Automatic Method For Measuring a Baby's, Particularly a Newborn's, Cry, and Related Apparatus

Abstract

The present invention concerns an automatic method for measuring a baby's cry, comprising the following step: A. having N samples ρ(i), for i=O, 1, . . . , (N−1), of an acoustic signal p(t) representing the cry, sampled at a sampling frequencŷ for a period of duration P; the method being characterised in that it assigns a score PainScore to the acoustic signal p(t) by means of a function AF of one or more acoustic parameters selected from the group comprising: —a root-mean-square or rms value prms of the acoustic signal p(t) in the period P; —a fundamental or pitch frequency F0 of the acoustic signal p(t), i.e. the minimum frequency at which a peak in the spectrum of the acoustic signal p(t) occurs in the period P; and—a configuration of amplitude and frequency modulation of the acoustic signal p(t) in the period P. The invention further concerns the apparatus performing the method.

Description

The present invention relates to an automatic method for measuring a baby's, particularly a newborn's, cry, and the related apparatus, that allows in a simple, reliable, and inexpensive way to provide an indication of the pain level suffered by the baby starting from the analysis of his/her cry acoustic characteristics.

Pain has different levels, quantifiable from zero up to a maximum, and the behaviour of babies consequently varies. In the last years, pain scales have been developed for discriminating the level of pain suffered by a newborn.

By way of example, the score scale known as Newborn's Sharp Pain, or DAN (Douleur Aiguë Nouveau-né), evaluates facial expressions, limb movements, and newborn's vocalizations for generating a score ranging from 0 (corresponding to lack of pain) and 10 (corresponding to maximum pain).

However, such scales are hardly usable, since they cannot be easily automated so as to provide objective and repeatable indications, because they require an active evaluation by an operator.

It is therefore an object of the present invention to provide in a simple, reliable, and inexpensive way an automatic, and hence objective and repeatable, indication of a baby's, in particular a newborn's, pain level.

It is specific subject matter of the present invention an automatic method for measuring a baby's cry, comprising the following step:

• A. having N samples p(i), for i=0, 1, . . . , (N−1), of an acoustic signal p(t) representing the cry, sampled at a sampling frequency f_s for a period of duration P;the method being characterised in that it assigns a score PainScore to the acoustic signal p(t) by means of a function AF of one or more acoustic parameters selected from the group comprising:
  • a root-mean-square or rms value p_rms of the acoustic signal p(t) in the period P;
  • a fundamental or pitch frequency F₀ of the acoustic signal p(t), i.e. the minimum frequency at which a peak in the spectrum of the acoustic signal p(t) occurs in the period P; and
  • a configuration of amplitude and frequency modulation of the acoustic signal p(t) in the period P.

In other words, the automatic method according to the invention measures a baby's, in particular a newborn's, cry starting from its time and/or spectral acoustic analysis.

In particular, the method is based on recording and analysing newborn's cry. The pain level is preferably assigned through the combined evaluation of a set of one or more measurable acoustic parameters, which are related to the pain level. A quantitative estimate of the pain level is obtained on the basis of a validated pain scale, based on the cry acoustic characteristics.

The acoustic parameters used for the diagnosis comprise one or more of the following three ones: the fundamental or pitch frequency; the normalised amplitude, with respect to the maximum value, of the root-mean-square or rms value; and the presence of a specific characteristic of cry frequency and amplitude modulation, which characteristic is defined as “siren cry”. The method provides as output value a score, preferably ranging from 0 to 6, that is proposed as an adequate scale for describing the pain level.

Further characteristics of other embodiments of the method according to the invention are defined in the enclosed claims 2-29.

It is still subject matter of the present invention an apparatus for measuring a baby's cry, comprising processing means, characterised in that it is capable to perform the previously described automatic method for measuring a baby's cry, the apparatus preferably further comprising means for detecting acoustic signals, and sampling means, capable to sample said acoustic signals.

In other words, the apparatus according to the invention performs the aforementioned automatic method for measuring a baby's cry, through an automatic acoustic analysis of the newborn's cry, in order to provide an objective estimate of the newborn's pain level.

