients receiving immunosuppression for conditions other than inflammatory cardiomyopathy (n=10), patients with inflammatory cardiomyopathy who were treated with standard heart failure therapy, but without immunosuppression (n=4) and patients with Post COVID-19 condition, who had neither signs nor symptoms of inflammatory cardiomyopathy and who did not receive any immunosuppressive therapy (n=9). Patients within those control groups were enrolled after the proof-of-concept study was completed for independent testing. The clinical parameters of patients in the control groups are further listed in Table 2.

Analysis of the difference value and measurement of ejection fraction by echocardiography were obtained in all 3 control groups on day one of admission and seven days later, cf. Figs. 8 to 10. No significant changes between the measurements of MCG vector or ejection fraction of day one and day seven were detected.

MCG represents a practical, non-invasive screening method to detect inflammatory cardiomyopathy in patients, in whom ischemic heart disease has been excluded or in patients with very low a priori probability of ischemic heart disease or other types of cardiac abnormality. In a recent study including 51 patients with coronary artery disease and 52 healthy volunteers, best sensitivity and specificity of MCG were 56% and 96%, revealing a similar diagnostic sensitivity as in the present study for detecting cardiomyopathy in general. Early reports in literature have suggested that MCG may be used to detect early myocardial inflammation in the setting of heart transplant rejection and myocarditis. While MCG was not able to discriminate different types of cardiomyopathies, the presented data suggest that MCG has the potential of measuring early treatment response to immunosuppression within seven days. Given the fact that this diagnostic screening method has no known side effects and does not require much time or effort to perform, its application may lower the threshold to screen patients for inflammatory cardiomyopathy and improve the number of undetected cases consequently. Once a distance value greater or equal to the threshold value is detected, additional diagnostic testing may be required to evaluate, whether the patient has inflammatory cardiomyopathy or another type of cardiomyopathy. Patients with inflammatory cardiomyopathy under immunosuppression experienced an early decrease of the distance towards normal, i.e. below the threshold value. However, no relevant changes were observed in three different control groups consisting of patients, who were either not treated with immunosuppression or received immunosuppression without having inflammatory cardiomyopathy.

With current state-of-the art diagnostic methods (echocardiography, FDG PET-CT, CMR) characterizing early treatment response to immunosuppression remains challenging. Echocardiography is a valuable tool to measure parameters such as ventricular function, intracardial pressures and valve function without relevant side effects. While providing a broad range of valuable data that will guide treatment in patients with cardiomyopathies, echocardiography mostly measures indirect effects of inflammation associated cardiac injury such as wall motion abnormalities or impaired ventricular function. A delay in detection may lead to late allocation of appropriate therapies, at which point there may have been already partial damage of the myocardium. FDG PET-CT directly measures the inflammatory metabolism of the heart. While FDG PET-CT is highly valuable in providing information about structural changes, as well as full extent and activity of inflammation during therapy, its clinical use is limited due to radiation exposure. CMR provides valuable structural and functional information about the heart and detects the effects of inflammation in inflammatory cardiomyopathy. Similar to PET-CT, CMR cannot be applied frequently due to limitations in terms of resources. Also, there has been cumulative evidence that gadolinium may get deposited in the brain when applied frequently during repeat CMRs. However, no adverse clinical effects have been demonstrated in the context of gadolinium deposition in the brain. Diagnostic accuracy of CMR is estimated at approximately 80%. EMB is the gold standard in diagnosing inflammatory cardiomyopathy and a prerequisite for initiation of immunosuppressive therapy. While the risk for complications during an EMB is overall low the cumulative risk would increase considerably if performed repetitively within short intervals in the same patient. Therefore, while important for initial diagnosis, EMB is rarely obtained in frequent intervals in patients undergoing immunosuppressive therapy for inflammatory cardiomyopathy. While these state-of-the art methods for diagnosis and monitoring of treatment response are crucial for the care of patients with inflammatory cardiomyopathy, there is a clinical need to develop a complementary method that supports the early detection of inflammatory cardiomyopathy and a therapeutic response and might be used as surveillance tool for immunosuppressive therapy.

