TECHNICAL FIELD The present invention relates to a method, and its implementation on a digital system, for detecting pace pulses in electrocardiogram (ECG) signal of a patient with a pacemaker.

BACKGROUND ART

Generally, a pace pulse that is acquired by the electrodes of an electrocardiograph, can be approximated by a square wave whose amplitude is bigger than 0.5 mV and whose duration lies within 0.1 to 2 ms [2]. The same square wave can overwhelm the physiological ECG signal in the diagnostic bandwidth (up to 150 Hz) [1]. Moreover, the overall signal that is acquired by the electrodes is typically characterized by different noise components that sometimes can disturb the pace pulse detection. A pacing pulse that is subject to a low-pass (LP) filtering in the diagnostic bandwidth, can be significantly widened depending on the filter impulse response; similarly when it is subject to a high-pass (HP) filtering, a tail at the end of the pulse can be created. This effects can cause both the pace pulse to overlap the physiological ECG signal and a failure of the pace pulse detection [2] [3]. For these reasons, it is necessary to detect and remove pacing pulses before applying any analog filter, which are typically used in ECG systems and typically work in the diagnostic bandwidth. The object of the present invention is a method to detect pace pulses in ECG signals, and its implementation in a fully digital equipment, with a flexible architecture, easily upgradable, which does not require any analog filtering in the diagnostic bandwidth before the ECG analog-to-digital conversion, and that is able to profusely exploit the electrical and statistical characterization of the pace pulse. Due to the high frequency nature of the pacemaker pulses, many methods and devices, like those proposed in [4] [5] [9], use dedicated analog circuitry to detect pacing pulses before the signal is passed to an analog-to-digital converter (A/D). Other methods and devices, such those in [6] [7], combine analog circuits with digital algorithms to aid the detection performance. Unlike the methods and devices that, because of their analog circuits, cannot be characterized by a flexible architecture, the fully digital methods and devices have a upgradable and flexible architecture that can be very useful to face with the evolution of the developed pacemakers. However, among all the known digital methods and algorithms, those that have a low complexity by employing simple linear filtering approaches [1] [2], cannot guarantee very good performance even in the presence of wide band electromyographic noise; on the other side, those algorithms [8] [11] [12] that employ a nonlinear filtering approach, do not fully exploit the pacemaker electrical and statistical characterization and thus are not totally protected against impulse and strong electromyographic noise.

DISCLOSURE OF INVENTION

In order to amplify the pace pulses with respect to the physiological ECG signal and also to all the noise components that affect an electrocardiogram measure, the present invention exploits the structure and the statistical characterization of the pace pulses in an ECG signal. The said goal is reached, by means of what has been discovered, by a procedure that includes a non-linear filtering that can enhance the pace pulses with respect to wide band noise and potentially, but not limiting, also a linear filtering whose aim is to remove great part of the ECG physiological signal and the noise in the diagnostic bandwidth. The said goal is reached, by means of what has been discovered, by a procedure that includes a non-linear filtering that can enhance the pace pulses with respect to wide band noise and potentially, but not limiting, also a linear filtering whose aim is to remove great part of the ECG physiological signal and the noise in the diagnostic bandwidth. The following description aids efforts to understand how the two filtering stages helps the pace pulses detection. The signal that is acquired by the electrocardiogram electrodes is composed by the addition of the physiological ECG signal, of the pace pulses and of the additive noise which in turn can be expressed by the addition of three components:

• baseline wander, due to the patient respiration and movement, with very low frequency content ([0.05-1] Hz) [10];

• power-line interference, which consist of a 50/60 Hz pickup (depending on whether we are in Europe or in U.S.A.), and whose amplitude can reach a maximum of 50% of the peak-to-peak ECG amplitude;

• electromyographic noise, which is introduced by the electrical activity, and is characterized by a wide band frequency content.