The present invention will now be described, by way of illustration and not by way of limitation, according to its preferred embodiments, by particularly referring to the Figures of the enclosed drawings, in which:

FIG. 1 shows a flow chart of a preferred embodiment of the method according to the invention;

FIG. 2 shows a detailed flow chart of step 2 of the method of FIG. 1;

FIG. 3 shows a graph of the rms values of normalised acoustic signals during cry sequences of 24 seconds as a function of the DAN scale;

FIG. 4 shows a detailed flow chart of step 3 of the method of FIG. 1;

FIG. 5 shows a graph of the values of the fundamental frequency F₀ as a function of the DAN scale; and

FIG. 6 shows a detailed flow chart of step 4 of the method of FIG. 1.

In the following of the description same references will be used to indicate alike elements in the Figures.

As mentioned, the cry acoustic parameters which are measured by the method according to the invention for providing a measure of the cry, indicative of the pain level suffered by the baby, comprise:

• • the normalised amplitude, with respect to the maximum value, of the root-mean-square or rms value of the acoustic signal;
  • the fundamental frequency or pitch of the acoustic signal;
  • the persistence of regular configurations of frequency and amplitude modulation (configurations defined as “siren cry”).

The higher the values of such acoustic parameters are, the higher is the pain level of the baby.

The normalised to its maximum value rms value is not a measure of the cry absolute intensity, but it is rather a measure of the emission constancy: in other words, it measures the fraction of the observation time along which the signal is close to its maximum. This is related to the pain level, since a suffering newborn tends to cry for long time close to its maximum reachable level. Preferably, a normalised rms value over 0.15-0.2 is associated with high pain levels.

The fundamental frequency or pitch is typically higher in cry caused by pain. A pitch frequency over 350-450 Hz is typically correlated with high pain levels.

Another specific characteristic of cry due to a high pain is the regularity and reproducibility of the configurations of amplitude and frequency modulation on a short time scale, of the order of 1 second, which configurations define the so-called siren cry, with a persistent configuration lasting several periods. The time-frequency intensity configuration of this siren cry shows a periodical modulation of the fundamental frequency F₀ and of its multiple frequencies, while the mean power spectrum has a quasi-periodical peak structure.

All the three cry acoustic parameters described above are correlated with the pain level, independently evaluated by using the DAN score scale.

With reference to FIG. 1, it may be observed that a preferred embodiment of the method according to the invention comprises a step 1 of acquiring N samples p(i), for i=0, 1, . . . , (N−1), of the acoustic signal p(t) that is sampled at a suitable sampling frequency f_s (taking into account that the Nyquist frequency is equal to f₂/2) for a period of duration P. Preferably, P is not shorter than 20 seconds, and N is equal to an involution of 2 (N=2^A).

Afterwards, the method comprises a step 2 of processing a first score on the basis of the root-mean-square value in the period P of the N samples p(i) of the acoustic signal p(t).

The method still comprises a step 3 of processing a second score on the basis of the fundamental or pitch frequency F₀ of the acoustic signal p(t), that is on the basis of the minimum frequency at which a peak in the spectrum of the acoustic signal p(t) occurs.

Furthermore, the method comprises a step 4 of processing a third score on the basis of the characteristic defined as “siren cry”, preferably not null only in case of persistent cry, i.e. with value of the first score larger than a corresponding threshold value.

Finally, the method comprises a step 5 of adding up the three calculated scores, that is given as output in a step 6.

With reference to FIG. 2, it may be observed that step 2 comprises:

• • a sub-step 21 of determining the maximum amplitude Oman of the acoustic signal p(t) in the period P:

$p_{\max }=\underset{i=0,1,\dots ,\left(N-1\right)}{\max }\left\{p\left(i\right)\right\}$

• • a sub-step 22 of calculating the root-mean-square value of the acoustic signal p(t), normalised to its maximum amplitude p_max, in the period P:

$p_{\mathrm{rms}}^{\mathrm{norm}}=\sqrt{\frac{1}{N}\sum _{i=0}^{\left(N-1\right)}{\left(\frac{p\left(i\right)}{p_{\max }}\right)}^2}$

• • a sub-step 23 of assigning the first score to the normalised rms value p_rms^norm, by means of a first, either continuous or discrete, preferably monotonic not decreasing, function g₁(p_rms^norm).