While the specificity of a distance value greater or equal to the threshold value was high (95%) for detection of cardiomyopathy of any kind, its sensitivity was rather low (59%). Also, MCG was not able to discriminate between different types of cardiomyopathy. However, all patients with inflammatory cardiomyopathy had a distance value greater or equal to the threshold value. Furthermore, MCG analysis was able to detect myocarditis after COVID-19 vaccine. While myocarditis after COVID- 19 vaccine is rare, the present data suggest that MCG could be a valuable screening tool in this still poorly understood adverse event of vaccination.

Taken together, the findings suggest that determining the distance value based on MCG is a valuable screening method for inflammatory cardiomyopathy and behaves similar to a tumor marker. Its value does not necessarily lie in diagnosing a specific disease, but rather in reflecting therapy response very accurately and early in time thereby supplying the physician an additional tool to individualize therapy.

Finally, it warrants mention that in some patients of the cohort, the findings related to the distance value did positively affect patient management. In three patients with inflammatory cardiomyopathy, detection of a worsening, i.e. increasing distance values over time led to additional diagnostic imaging including PET-CT or CMR, which detected indeed worsening inflammation and lead to escalation of immunosuppressive therapy with subsequent improvement of the patient’s clinical condition.

Fig. 11 shows an interface 20 of the MCG-display 11 of the MCG-system 10 with multiple different panels 21 ,22,23,24,25,26. In a first panel 21 , various magnetic field signals obtained from multiple magnetic field sensors 1 ,2 are plotted as a function of the cycle time. Each curve corresponds to the average magnetic field measured by the respective sensor as a function of the cycle time. The average of the magnetic field in this case means that the magnetic field was recorded for the duration of multiple heartbeats and averaged over the multiple heartbeats. The magnetic fields exhibit the characteristic deflections corresponding to the P-, Q-, R-, S- and T-waves. From these measured magnetic fields, the MCG-vector 12 with its magnetic dipole strength and -orientation is determined. As can be seen in the angular coordinate system shown in the second panel 22, the orientation of the MCG-vector 12 changes as a function of cycle time, which can be understood from the shown trace with each point corresponding to the orientation of the MCG-vector 12 at a given time within the cycle time. The MCG-vector 12 is also plotted as a function of cycle time as a trace within the three- dimensional coordinate shown in the third panel 23, which depicts both the orientation and the magnetic dipole strength of the MCG-vector 12. The remaining fourth, fifth and sixth panel 24,25,26 show projections of the MCG-vector 12 onto the sagittal-, the transverse-, and the frontal plane-, respectively.

Claims

Patent claims:

1. A method for characterizing a heartbeat cycle using magnetocardiography (MCG), the method comprising the steps of: i) acquiring magnetic field data with at least one magnetic field sensor for a duration covering at least a portion of at least one heartbeat cycle from a patient; ii) selecting a first and a second reference cycle time in the magnetic field data for each heartbeat cycle defining an evaluation interval, wherein the evaluation interval comprises a P- and a T-wave of each heartbeat cycle, iii) determining an MCG-vector indicative of a magnetic dipole strength and orientation from the magnetic field data for at least the first and the second reference cycle time, iv) determining a distance value corresponding to an absolute value of a difference vector of the MCG-vector at the first reference cycle time and the MCG-vector at the second reference cycle time, v) outputting said distance value as a characteristic of the heartbeat cycle.

2. The method for characterizing a heartbeat cycle according to claim 1 , wherein a temporal course of the MCG-vector is determined during at least a fraction of the evaluation interval.

3. The method for characterizing a heartbeat cycle according to claim 2, wherein a trace of the MCG-vector along the temporal course is determined as a further characteristic of the heartbeat cycle.

4. The method for characterizing a heartbeat cycle according to claim 3, wherein a trace area corresponding to the area enclosed by the trace of the MCG-vector during the evaluation interval or a subinterval thereof is determined.

5. The method for characterizing a heartbeat cycle according to one of the preceding claims, wherein a baseline of the magnetic field data is determined.

