SIGNAL PROCESSING IN MAGNETOMETER FOR MEDICAL USE

Abstract

A method of using a magnetometer system to analyse the magnetic field of a region of a subject's body is disclosed. The method comprises obtaining one or more signals corresponding to the time derivative of the time varying magnetic field of a region of a subject's body, averaging the time derivative signal or signals over plural periods, and using the averaged time derivative signal or signals to analyse the magnetic field generated by the region of the subject's body.

Claims

1. A method of using a magnetometer system to analyse the magnetic field of a region of a subject's body, the method comprising: obtaining one or more signals corresponding to the time derivative of the time varying magnetic field of a region of a subject's body; averaging the time derivative signal or signals over plural periods; and using the averaged time derivative signal or signals to analyse the magnetic field generated by the region of the subject's body.

2. The method of claim 1, wherein obtaining one or more signals corresponding to the time derivative of the time varying magnetic field of the region of the subject's body comprises: using a detector to produce a signal having a time varying magnitude that corresponds to the time derivative of the time varying magnetic field of the region of the subject's body.

3. The method of claim 1, wherein obtaining one or more signals corresponding to the time derivative of the time varying magnetic field of the region of the subject's body comprises: using a detector to produce a signal having a time varying magnitude that corresponds the time varying magnetic field of the region of the subject's body; and differentiating the produced signal to obtain a signal corresponding to the time derivative of the time varying magnetic field of the region of the subject's body.

4. (canceled)

5. (canceled)

6. (canceled)

7. The method of claim 1, wherein the one or more obtained signals comprise one or more digitised signals, and the method comprises: averaging the digitised time derivative signal or signals over plural periods; and using the averaged digitised time derivative signal or signals to analyse the region of the subject's body.

8. The method of claim 1, wherein averaging the time derivative signal or signals over plural periods comprises: using a trigger to identify each repeating period of the time derivative signal or signals; and averaging the signal over the plural identified periods; wherein the trigger is determined using a time derivative signal or signals.

9. The method of claim 1, further comprising filtering the time derivative signal or signals.

10. The method of claim 1, wherein using the averaged time derivative signal or signals comprises at least one of: extracting one or more diagnostic parameters from the averaged time derivative signal or signals and using the averaged time derivative signal or signals without integrating.

11. (canceled)

12. The method of claim 1, wherein the region of the subject's body comprises one of: the abdomen, bladder, heart, head, brain, chest, womb, one or more foetuses, or a muscle.

13. A magnetometer system for medical use, comprising: one or more detectors for detecting the time varying magnetic field of a region of a subject's body; detection circuitry configured to obtain from the one or more detectors one or more signals corresponding to the time derivative of a detected time varying magnetic field; and averaging circuitry configured to average the time derivative signal or signals over plural periods; wherein the magnetometer system is configured to use the averaged time derivative signal or signals to analyse the magnetic field generated by the region of the subject's body.

14. The system of claim 13, wherein the one or more detectors and the detection circuitry is configured to produce a signal or signals having a time varying magnitude that corresponds to the time derivative of the time varying magnetic field of the region of the subject's body.

15. The system of claim 13, wherein the one or more detectors and the detection circuitry is configured to produce a signal or signals having a time varying magnitude that corresponds the time varying magnetic field of the region of the subject's body; and wherein the system further comprises processing circuitry configured to differentiate the magnetic field signal or signals to obtain the signal or signals corresponding to the time derivative of the time varying magnetic field of the region of the subject's body.

16. A electrocardiography system for medical use, comprising: one or more detectors for detecting the time varying electric potential of a region of a subject's body; detection circuitry configured to obtain from the one or more detectors one or more signals corresponding to the time derivative of a detected time varying electric potential; and averaging circuitry configured to average the time derivative signal or signals over plural periods; wherein the electrocardiography system is configured to use the averaged time derivative signal or signals to analyse the electric potential generated by the region of the subject's body.

17. The system of claim 16, wherein the one or more detectors and the detection circuitry is configured to produce a signal or signals having a time varying magnitude that corresponds to the time derivative of the time varying electric potential of the region of the subject's body.