In particular, FIG. 3 shows the rms values of the normalised acoustic pressure during a cry sequence of 24 seconds, as a function of the DAN scale.

Preferably the first function g₁(p_rms^norm) is continuous, more preferably equal to:

$\begin{array}{cc}\mathrm{score}\left(p_{\mathrm{rms}}^{\mathrm{norm}}\right)=\frac{2}{\pi }\arctan \left(\alpha \left(p_{\mathrm{rms}}^{\mathrm{norm}}-\beta \right)\right)+1& \left[1\right]\end{array}$

where coefficients α and β are preferably equal to the following values:

α=100

β=0.14  [2]

so that the values of score(p_rms^norm) meet the following conditions:

for p_rms^norm<<0.1 it is score(p_rms^norm)≈0

for p_rms^norm=0.1 it is score(p_rms^norm)=0.15

for p_rms^norm=0.14 it is score(p_rms^norm)=1

for p_rms^norm=0.18 it is score(p_rms^norm)=1.85

for p_rms^norm>>0.18 it is score(p_rms^norm)≈2

Alternatively, the first function g₁(p_rms^norm) may be discrete, so that the possible values of p_rms^norm are subdivided into at least two ranges to which a respective value of score(p_rms^norm) corresponds. Preferably, such discrete function may be the following:

$\mathrm{score}\left(p_{\mathrm{rms}}^{\mathrm{norm}}\right)=\{\begin{array}{cccc}0& \mathrm{per}& 0\le p_{\mathrm{rms}}^{\mathrm{norm}}<0,1& \\ 1& \mathrm{per}& 0,1\le p_{\mathrm{rms}}^{\mathrm{norm}}<0,18& \\ 2& \mathrm{per}& p_{\mathrm{rms}}^{\mathrm{norm}}\ge 0,18& \end{array}$

With reference to FIG. 4, it may be observed that step 3 of FIG. 1 comprises a sub-step 31 of subdividing the N samples p(i) into M time intervals, of duration equal to D=P/M, wherein M is preferably equal to an involution of 2 (M=2^B, with B≦A), each one of which hence comprises N_D samples, with

N _D =N/M=2^(A-B).

In order to avoid in the successive frequency analysis the introduction of spurious spectral characteristics caused by cutting the waveform off, in sub-step 31 a Hanning window W_H(j) (for 0, 1, . . . , (N_D−1)) is applied to each interval, thus obtaining, for each one of the M intervals, N_D samples p_Hk(j) (where k is the interval index, i.e. k=0, 1, . . . , (M−1));

p _Hk(j)=p(N_D·k+j)·W_H(j)

• • for j . . . , (N_D−1) and k=0, 1, . . . , (M−1)

In successive sub-step 32, it is calculated for each interval the power spectrum of the digitised signal:

S _Hk(j)=FT_ND{p_Hk(j)}

• • for j=0, 1, . . . , (N_D−1) and k=0, 1, . . . , (M−1)

where y(j)=FT_ND{x(j)} indicates the operator FT_ND (preferably the Fourier transform of the autocorrelation function) that transforms N_D samples x(j) from the time domain to N_D samples y(j) in the frequency domain. As a consequence, in sub-step 32 it is obtained a time sequence of M spectra, each one with a frequency resolution R_f equal to:

R _f =f _s /N _D

and a bandwidth B1 equal to the Nyquist frequency:

B1=f_s/2.