Thanks to the fact that the ECG frequency content is almost on the diagnostic bandwidth [0- 150] Hz, and that the pace pulses (whose minimum duration is 0.1 ms) are conversely characterized by significant high frequency energy due to the sharp transition at the rising and falling edges of the pulse, it is easy to understand that:

• the significantly different frequency contents allow us to isolate pace pulses from the ECG signal, the baseline wander and the power-line interference simply by means of a linear filtering, without modify the electrical characterization of the pace pulses,

• the statistical characterization of the pace pulses allow us to design a non-linear filter able to enhance them with respect to the wide band electromyographic noise, even if the two components have very similar frequency content,

• finally, the pace pulses detection is obtained simply by comparing the non-linear filter output with a given threshold. Summarizing, with reference to the present invention, in order to effectively sample also the pace pulses with minimum duration (0.1 ms), the analog signal, which is acquired by the electrodes of an electrocardiograph, is analog-to-digital converted with a sampling rate which is higher than those typically used in the diagnostic system. The digital signal is successively high-pass filtered in order to remove the low frequency content components due to the physiological ECG signal and to the baseline wander and power-line interference noise components. The following non-linear filter is designed first by sorting the digital samples in an opportunely defined temporal window, then by differentiating the samples with adjacent ranks (see (8)) which belong to the vector which has been previously sorted. This approach significantly improves the pace pulse detection performance with respect with the linear filtering methods conventionally used in ECG systems [1] [2]. Indeed, simply by using one or more linear filters, due to the very similar frequency content of the pace pulses and the electromyographic noise, noise spikes can be frequently and erroneously interpreted as pace pulses. Moreover, the proposed non-linear filtering, that is mainly characterized by a sorting procedure, provides very higher robustness against wide-band noise, also with respect to the typical non-linear filtering approaches described in [8] [11] [12] that, on the contrary, do not exploit the different statistical characterization of the pace pulses with respect to that one of the wide-band noise. Indeed, the non-linear filtering approach of the present invention, which represents the signal derivative in the sorted domain, allows the detector to avoid false detections, which are typically caused by wide-band noise spikes, because it would compare, with a high probability, a noise spike with another noise spike of quite the same amplitude. To this end, it is very important to correctly choose the set of samples that have to be sorted in order to avoid the possibility to have a single noise spike in the considered window, thus causing a false detection. BRIEF DESCRIPTION OF DRAWING

Fig. 1 represents the architecture of an electrocardiograph that exploits the pace pulse detection system described by this invention. Fig. 2 and 3 show the block diagram and the flowchart of a possible, but not limiting, embodiment of the pace pulse detection method which exploits the invention, respectively.

Fig. 4 (a) shows the pace pulse response of a possible, but not limiting, embodiment of the linear filter, while Fig. 4 (b) shows the absolute value of the corresponding frequency response of the same linear filter. Fig. 5 shows the flowchart of an efficient implementation, but not limiting, of the sorting algorithm which can be used by the pace pulse detection system and apparatus

DETAILED DESCRIPTION

In order to clarify the adopted notation that is used in the following description and in the attached figures, the digital signal s[n] , which is acquired by the electrodes of an electrocardiograph, is expressed as s[n] = x _ECG [n] + p[n] + w[n] , ( 1 ) where x _ECG [n] represents the physiological ECG signal in the absence of noise, p[ri] is the signal caused by the presence of the pacemaker and is the additive noise, which in turn is expressed as

w[«] = w _m [»] + ^W PLI M + ^W EMG W» ( ² ) where w _mv [n] , w _PLI [n] and w _mG [ή] are the baseline wander, the power-line interference and the electromyographic noise contribution, respectively. Even if a pacemaker pulse in an ECG signal may have both positive and negative amplitude, without loss of generality, the rest of the description and the attached figures refer simply to a sole positive impulse pace pulse. The following description relates to a possible, but not limiting, realization of an apparatus, which can be used into an electrocardiograph, for pace pulses detection that exploits the invention.

Fig. 1 shows how the electric signal of a patient with a pacemaker (101 ) is processed by a wide-band analog circuit (102) whose aim is to amplify the signal and adjust its dynamic range to the analog-to-digital converter (104) input. After the signal is low-pass filtered (103) in order to avoid aliasing, it is digitally converted (104) employing a sampling frequency f _s higher than those used by the typical monitoring systems, in order to guarantee the pace pulse detection and also to prevent the pacing pulses widening; the digital signal which is obtained this way is processed by the pace pulse detection method (105) in accordance with the present invention. Once the pacing pulses are detected, they can be easily removed (106) at the high sampling frequency f _s by simply replacing a portion of the signal before and after the time instant of the detected pulse with a signal average over the recent past. The high sampling frequency which is adopted, also permits to implement some or all the algorithm for the detection of the typical parameter of the high-resolution electrocardiography (109). Successively, it is possible to down-sample (107) the digital signal for a standard ECG monitoring system (108).