18. The system of claim 16, wherein the one or more detectors and the detection circuitry is configured to produce a signal or signals having a time varying magnitude that corresponds to the time varying electric potential of the region of the subject's body; and wherein the system further comprises processing circuitry configured to differentiate the electric potential signal or signals to obtain the signal or signals corresponding to the time derivative of the time varying electric potential of the region of the subject's body.

19. The system of claim 13, wherein: the one or more obtained signals comprise one or more digitised signals; the averaging circuitry is configured to average the digitised time derivative signal or signals over plural periods; and the system is configured to use the averaged digitised time derivative signal or signals to analyse the region of the subject's body.

20. The system of claim 13, wherein the averaging circuitry is configured to average the time derivative signal or signals over plural periods by: using a trigger to identify each repeating period of the time derivative signal or signals; and averaging the signal over the plural identified periods; wherein the averaging circuitry is configured to determine the trigger using a time derivative signal or signals.

21. The system of claim 13, further comprising one or more filters configured to filter the time derivative signal or signals.

22. The system of claim 13, wherein the system is configured to analyse the averaged time derivative signal or signals by extracting one or more diagnostic parameters from the averaged time derivative signal or signals.

23. The system of claim 13, wherein the system is configured to analyse the averaged time derivative signal or signals without integrating the averaged time derivative signal or signals.

24. The system of claim 13, wherein the region of the subject's body comprises one of: the abdomen, bladder, heart, head, brain, chest, womb, one or more foetuses, or a muscle.

Description

[0179] A number of preferred embodiments of the present invention will now be described by way of example only and with reference to the accompanying drawings, in which:

[0180] FIG. 1 shows schematically the use of an embodiment of the present invention for detecting the magnetic field of a subject's heart;

[0181] FIGS. 2-5 show further exemplary arrangements of the use of an embodiment of the present invention when detecting the magnetic field of a subject's heart;

[0182] FIG. 6A shows schematically a coil arrangement in accordance with an embodiment of the present invention, and FIG. 6B shows schematically another coil arrangement in accordance with an embodiment of the present invention;

[0183] FIG. 7 shows a further exemplary arrangement of the use of an embodiment of the present invention when detecting the magnetic field of a subject's heart;

[0184] FIG. 8 shows a typical healthy ECG trace;

[0185] FIG. 9 shows three different ECG traces that are indicative of myocardial injury;

[0186] FIG. 10 shows ECG traces that exhibit baseline wander;

[0187] FIG. 11A shows raw ECG data exhibiting a large baseline shift; FIG. 11B shows the data of FIG. 11A filtered to remove baseline shifts; and FIG. 11C shows the derivative of the data of FIG. 11A without filtering;

[0188] FIG. 12 illustrates the extraction of the average heartbeat from the raw data of FIG. 11 and its integration to show the normal time domain view;

[0189] FIG. 13 shows data for a patient with Myocardial infarct;

[0190] FIG. 14 shows data for the same patient with Myocardial infarct where the signal is processed in the derivative;

[0191] FIG. 15 shows data for another patient with Myocardial infarct where the signal is processed in the derivative;

[0192] FIG. 16 shows data where the signal is processed in the derivative;

[0193] FIG. 17 shows the Fourier transform of the derivative and the integrated (normal) signal;

[0194] FIG. 18 illustrates a process in accordance with an embodiment of the present invention;

[0195] FIG. 19 illustrates an ideal band-pass filter in the frequency domain;

[0196] FIG. 20A shows a filter kernel formed from the difference between two windowed-sinc filters with cut-off frequencies at 8 Hz and 45 Hz, and M=2400, and

[0197] FIG. 20B shows the frequency response of the filter; and

[0198] FIGS. 21A-C show various arbitrary time domain ECG or MCG signals in the form of Gaussian peaks with the same centres and amplitudes but different FWHMs for each half, together with their corresponding time derivative signals; and

[0199] FIGS. 22A-F show various arbitrary time domain ECG or MCG signals in the form of sine waves with the same phase and amplitudes but different offsets together with their corresponding time derivative signals.

[0200] Like reference numerals are used for like components where appropriate in the Figures.

[0201] FIG. 1 shows schematically the basic arrangement of a preferred embodiment of a magnetometer system that may be operated in accordance with the present invention. This magnetometer system is specifically intended for use as a cardiac magnetometer (for use to detect the magnetic field of a subject's heart). However, the same magnetometer design can be used to detect the magnetic field produced by other body regions, for example for detecting and diagnosing bladder conditions, pre-term labour, foetal abnormalities and for magnetoencephalography. Thus, although the present embodiment is described with particular reference to cardio-magnetometry, it should be noted that the present embodiment (and the present invention) extends to other medical uses as well.