Afterwards, in sub-step 33 it is calculated the mean spectrum S_Hk(j) of the M spectra:

$\begin{array}{cc}\stackrel{\_}{S_{\mathrm{Hk}}}\left(j\right)=\frac{1}{M}\sum _{k=0}^{M-1}S_{\mathrm{Hk}}\left(j\right)& \mathrm{for}j=\end{array}0,1,\dots ,\left(N_D-1\right)$

Sub-step 34 determines the mean value S_mean of the mean spectrum S_Hk(j) in a first frequency range included between two respective frequency limit values F₁ and F₂ (to which two indexes correspond j₁=F₁/R_f and j₂=F₂/R_f), preferably included within the low frequency part of the spectrum bandwidth B1:

$S_{\mathrm{mean}}=\stackrel{\_}{S_{\mathrm{Hk}}}\left(j\right)=\frac{1}{\left(\frac{F_2-F_1}{R_f}+1\right)}\sum _{j=\frac{F1}{\mathrm{Rf}}}^{\frac{F2}{\mathrm{Rf}}}\stackrel{\_}{S_{\mathrm{Hk}}}\left(j\right)$

Sub-step 35 determines the pitch F₀ as the minimum frequency at which a peak of the mean power spectrum S_Hk(j) occurs. In particular, sub-step 35 determines the frequency F₀ as the one corresponding to the first peak of the mean spectrum (i.e. to the first relative maximum) the value of which is larger than a threshold T1, preferably equal to the mean level S_mean of the mean spectrum added to an offset value Δ1, possibly even negative, preferably equal to 5 dB:

F ₀ =R _f·min{j|max_relative[ S_Hk(j)]>T1=S_mean+Δ}

This definition of the pitch F₀ is independent from the absolute calibration.

In particular, FIG. 5 shows the values of the fundamental frequency F₀, as a function of the DAN scale. The continuous line is an interpolation of all the data, while the two dotted lines are two different interpolations for the data related to cries of newborns with DAN≦8 and with DAN≧8.

Still with reference to FIG. 4, step 3 finally comprises a sub-step 36 of assigning the second score to the value of fundamental frequency or pitch F₀, by means of a second, either continuous or discrete, preferably monotonic not decreasing, function g₂(F₀).

Preferably the second function g₂(F₀) is continuous, more preferably equal to:

$\begin{array}{cc}\mathrm{score}\left(F_0\right)=\frac{2}{\pi }\arctan \left(\gamma \left(F_0-\delta \right)\right)+1& \left[3\right]\end{array}$

where coefficients γ and δ are preferably equal to the following values:

γ=100

δ=0.4  [4]

so that the values of score(F₀) meet the following conditions:

for F₀<<350 Hz it is score(F₀)≈0

for F₀=350 Hz it is score(F₀)=0.13

for F₀=400 Hz it is score(F₀)=1

for F₀=450 Hz it is score(F₀)=1.87

for F₀>>450 Hz it is score(F₀)≈2

Alternatively, the second function g₂(F₀) may be discrete, so that the possible values of F₀ are subdivided into at least two ranges to which a respective value of score(F₀) corresponds. Preferably, such discrete function may be as follows:

$\mathrm{score}\left(F_0\right)=\{\begin{array}{ccc}0& \mathrm{for}& F_0<400\mathrm{Hz}\\ 2& \mathrm{for}& F_0\ge 400\mathrm{Hz}\end{array}$

With reference to FIG. 6, it may be observed that step 4 of FIG. 1 comprises a sub-step 41 in which, for each digitised power spectrum S_Hk(j) of the signal, obtained in sub-step 32 of FIG. 4, it is calculated the energy contribution E_F3—F4(k) in a second frequency range included between two respective frequency limit values F₃ and F₄ (to which two indexes j₃=F₃/R_f and j₄=F₄/R_f correspond), preferably included within the low frequency part of the spectrum bandwidth B1. In other words, it is calculated the integral (i.e., the sum of the digitised values) of the spectrum between F₃ and F₄:

$E_{\mathrm{F3\_F}4}\left(k\right)=\sum _{j=F3/\mathrm{Rf}}^{F4/\mathrm{Rf}}S_{\mathrm{Hk}}\left(j\right)$ $\mathrm{for}k=0,1,\dots ,\left(M-1\right)$