6. The method for characterizing a heartbeat cycle according to claim 5, wherein the first reference cycle time corresponds to a first cycle time at which a magnetic field strength at the onset of a P-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the P-wave with respect to the baseline. The method for characterizing a heartbeat cycle according to claim 5 or 6, wherein the second reference cycle time corresponds to a second cycle time at which a magnetic field strength at a decay of the T-wave drops below a predefined threshold value, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the T-wave with respect to the baseline. The method for characterizing a heartbeat cycle according to claim 6 or 7, wherein the first or the second reference cycle time corresponds to the first or the second cycle minus a predetermined time. The method for characterizing a heartbeat cycle according to one of the preceding claims, wherein an electric current is determined based on the acquired magnetic field data and wherein based on the determined electric current, a corresponding electric dipole is determined. The method for characterizing a heartbeat cycle according to one of the claims 2 to 9, wherein the temporal course of the MCG-vector and/or the electric dipole is displayed. The method for characterizing a heartbeat cycle according to one of the preceding claims, wherein the magnetic field data is acquired from a plurality of heartbeat cycles and averaged over the plurality of heartbeat cycles, generating averaged magnetic field data reflecting an average magnetic field strength as a function of the averaged heartbeat cycle. The method for characterizing a heartbeat cycle according to claim 11 , wherein for each heartbeat cycle, the corresponding distance value of the corresponding difference vector is put out. The method for characterizing a heartbeat cycle according to claim 12, wherein from the distance values, an averaged distance value of the difference vectors is determined and output. The method for characterizing a heartbeat cycle according to claim 11 , wherein an average MCG-vector is determined from the averaged magnetic field data, and wherein the averaged distance value is determined from the averaged MCG-vector. The method for characterizing a heartbeat cycle according to one of the claims 2 to

14, wherein said fraction of the evaluation interval is delimited at least by a third cycle time at which a magnetic field strength at the onset of a T-wave excitation exceeds a predefined threshold value with respect to the baseline, particularly wherein said threshold value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the amplitude of the T-wave excitation with respect to the baseline. The method for characterizing a heartbeat cycle according to one of the claims 2 to

15, wherein said fraction of the evaluation interval is delimited at least by a fourth cycle time at which a magnetic field strength of the T-wave-excitation is at its maximum amplitude or at a predefined value with respect to said maximum amplitude, particularly wherein said predefined value is in the range of 2% to 10%, more particularly in the range of 4% to 7% of the maximum amplitude of the T-wave excitation with respect to the baseline. The method for characterizing a heartbeat cycle according to one of the claims 2 to

16, wherein a number of magnetic poles of the spatial magnetic field is determined at least during said fraction of the evaluation interval as a further characteristic of the heartbeat cycle. The method for characterizing a heartbeat cycle according to at least the claims 15 and 16, wherein a direction of the difference vector of the MCG-vector at the third cycle time and the MCG-vector at the fourth cycle time is determined as a further characteristic of the heartbeat cycle. The method for characterizing a heartbeat cycle according to at least claim 17 or 18, wherein the number of magnetic poles of the spatial magnetic field or the direction of the difference vector of the MCG-vector is determined in the sagittal plane of the patient. A method for treatment of inflammatory cardiomyopathy comprising the method according to one of the preceding claims, wherein if a normalized distance value based on the distance value or the averaged distance value is greater or equal to a predetermined threshold value, a patient is treated with a predetermined amount of a medication against inflammatory cardiomyopathy. The method for treatment of inflammatory cardiomyopathy according to claim 20, wherein the predetermined threshold value is 0.051. A computer program comprising computer program code, that when executed on a computer, executes the method according to one of the claims 1 to 19. The computer program according to claim 22, comprising a user input for selecting and adjusting the first and second reference cycle times and/or the baseline. An MCG-System for characterizing a heartbeat cycle using MCG, comprising:

- at least one sensor configured to measure signals indicative of a magnetic field generated by a heart of a patient and to generate magnetic field data from said measured signals during at least a portion of at least one heartbeat cycle from the patient, and a processor unit configured to receive the magnetic field data and to execute the computer program according to one of the claims 22 or 23. The MCG-System for characterizing a heartbeat cycle according to claim 24, further comprising a display for displaying the distance value.