[0202] The magnetometer system comprises a detector 40 coupled to a detection circuit 41 that may contain a number of components. The detector 40 may be an induction coil 40.

[0203] The detection circuit 41 may comprise a low impedance pre-amplifier, such as a microphone amplifier, that is connected to the coil 40, a low pass filter, e.g. with a frequency cut off of 250 Hz, and a notch filter to remove line noise (e.g. 50 or 60 Hz and harmonics).

[0204] The current output from the coil 40 is processed and converted to a voltage by the detection circuit 41 and provided to an analogue to digital converter (ADC) 42 which digitises the analogue signal from the coil 40 and provides it to a data acquisition system 43.

[0205] A biological signal that is correlated to the heartbeat, e.g. an ECG or Pulse-Ox trigger from the test subject may be used as a detection trigger for the digital signal acquisition, and the digitised signal over a number of trigger pulses is then binned into appropriate signal bins, and the signal bins overlaid or averaged, by the data acquisition unit 43. Other arrangements would, however, be possible.

[0206] The coil 40 and detection circuit 41 may be arranged such that the coil 40 and the preamplifier of the detection circuit 41 are arranged together in a sensor head or probe which is then joined by a wire to a processing circuit that comprises the remaining components of the detection circuit 41. Connecting the sensor head (probe) and the processing circuit by wire allows the processing circuit to be spaced from the sensor head (probe) in use.

[0207] With this magnetometer, the sensor head (probe) will be used as a magnetic probe by placing it in the vicinity of the magnetic fields of interest.

[0208] FIG. 2 shows an improvement over the FIG. 1 arrangement, which uses in particular the technique of gradient subtraction to try to compensate for background noise. (Other techniques could, however, be used). In this case, an inverse coil 44 is used to attempt to subtract the effect of the background noise magnetic field from the signal detected by the probe coil 40. The inverse coil 44 will, as is known in the art, be equally sensitive to any background magnetic field, but only weakly sensitive to the subject's magnetic field. The inverse coil 44 can be accurately matched to the pickup coil 40 by, for example, using a movable laminated core to tune the performance of the inverse coil to that of the pickup coil 40.

[0209] FIG. 3 shows an alternative gradient subtraction arrangement. In this case, both coils 40, 44 have the same orientation, but their respective signals are subtracted using a differential amplifier 45. Again, the best operation is achieved by accurately matching the coils and the performance of the detection circuits 41. Again, a movable laminated core can be used to tune the performance of one coil to match the performance of the other.

[0210] FIG. 4 shows a further preferred arrangement. This circuit operates on the same principle as the arrangement of FIG. 3, but uses a more sophisticated method of field cancellation, and passive coil matching. In particular, a known global magnetic field 44 is introduced to both coils 40, 44 to try to remove background magnetic field interference.

[0211] In this circuit, the outputs from the detection circuits 41 are passed through respective amplifiers 47, 48, respectively, before being provided to the differential amplifier 45. At least one of the amplifiers 47, 48 is tuneable. In use, a known global field 46, such as 50 or 60 Hz (and harmonics) line noise, or a signal, such as a 1 kHz signal, applied by a signal generator 49, is introduced to both coils 40, 44. The presence of a signal on this frequency on the output of the differential amplifier 45, which can be observed, for example, using an oscilloscope 50, will then indicate that the coils 40, 44 are not matched. An amplifier control 51 can then be used to tune the tuneable voltage controlled amplifier 48 to eliminate the global noise on the output of the differential amplifier 45 thereby matching the outputs from the two coils appropriately.

[0212] Most preferably in this arrangement, a known global field of 1 kHz or so is applied to both coils, so as to achieve the appropriate coil matching for the gradient subtraction, but also a filter to remove 50 or 60 Hz (and harmonics) noise is applied to the output signal.