In sub-step 42, it is calculated the mean value E_{F3_F4} along time of the energy contribution E_F3—F4(k):

$\stackrel{\_}{E_{\mathrm{F3\_F4}}\left(k\right)}=\frac{1}{M}\sum _{k=0}^{M-1}E_{\mathrm{F3\_F4}}\left(k\right)$

In sub-step 43, it is calculated the deviation ΔE_F3—F4(k) of the energy contribution E_F3—F4(k) in the second frequency range with respect to its mean value E_{F3_F4}:

ΔE_F3—F4(k)=E_F3—F4(k)− E_{F3_F4}

• • for k=0, 1, . . . , (M−1)

In sub-step 44, a window W_flat-top(k) (for k=0, 1, . . . , (M−1)) having spectrum with flat top main lobe, known as flat-top window, is applied to such deviation, thus obtaining M samples ΔE_F3—F4^flat-top(k):

ΔE_F3—F4^flat-top(k)=ΔE_F3—F4(k)·W^flat-top(k)

• • for k=0, 1, . . . , (M−1)

In next sub-step 45, it is calculated the digitised power spectrum V^F3—F4(k) of the signal ΔE_F3—F4^flat-top(k) obtained from sub-step 44, that is indicative of the frequency components of the variation dynamics of the energy contribution E_F3—F4(k) in the second frequency range:

V ^{F3 — F4}(k)=FT_M{ΔE_F3—F4^flat-top(k)}

• • for k=0, 1, . . . , (M−1)thus obtaining M samples V^F3—F4(k) in the frequency domain, with frequency resolution VR_f equal to:

VR _f =f _s /N

and a bandwidth B2 equal to:

B2=f_s/(2·N_D).

In next sub-step 46, it is calculated the energy contribution V_{XTND—F5—F6}^F3—F4 in a third frequency range included between two respective frequency limit values F₅ and F₆ (to which two indexes k₅=F₅/VR_f and k₆=F₆/VR_f correspond), the preferably excludes only the end at lowest frequency of the spectrum V^F3—F4(k). In other words, it is calculated the integral (i.e., the sum of the digitised values) of the spectrum V^F3—F4(k) between F₅ and F₆:

$V_{\mathrm{XTIND\_F5}\mathrm{\_F6}}^{\mathrm{F3\_F4}}=\sum _{k=F5/\mathrm{VRf}}^{F6/\mathrm{VRf}}V^{\mathrm{F3\_F4}}\left(k\right)$

In next sub-step 47, it is calculated the energy contribution V_{SHRT—F7—F8}^F3—F4 in a fourth frequency range included between two respective frequency limit values F₇ and F₈ (to which two indexes k₇=F₇/VR_f and k₈=F₈/VR_f correspond), preferably included within the part at frequency around 1 Hz of the spectrum V^F3—F4(k), more preferably included within the third frequency range. In other words, it is calculated the integral (i.e., the sum of the digitised values) of the spectrum V^F3—F4(k) between F₇ and F₈:

$V_{\mathrm{SHRT\_F7}\mathrm{\_F8}}^{\mathrm{F3\_F4}}=\sum _{k=F7/\mathrm{VRf}}^{F8/\mathrm{VRf}}V^{\mathrm{F3\_F4}}\left(k\right)$

Afterwards, step 4 evaluates the presence and, possibly, the level of the so-called siren cry on the basis of a comparison of the energy contribution V_{SHRT—F7—F8}^F3—F4 in the fourth frequency range with the energy contribution V_{XTND—F5—F6}^F3—F4 in the third frequency range of the spectral dynamics V^F3—F4(k), consequently assigning the third score in relation to such possible characteristic of the siren cry. In particular, the third score score(sirencry) is advantageously assigned by means of a third, either continuous or discrete, preferably monotonic not decreasing, function g₃(V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4) of the difference between the two mentioned energy contributions (V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4).