A possible, but not limiting, embodiment of the pace pulse detection method, in accordance with the invention, is described by the block diagram and the flowchart which are shown in Fig. 2 and Fig. 3 respectively. In this embodiment of the invention it is possible, but in a not limiting way, to consider an analog-to-digital conversion of the signal with a sampling frequency f _s = 10kHz . Optionally, it is possible to introduce a linear high-pass filter (201) and (301), which can be implemented by the average of two consecutive two-step (2 + / ) finite differences (derivatives), as summarized by the following equation s _HP [n] = s[n] + s[n -l] -(s[n - 2 - l] + s[n - 3 -l]). (3) The goal of this high-pass filtering is to enhance the rising and falling edges of the pacing pulse with respect to the low frequency contents of the physiological electrocardiograph signal. x _£CG [n] and of the noise components w _m [n] and .

Fig. 4 (a) shows the output of the linear filter when its input (401 ) is the electrocardiograph signal s[ή] where the pace pulse signal p[n] is positive and has a width of d samples, distinguishing the case (402) when I < d from the case (403) when / > d . In this exemplary embodiment, the signal s _HP \n\ is characterized by two trapezoidal waves with opposite amplitude, whose discrete duration l _F and distance d _F (total duration d _τoτ = 21 _F +d _F ) are Ip = min( d + 1, 1 + 3); d _F = max(d, l + 2) -l _F . (4)

Clearly, the parameter / in (4) controls the filter time-span and, consequently, frequency response H _HP (ώ) that is associated to eq. (3), and expressed by

TT , x , . -i<^- ■ ( 2 +lλ (ω\ , ^, ,

H _HP (ω) = 4j e - sin ω cos — , w) and whose absolute value \ H _HP (ω) \ is shown in Fig. 4 (b) for different values of the parameter / . It is clear that the low frequency content of the electrocardiograph signal and of the noise components w _BW [n] and w _PLI [n] are widely eliminated in all the conditions. For example, a possible, but not limiting, choice for the parameter / in the exemplary embodiment is to maximize the low frequency reduction and thus to use / = 0.

The linear filter output s _HP [n] is processed by the non-linear filtering stage expressed by (302) and (304). Specifically, we define the (N + T) -dimensional vector s _N corresponding to an observation window extended towards the (N + V) samples, as expressed by s _N = [s _HP [n], s _HP [n -k], s _HP [n - k -ϊ], ..., s _HP [n - k-N + l]]. (6)

It is clear that the observation window is composed by the current sample, which has to be detected as a pace pulse or not, and by the JV contiguous past samples (stored into a FIFO memory (203) and obtained by a delay block (202)) at a discrete temporal distance k .

A possible, but not limiting, choice of the parameter k , in an exemplary embodiment in 65 accordance with the present invention, could be to consider a 23 samples discrete temporal distance, which is not else but the discrete temporal distance d _τoτ = 21 _F + d _F between the beginning of the first and the end of the second of the two trapezoidal waveforms in Fig. 4 (a), when 1 = 0 , the sampling frequency f _s = 10 kHz , and d = 20 , which corresponds to the wider possible pace pulse duration [2]. This way, in the exemplary embodiment considered, it 0 is possible to avoid the comparison between rising and falling edges of the same pace pulse. Indeed, by using k > 23 , the observation window is composed only by the sample that potentially belongs to a pace pulse and by those samples that potentially do not belong to it. The non-linear filtering which is obtained by sorting (204) (302) the observation window s _N , produces a variational series \ _N [n] (303) expressed by S _v r _w -i _ L(D „(2) „(#+1) 1 _{with v} (i) < „(2) < < „ (N ₊ I) _m

/0 ^N l ⁿ i - [ ^S HP ' ^S HP > — ' ^S HP J> ^{WITπ S} HP - ^S HP ^ — ^ S _Hp . { / )