[0213] FIG. 5 shows a further variation on the FIG. 4 arrangement, but in this case using active coil matching. Thus, in this arrangement, the outputs of the coils 40, 44 are again channelled to appropriate detection circuits 41, and then to respective amplifiers 47, 48, at least one of which is tuneable. However, the tuneable amplifier 48 is tuned in this arrangement to remove the common mode noise using a lock in amplifier 52 or similar voltage controller that is appropriately coupled to the output from the differential amplifier 45 and the signal generator 49.

[0214] The above embodiments of the present invention show arrangements in which there is a single pickup coil that may be used to detect the magnetic field of the subject's heart. In these arrangements, in order then to make a diagnostic scan of the magnetic fields generated by a subject's heart, the single pickup coil can be moved appropriately over the subject's chest to take readings at appropriate spatial positions over the subject's chest. The readings can then be collected and used to compile appropriate magnetic field scans of the subject's heart.

[0215] It would also be possible to arrange a plurality of coil and detection circuit arrangements, e.g. of the form shown in FIG. 1, in an array, and to then use such an array to take measurements of the magnetic field generated by a subject's heart. In this case, the array of coils could be used to take readings from plural positions over a subject's chest simultaneously, thereby, e.g., avoiding or reducing the need to take readings using the same coil at different positions over the subject's chest.

[0216] FIGS. 6A and 6B show suitable coil array arrangements that have an array 60 of 16 detection coils 61, which may be then placed over a subject's chest to measure the magnetic field of a subject's heart at 16 sampling positions over the subject's chest. FIG. 6A shows a regular rectangular array and FIG. 6B shows a regular hexagonal array. In these cases, each coil 61 of the array 60 should be configured as described above and connected to its own respective detection circuit (i.e. each individual coil 61 will be arranged and have a detection circuit connected to it as shown in FIG. 1). The output signals from the respective coils 61 can then be combined and used appropriately to generate a magnetic scan of the subject's heart.

[0217] Other array arrangements could be used, if desired, such as circular arrays, irregular arrays, etc.

[0218] More (or less) coils could be provided in the array, e.g. up to 500 coils, or more than 500 coils. For example, where it is desired to measure the magnetic field of a different region of a subject's body (i.e. other than the heart), then an increased number of coils may be provided so as to provide an appropriate number of sampling points and an appropriate spatial coverage for the region of the subject's body in question.

[0219] It would also be possible in this arrangement to use some of the coils 61 to detect the background magnetic field for the purposes of background noise subtraction, rather than for detecting the wanted field of the subject's heart. For example, the outer coils 62 of the array could be used as background field detectors, with the signals detected by those coils then being subtracted appropriately from the signals detected by the remaining coils of the array. Other arrangements for background noise subtraction would, of course, be possible.

[0220] It would also be possible to have multiple layers of arrays of the form shown in FIG. 6, if desired. In this case, there could, for example, be two such arrays, one on top of each other, with the array that is closer to the subject's chest being used to detect the magnetic field generated by the subject's heart, and the array that is further away being used for the purposes of background noise detection.

[0221] To measure the magnetic fields generated by the heart, the above arrangements can be used to compile magnetic field scans of a subject's heart by collecting magnetic field measurements at intervals over the subject's chest. False colour images, for example, can then be compiled for any section of the heartbeat, and the scans then used, for example by comparison with known reference images, to diagnose various cardiac conditions. Moreover this can be done for significantly lower costs both in terms of installation and on-going running costs, than existing cardiac magnetometry devices.

[0222] FIG. 7 shows an exemplary arrangement of the magnetometer as it is envisaged it may be used in a hospital, for example. The magnetometer 30 is a portable device that may be wheeled to a patient's bedside 31 where it is then used to take a scan of the patient's heart (e.g.). There is no need for any magnetic shielding, cryogenic cooling, etc. The magnetometer 30 can be used in the normal ward environment. (Magnetic shielding and/or cooling could, however, be provided if desired.)

[0223] As used herein, a magnetometer or other apparatus in a magnetically shielded environment may comprise a magnetometer or other apparatus that is either arranged in a specially designed room or enclosure. In such arrangements, both the subject being measured and the equipment doing the measuring are contained within the same shielded enclosure. By contrast, as used herein, a magnetometer or other apparatus in a non-magnetically shielded comprises a magnetometer or other apparatus for which no external piece or pieces of apparatus are used to protect the subject being measured, nor the equipment doing the measuring.