Preferably, the third function g₃(V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4) is discrete, with two intervals of membership for the difference (V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4), to which a respective score value score(sirencry) corresponds.

In fact, as shown in FIG. 6, step 4 of FIG. 1 comprises a sub-step 48 in which it is verified if the energy contribution V_{SHRT—F7—F8}^F3—F4 within the fourth frequency range is larger than 60% of the energy contribution V_{XTND—F5—F6}^F3—F4 within the third frequency range. In the positive, the siren cry characteristic is considered as present, and sub-step 49 is performed, in which a value equal to 2 is assigned to the third score:

score(siren cry)=2

Instead, in the case when the verification of sub-step 48 gives a negative outcome, the siren cry characteristic is considered as absent, and sub-step 50 is performed, in which a null value is assigned to the third score:

score(siren cry)=0

Such score is preferably also assigned in the case when there is no persistent cry, i.e. in the case when the normalised rms value of the acoustic signal is low. As shown in FIG. 6, such condition is achieved through a preliminary sub-step 40 of step 4 verifying that the first score score(p_rms^norm) depending on the normalised rms value is larger than a respective threshold T2, more preferably equal to 1.85.

In the case when the verification of sub-step 40 has a positive outcome, i.e. a persistent cry has been recognised, then step 4 of FIG. 1 continues with the successive sub-steps 41-48 of FIG. 6, illustrated above.

Otherwise, i.e. in the case when the verification of sub-step 40 has a negative outcome, step 4 of FIG. 1 directly continues with sub-step 50 of assigning a null value to the third score score(siren cry).

Alternatively, the third function g₃(V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4) is discrete, with more than two intervals of membership for the difference (V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4), to which a respective score value score(sirencry).

Still alternatively, the third function g₃(V_{XTND—F5—F6}^F3—F4−V_{SHRT—F7—F8}^F3—F4) may be continuous.

In the following a prototype made by the inventors is illustrated, that operates according to a preferred embodiment of the method according to the invention for discriminating different pain levels. In particular, the prototype has been tested by analysing the cry, during heel prick, of 57 newborns, the pain intensity of which has been independently evaluated according the DAN index.

The acoustic signal coming from a ½ inch (i.e. 1.27 cm) microphone, with a 50 mV/Pa sensitivity, has been sample at a frequency of 44.1 kHz, corresponding to a Nyquist frequency of 22.05 kHz. This frequency corresponds to the standard sampling rate of commercial audio devices. A digitised electronic files of about 23.77 s of duration (thus comprising N=2²⁰ samples) has been extracted by each recording, starting from a given time t₀ established by the operator.

The digitised waveform has been divided into M=256 (equal to 2⁸) time intervals, each one of about 92.88 ms of duration. The signal power spectrum has been calculated for each interval for providing a time sequence of 256 spectra for each newborn, with a frequency resolution of about 10.77 Hz. As said, in order to avoid the introduction of spurious spectral characteristics caused by cutting the waveform off, a Hanning window has been applied to each interval. Time evolution of these spectra has been displayed as time-frequency intensity graphs, which may be used for a preliminary heuristic analysis. The acoustic pressure signal p(t) of each cry sequence has been normalised to its maximum amplitude p_max. The rms value of the normalised acoustic pressure has been calculated for each waveform. A first score has been assigned to the normalised rms value by means of the continuous function [1] that is optimised as in [2].

It has been then calculated the mean of the 256 spectra, in order to determine the pitch F₀ as the minimum frequency at which a peak of the mean power spectrum occurs. In particular, a peak has been considered as such when the signal exceeds by at least 5 dB the mean level of the spectrum within the frequency range 3-7.5 kHz.

A third score has been assigned to the pitch value F₀ by means of the continuous function [3] that is optimised as in [4].