Thus we can define the rank R(γ _N [n],i) as the position of the sample s _HP [n — i] in the variational series Y _N [n] and as the value of the sample whose position is m in the variational series \ _N [n] . It is thus possible to define the differential rank dr[ri\ as

which is defined either as the difference between the current sample with the next (or previous) one in the variational series if the position of the current sample is smaller (or higher) of the median in the sorted version of the observation window, or as zero if the said position exactly corresponds to the median. The median of a variational series \ _N [n] of

85 N + l samples is defined as

V(y _N [n],N/2 + l) if N + l is odd m ^{edian(Υ[n]) =} \ ~(v(y _N [nUN-l)/2) ₊ V(y _N [nUN + l)/2)) if JV ^" + 1 is even O) We identify a pace pulse by comparing the differential rank with a threshold T (206) (305), which for example can be set to 0.35 mV. In order to avoid a false pulse detection induced by a sudden voltage rise caused by an electrode-skin contact loss, it is necessary

90 that the sample s[ή] with the whose corresponding dr[n] is bigger than the threshold T , is

. followed, within a discrete temporal distance L (306), by a sample s[k], obtained by the delay block (206), whose dr[k] has both its absolute value bigger than the threshold (308), and the opposite sign of dr[n] (309).

With reference to the parameters that have been chosen in this exemplary embodiments in

95 accordance with the present invention, a possible but not limiting choice of the parameter L is L = 23 samples, which represents the minimum distance between the two trapezoidal waveforms in s _HP [n] that are obtained by a pace pulse in p[ή] . In a wide band noise environment, typically caused by the electromyographic noise x _mG [ri] , the differential rank signal dr[ή] can increase the detector performance with respect to the simple use of s _HP [n] .

100 Indeed, if we would simply use the filtered signal s _HP [ή] , some noise spikes could produce some false detections; on the contrary, the differential rank signal , which represents the signal derivative in the sorted domain, allows the detector to avoid false detections because it would compare, with a high probability, a noise spike with another noise spike of quite the same amplitude. It is clear that if there is only a single noise spike into the

105 observation window, the detector may occurs in a false detection also with the differential rank signal dr[n] .

Thus, for this reason, it is very important to correctly choose the observation window width. In the exemplary embodiment, in accordance with the invention, a possible but not limiting choice of the parameter N of the observation window is JV = 100 as the result of a tradeoff no between reliability (the false detection probability is reduced by a wide support window) and accuracy (a smaller value of N reduces the probability to have a missed detection). Once a pacing pulse has been detected thus obtaining p[n] ≠ 0 in s[n] , the detection algorithm considers a refractory period of R samples (310) in which it does not search (309) for any other pulses in order to avoid multiple detections of the same pulse. A possible, but

115 not limiting choice of the refractory period R is i? = 1000 samples.

The flowchart of an efficient implementation of the sorting algorithm is shown in Fig. 5. The finite difference s _HP [z] of an ECG signal, which is obtained in a generic sampling interval z , is with high probability very similar to the value s _HP [z-ϊ\ that is calculated in the previous sampling interval. For this reason, for each step of the sorting algorithm, the variational

120 series \[n] is obtained by putting the samples s _HP [n] and s _HP [n -k] (that are represented by s _HP [z] in Fig. 5) into v[» -l] , which is obtained in the previous step, and discarding the samples s _HP [n -N -k] and $ _HP [n -l] from it. In order to decrease the computational complexity, instead of sorting the vector by comparing the samples s _HP [n] and s _HP [n — k] ($ _HP [∑] ) with those either at the outermost or in the middle of the previous variational series

125 \[n -1] , we exploit the low variability of the ECG signal by starting the sorting by comparing the new samples s _HP [n] and s _HP [n -k] with those samples in the previous variational series v[« -l] that correspond to the next samples (501) s _HP [n -l] and s _HP [n -k -l] (s _HP [z-ϊ\ ) and by moving towards higher or smaller values depending on the result of the comparison (502) (503). To this end, in order to both correctly insert the new samples s _HP [tt] and

130 s _HP [n -k-Y\ (504) (505) into and correctly remove the older ones s _HP [n -k -N] and s _HP [n -Y\ from the previous variational series \[n -ϊ] , it is important to control the structures by two doubly linked lists, the first to control the temporal sorting and the second to control the amplitude sorting of the samples. Bibliography

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