[0224] The magnetometer system can be used in an analogous manner to detect and analyse other medically useful magnetic fields produced by other regions of the body, such as the bladder, head, brain, a foetus, etc.

[0225] FIG. 8 shows a typical ECG trace and the conventional labelling of the typical elements present in the ECG trace. Similar elements also occur in the MCG trace and the correspondence between the two has led to researchers using the same labelling convention.

[0226] As shown in FIG. 8, the ECG trace comprises a repeating P-P interval comprising a so-called P-wave, followed by a P-R (or P-Q) segment (where the combination of the P-wave and the P-R (or P-Q) segment is referred to as the P-R (or P-Q) interval), followed by a QRS complex, followed by an S-T segment, followed by a T-wave (where the combination of the S-T segment and the T-wave is referred to as the S-T interval, and the combination of the QRS complex and the S-T interval is referred to as the Q-T interval), followed by a T-P segment. Each of the features within the ECG can have diagnostic importance.

[0227] The signal generated by the induction coil in the present embodiment will be the derivative of the magnetic field. However, rather than integrating the output signal over time as would normally be done, the raw derivative signal is instead used for data analysis, etc.

[0228] Accordingly, the use of an induction coil represents a particularly convenient arrangement for obtaining a signal corresponding to the time derivative of the magnetic field, since for example, there is no need to differentiate the signal.

[0229] However, the Applicants have also found that there are benefits, e.g. as described below, when using a detector whose output signal corresponds to the time varying magnetic field, e.g. by differentiating the output signal to obtain a signal corresponding to the time derivative of the time varying magnetic field.

[0230] Thus, in the present embodiment, cardiac signals are analysed using the derivative of the magnetic field, dB/dt, rather than using the magnetic field B, as is conventional. Cardiac signals can also be analysed using the derivative of the voltage, dV/dt, rather than using the voltage V, as is conventional.

[0231] Analysis of the signal in the derivative is beneficial since, amongst other things, signal processing algorithms for the ECG and MCG must solve two conflicting problems, namely to remove background wander and to preserve biological signals which may have vital diagnostic importance. The conflict arises because frequently background effects can themselves have a biological origin.

[0232] FIG. 9A again shows an example of a normal, healthy ECG trace. FIGS. 9B and 9C show examples of ECG traces that indicate myocardial injury, where the S-T baseline is elevated (FIG. 9B) or depressed (FIG. 9C) with respect to the PR baseline. The MCG exhibits similar behaviour in corresponding regions of the complex albeit in different areas of the chest. The information content of the MCG is also different, with the S-T region being even more sensitive in MCG than in the ECG.

[0233] On the other hand, movement of a subject's limbs can cause baseline wander. FIG. 10 shows typical examples of baseline wander.

[0234] As such, movement of a subject's limbs can create low frequency baseline wander in the ECG signal, while a small shift in the S-T segment of the ECG can indicate myocardial infarction.

[0235] Separating baseline wander from shifts in the S-T baseline is relatively straightforward for a trained physician, but is somewhat more difficult for automated algorithms. The same cannot be said for MCG, principally because MCG lacks the decades of understanding and analysis that underpin the analysis of ECG. However, for a signal processing algorithm the task of separating the two is very challenging.

[0236] In the present embodiment, baseline wander is removed from the signal by using the derivative signal. Baseline drift is very low in frequency and therefore the derivative (dV/dt) is very small (i.e. dt is very large when the frequency of the drift is so small), so that using the derivative can negate the presence of baseline wander in the analysis of the results.

[0237] FIG. 11 shows ECG plots from a healthy volunteer (PTB ECG database). FIG. 11A shows the raw data showing large baseline shifts, FIG. 11B shows this data filtered to remove baseline shifts, and FIG. 11C shows the derivative data without filtering. It will be appreciated that the derivative data shown in FIG. 11C does not show a baseline shift.

[0238] The use of the derivative signal can accordingly remove or reduce the need for filtering. This is beneficial since with filtering there is always a risk that real signal will be removed when removing noise. This is particularly the case where the baseline drift is itself a biological signal. This accordingly means that more of the wanted signal can be retained for further analysis.

[0239] In the present embodiment, the derivative data is acquired repeatedly, and signal averaging techniques are applied to the data to produce the average heartbeat. This process is illustrated by FIG. 12.