It has been then performed the automatic procedure for recognising the “siren cry”, which is only applied in case of persistent cry, i.e. with pain score due to a normalised rms value larger than a threshold (equal to 1.85). In particular:

• • it has been calculated a spectrogram (i.e. the graph of the sound spectral composition as time varies) with time resolution of about 0.093 s;
  • the spectrogram has been frequency integrated from 2 to 8 kHz, obtaining an integrated signal that is a time function with a time resolution equal to about 0.093 s;
  • the mean value of the signal has been subtracted from the same;
  • a flat-top window has been applied to the thus obtained zero mean signal;
  • it has been calculated the power spectrum thereof;
  • it has been calculated the energy within the frequency range of 0.6-1.7 Hz;
  • the presence of the “siren cry” has been assigned to the cry signal if the energy within the frequency range of 0.6-1.7 Hz is larger than 60% of the total energy within the range of 0.4-5.3 Hz.

The pain score as illustrated in FIG. 6 has been assigned to the presence of the “siren cry”, i.e.:

in the case when the siren cry is present,

score(siren cry)=2;

in the case when the siren cry is absent,

score(siren cry)=0.

The total score PainScore, equal to the sum of the three (possibly weighed) scores which are calculated with respect to the three characteristics of the cry acoustic signal:

PainScore=score(p_rms^norm)+score(F₀)+score(siren cry)

has given a reliable indication of the level of pain suffered by the newborn by means of the following correspondence table, validated in literature:

PainScore Pain
0         Absent
1-3       Intermediate
4-6       High

The prototype implementation of the analysis procedure has been made by using the software LabVIEW from the National Instruments.

The instrument has been successfully tested on the recordings of 57 crying newborns, whose pain level has been independently evaluated by using the DAN index, providing values in accordance with the ones of the prototype.

The preferred embodiments have been above described and some modifications of this invention have been suggested, but it should be understood that those skilled in the art can make other variations and changes, without so departing from the related scope of protection, as defined by the following claims.

Claims

1. An automatic method for measuring a baby's cry, comprising the following step: A. having N samples p(i), for i=0, 1, . . . , (N−1), of an acoustic signal p(t) representing the cry, sampled at a sampling frequency f, for a period of duration P;the method being characterised in that it assigns a score PainScore to the acoustic signal p(t) by means of a function AF of one or more acoustic parameters selected from the group comprising:a root-mean-square or rms value prms of the acoustic signal p(t) in the period P;a fundamental or pitch frequency F0 of the acoustic signal p(t), i.e. the minimum frequency at which a peak in the spectrum of the acoustic signal p(t) occurs in the period P; anda configuration of amplitude and frequency modulation of the acoustic signal p(t) in the period P.

2. A method according to claim 1, wherein the duration P is not shorter than 20 seconds.

3. A method according to claim 1, wherein the number N of samples p(i) is equal to an involution of 2 (N=2A).

4. A method according to claim 1, wherein the function AF depends on the rms value prms of the acoustic signal p(t) in the period P that is normalised to its maximum amplitude pmax.

5. A method according to claim 1, wherein the function AF is a linear combination of one or more terms, each one of which is a function of assigning a score to a respective parameter of said one or more acoustic parameters.

6. A method according to claim 5, wherein the function AF is a sum of said one or more terms.

7. A method according to claim 5, wherein said function of score assignment is an either continuous or discrete function.

8. A method according to claim 5, wherein said function of score assignment is a preferably monotonic not decreasing function of the respective acoustic parameter.

9. A method according to claim 4, wherein it comprises the following steps: B.1 determining the maximum amplitude pmax of the acoustic signal p(t) in the period P:

10. A method according to claim 9, wherein the first function g1(prmsnorm) is equal to ([1]):

11. A method according to claim 10, wherein coefficients α and β are equal to ([2]): α=100β=0.14

12. A method according to claim 9, wherein the first function g1(prmsnorm) is discrete, so that the possible values of prmsnorm are subdivided into at least two ranges to which a respective value of score(prmsnorm) corresponds.