[0240] FIG. 12A again shows the raw data from the healthy volunteer as shown in FIG. 11C. As illustrated by FIG. 12B, in the present embodiment this data is averaged over plural repeating periods to determine the average heartbeat. The average heartbeat of FIG. 12B may be used for diagnostic purposes. As shown in FIG. 12C, the average (derivative) heartbeat may be integrated to determine the average integrated heartbeat.

[0241] FIG. 12 shows the process of resolving a signal using the derivative. It can be seen in FIG. 12B that some frequency components that can be seen in the derivative are not visible in the normal time domain view. It can be seen from FIG. 12C that the integrated signal loses high frequency information.

[0242] FIG. 13 shows ECG data for a patient with Myocardial infarct indicated by the presence of S-T segment baseline shift. FIG. 13B shows the average heartbeat after averaging the raw data of FIG. 13A, where an S-T segment baseline shift can be seen in the average heartbeat. FIG. 13C shows the derivative of the averaged heartbeat.

[0243] FIGS. 13D-F show corresponding data where bandpass filtering has been applied. The use of bandpass filtering reduces the S-T segment shift because low frequency components are suppressed. This can be seen most clearly in the derivative, i.e. by comparing FIG. 13C (no filtering) with FIG. 13F (with filtering).

[0244] It can be seen from FIG. 13C that higher noise is present in the derivative due to an enhanced sensitivity to high frequency components. Lower frequency components are suppressed in FIG. 13F. FIG. 13E shows alterations to the T-wave, and changes to the R-peak structure.

[0245] FIG. 14 shows data for the same patient as FIG. 13, where the signal is processed in the derivative and then integrated. FIG. 14A shows the raw derivative data, FIG. 14B shows the data of FIG. 14A after averaging, and FIG. 14C shows the integrated version of the averaged heartbeat of FIG. 14B. FIGS. 14D-F show corresponding data where filtering has been used.

[0246] As can be seen from FIG. 14, the need to filter the signal to remove low frequency drift is obviated and the baseline S-T shift is retained by processing the data in the derivative.

[0247] FIG. 14E shows that higher frequency components are suppressed when the sound is filtered (as expected). However, as seen in FIG. 14F, the baseline shift is retained, the R-peak structure is only slightly altered, and the T-wave structure is unaltered.

[0248] As such, the Applicants have recognised that the derivative is a useful tool because (i) high frequency information that has a diagnostic value is naturally present in the derivative; and (ii) no additional filtering is necessary to arrive at the average heartbeat.

[0249] In addition, the low frequency structure of the signal is the same even if bandpass filtering is applied.

[0250] FIG. 15 show shows corresponding data to FIG. 14 but from a different Myocardial Infarct patient with an S-T baseline shift. FIG. 15C shows a baseline shift. The band pass signal in FIG. 15E shows no baseline shift, and FIG. 15F shows that low frequency data is removed. This illustrates that the results are repeated.

[0251] FIG. 16 illustrates why it is possible to filter and retain the relevant information when processing the data in the derivative. As shown in FIG. 16A, in the derivative, important information relating to the baseline shift is effectively moved from the S-T region into the QRS complex. The region marked peak height determines the R-Wave peak height and the region marked peak drop determines the subsequent fall. If these two regions have a similar area then there is very little baseline shift.

[0252] This can be seen in FIG. 17, where the frequency components of the two signals are compared. FIG. 17 compares the Fourier transform of the derivative (FIG. 17A) with the integrated (normal) signal (FIG. 17B). There is considerably less low frequency information in the derivative. However, it can be seen from the analysis above that the information concerning the state of the heart is preserved.

[0253] It will be appreciated that the derivative naturally reduces the scale of low frequency information, shifting it to higher frequencies within the complex. This, in turn, allows for filtering to be applied without destroying the relevant information.

[0254] FIG. 18 shows a sequence of data processing steps in accordance with the present embodiment.

[0255] A sensor 40 and a digitiser 42 are used to obtain a digitised derivative signal 101. As discussed above, this may be done by using the natural signal from a sensor that is configured to output a derivative signal, or by differentiating the digitised signal output from a sensor that is configured to output a magnetic field B or voltage V signal.

[0256] The differentiation may be performed in any suitable manner. Where, for example, the digitised signal comprises a sequence of values,

V(t)=[V.sub.1,V.sub.2,V.sub.3, . . . ,V.sub.n],

and where the values V.sub.i, V.sub.i+1 are separated by a fixed time step t, then the derivative may be approximated by:

[00003]$\frac{\mathrm{dV}}{\mathrm{dt}}\left[\frac{V_1-V_2}{.Math..Math.t},\frac{V_2-V_3}{.Math..Math.t},\frac{V_3-V_4}{.Math..Math.t},\mathrm{.Math.}.Math.,\frac{V_{n-1}-V_n}{.Math..Math.t}\right].$

[0257] The digitised derivative signal is then averaged 102 over plural periods. This involves using a trigger such as the ECG to determine the plural repeating periods of the signal. Data is taken from the target waveform in each of plural windows around each of plural triggers. Several subsequent windows are averaged to remove random noise.

[0258] The use of the derivative signal is beneficial for this triggering operation, because the trigger is normally defined by the shape of the wave, or by a threshold detection. In either case, the removal of low frequency baseline shifts can improve triggering. Signal averaged ECG normally uses a trigger point derived from the ECG as the averaging position. This is prone to errors arising from baseline shifts whereas trigger derived from the derivative is not.

[0259] Additional filtering 103 may be applied, e.g. to remove noise that cannot be removed by averaging. For example, the digitised time derivative signal or signals may be filtered using (i) a notch filter to remove power line noise; and/or (ii) a bandpass filter to remove environmental noise. The digitised time derivative signal or signals may be filtered to remove external magnetic noise, e.g. arising from power lines and other environmental noise sources such as elevators, air conditioners, nearby traffic, mechanical vibrations, etc.

[0260] The Applicants have found, in particular, that a bandpass filter having a passband around 8-45 Hz can be used to separate the MCG signal from the environmental noise and background noise. The filter is a band pass filter constructed as combination of a high pass filter (removing environmental noise <10 Hz), and a low pass filter (removing background noise >50 Hz).

[0261] FIG. 19 illustrates an ideal band-pass filter. An idealised filter is one that removes all frequency components above a given cutoff frequency, without affecting lower frequencies, and has linear phase response. All frequencies within the passband 10-50 Hz, are passed with unity amplitude, while all other frequencies are blocked. The passband is perfectly flat, the attenuation in the stopband is infinite, and the transition between the two is infinitely small. The filter's impulse response is a sinc function in the time domain, and its frequency response is a rectangular function. It is an ideal low-pass filter in the frequency sense, perfectly passing low frequencies, perfectly cutting high frequencies, and thus may be considered to be a brick-wall filter.

[0262] In the present embodiment, in order to approximate such an ideal filter, two windowed-sinc filters are combined to construct a band-pass filter that can separate the MCG signal from environmental noise and background noise. This allows for an efficient separation of the QRS-complex from the environmental noise and other background noise interferences, without phase distortions.

[0263] The filter is configured such that it removes all frequency components below a cut-off frequency f.sub.c1 and above a cut-off frequency f.sub.c2 without affecting frequencies in between. The filter is designed as the difference of two windowed-sinc filters whose cut-off frequencies are f.sub.c1 and f.sub.c2. The filter is able to significantly reduce the impact of environmental noise on the MCG signal, specifically the depolarisation (QRS) section.

[0264] FIG. 20A shows the filter kernel and FIG. 20B shows the frequency response of the difference of two windowed-sinc filters with cut-off frequencies f.sub.c1=0.0033 (8.0 Hz), f.sub.c2=0.01875 (45.0 Hz) and M=2400. The filter acts as a band pass filter.

[0265] The filter can be applied in either the time domain or the frequency domain to effectively separate the repolarisation (QRS section) of the MCG signal from the BCG effects and background noise.

[0266] Returning now to FIG. 18, diagnostic information can be extracted in the derivative, e.g. after other noise sources have been identified and removed.

[0267] Thus, following any additional data processing 104, diagnostic parameter extraction 105 may be performed, and used for analysis 106.

[0268] Some examples of medically useful signals that may be analysed are (i) S-T baseline shifts (STEMI) e.g. S-T elevated myocardial infarction (on taking the derivative, this becomes the R-S signal height); and (ii) R-S transition rate, e.g. bundle branch block (on taking the derivative, this becomes the R-S signal height).