13. A method according to claim 12, wherein the first function g1(prmsnorm) is equal to:

14. A method according to claim 4, wherein it comprises the following steps: C.1 subdividing the N samples p(i) into M time intervals, of duration equal to D=P/M, each one of which comprising ND samples pHk(j), with ND=N/M C.2 calculating for each interval the digitised power spectrum of the signal: SHk(j)=FTND{pHk(j)}for j=0, 1, . . . , (ND−1) and k=0, 1, . . . , (M−1)where y(j)=FTQ{x(j)} indicates the operator FTQ transforming Q samples x(j) in the time domain to Q samples y(j) in the frequency domain;C.3 calculating the mean spectrum SHk(j) of the M spectra:

15. A method according to claim 14, wherein the first threshold T1 is equal to the sum of the mean value Smean of the mean spectrum SHk(j) with an offset value Δ1.

16. A method according to claim 14, wherein the second function g2(F0) is equal to ([3]):

17. A method according to claim 16, wherein coefficients γ and δ are equal to ([4]): γ=100δ=0.4

18. A method according to claim 14, wherein the second function g2(F0) is equal to ([3]):

19. A method according to claim 18, wherein FREF=400 Hz.

20. A method according to claim 4, wherein it comprises the following steps: C.1 subdividing the N samples p(i) into M time intervals, of duration equal to D=P/M, each one of which comprising ND samples pHk(j), with ND=N/M C.2 calculating for each interval the digitised power spectrum of the signal: SHk(j)=FTND{pHk(j)}for j=0, 1, . . . , (ND−1) and k=0, 1, . . . , (M−1)where y(j)=FTQ{x(j)} indicates the operator FTQ transforming Q samples x(j) in the time domain to Q samples y(j) in the frequency domain;D.1 for each digitised power spectrum SHk(j), calculating the energy contribution EF3—F4(k) in a second frequency range included between two respective frequency limit values F3 and F4:

21. A method according to claim 20, wherein the third function g3(VXTND—F5—F6F3—F4−VSHRT—F7—F8F3—F4) is discrete, with two intervals of membership for the difference (VXTND—F5—F6F3—F4−VSHRT—F7—F8F3—F4), to which a respective value of score score(sirencry) corresponds, the method further comprising the following steps: D.8 verifying if the energy contribution VSHRT—F7—F8F3—F4 in the fourth frequency range is larger than a percentage threshold PT of the energy contribution VXTND—F5—F6F3—F4 in the third frequency range;D.9 in the case when the verification of step D.8 gives a positive outcome, assigning a value equal to 2 to the third score: score(siren cry)=2D.10 in the case when the verification of step D.8 gives a negative outcome, assigning a null value to the third score: score(siren cry)=0.

22. A method according to claim 21, wherein the percentage threshold PT is equal to 60%.

23. A method according to claim 20, wherein the following step is performed between steps D.3 and D.4: D.11 applying a window Wflat-top(k) (for k=0, 1, . . . , (M−1)) to the deviation ΔEF3—F4(k).

24. A method according to claim 23, wherein the window Wflat-top(k) is a window having spectrum with flat top main lobe, or window flat-top.

25. A method according to claim 20, wherein the third score score(sirencry) is null in the case when the rms value prms of the acoustic signal p(t) in the period P is lower than a second threshold T2.

26. A method according to claim 14, wherein the number M of time intervals is equal to an involution of 2: M=2B, with B≦A.

27. A method according to claim 14, wherein step C.2 calculates for each interval the digitised power spectrum of the signal through a numerical Fourier transform.

28. A method according to claim 14, wherein the following step is performed between steps C.1 and C.2: C.7 applying a window WH(j) capable to eliminate spurious spectral characteristics caused by cutting the waveform off to each of the M time intervals, whereby: pHk(j)=p(ND·k+j)·WH(j)for j=0, 1, . . . , (ND−1) and k=0, 1, . . . , (M−1)

29. A method according to claim 28, wherein said window is a Hanning window.

30. An apparatus for measuring a baby's cry, comprising processing means, wherein it is capable to perform the automatic method for measuring a baby's cry according to claim 1.

31. An apparatus according to claim 30, wherein it further comprises means for detecting acoustic signals, and sampling means, capable to sample said acoustic signals.