[0269] However, in general any of the signal features described herein may have diagnostic importance and may be used for analysis. On taking the derivative, parameters that depend on a rate become a height, and parameters where a transition produces level shifts becomes a measurement of area.

[0270] It will be appreciated that, in the present embodiment, the derivative is used for analysis. The derivative emphasises high frequency information and supresses low frequency information. High frequency information can be diagnostic on its own. In addition, the derivative removes background drift without the need for filtering. It also concentrates information relating to S-T transition level shifts in the R-S region. This is a higher frequency region and therefore this signal can be separated from lower frequency components.

[0271] It should be noted that when analysing the magnetic field in the derivative domain, the rate of change, gradient, or slope of a feature may be used. The gradient of a feature in the integral corresponds to the amplitude of a feature in the derivative. This can allow more detailed or accurate diagnostic information to be obtained.

[0272] For example, as illustrated by FIGS. 21A-C, in the time domain ECG (and in the time domain MCG) a signal feature such as the QRS complex may comprise one or more single peaks. FIG. 21A shows a symmetric signal feature, FIG. 21B shows a slightly asymmetrical signal feature and FIG. 21C shows a moderately asymmetric feature.

[0273] As can be seen e.g. by comparing FIGS. 21A and 21B, it can be challenging to determine (or accurately measure), e.g., a slight imbalance or asymmetry in the (e.g. QRS) peak, e.g. if one side of the peak falls faster or slower than it rises (or vice versa).

[0274] By contrast, when using the derivative (MCG or ECG) signal, a signal feature (e.g. QRS complex) comprises two peaks, one corresponding to the rising edge (e.g. QR) and one to the falling edge (e.g. the RS) of the time domain feature (e.g. QRS complex). This means that, when using the derivative domain, any difference (imbalance or asymmetry) as described above is much easier to detect, e.g. since the two peaks will have different shapes and/or amplitudes. The same is true for other peaks and signal features in the averaged time derivative signal or signals.

[0275] In addition, small fluctuations on large absolute values (e.g. signals with large offsets or DC biases) can more readily be seen in the derivative than the integral. This is because upwards or downwards trends (or gradients/slopes) can be seen as positive or negative features in the derivative. For a sufficiently offset (or biased) signal, all values may remain positive (or negative) despite small fluctuations making it difficult to establish a trend.

[0276] This is illustrated by FIGS. 22A-F. FIGS. 22A and 22C show arbitrary time domain signals with and without an offset. By contrast, FIGS. 22B and 22D show the same signals in the derivative, where it can be seen the effect of the offset has been removed.

[0277] As also illustrated by FIGS. 22A-D, a large absolute value with a small fluctuation (e.g. 100010) is no different from a small absolute value with the same fluctuation (e.g. 110) in the derivative as only the fluctuation (e.g. 10) is observed (i.e. as a peak with 10 amplitude and a second peak with +10 amplitude). In the integral these fluctuations are 1% and 1000% respectively of the absolute signal value, and can make it hard to locate a threshold with variable data, particularly in the case of a large absolute value with a small fluctuation (e.g. 100010) as all values may be positive.

[0278] In addition, for datasets or signals with increasing (or decreasing) DC or low frequency offsets, small fluctuations can more readily seen in the derivative than the integral. This is illustrated by FIGS. 22E-F, where it can be seen that subtle changes can be picked up in the derivative even when the offset is not constant.

[0279] As such, using the derivative domain in the manner of various embodiments can make diagnostic measurements more resistant to offsets or (e.g. DC) biases, i.e. since only change is measured. This can make it easier to deal with situations, for example, where a threshold value is of interest and is required to be measured. In particular, this can address the situation where, for example, it is desired to determine the value or location of a change from a positive to negative value in the MCG signal, but where because of an offset or (e.g. DC) bias, all values of the signal are positive or negative.

[0280] It can be seen from above that the present invention provides an improved magnetometer system for medical use.

[0281] This is achieved, in the preferred embodiments of the present invention at least, by obtaining one or more signals corresponding to the time derivative of the time varying magnetic field of a region of a subject's body, averaging the signal or signals, and using the averaged signal or signals to analyse the magnetic field generated by the region of the subject's body.