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Frequency characteristics of crying in healthy and pathological newborns

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https://www.eduzhai.net American Journal of Biomedical Engineer ing 2013, 3(6): 182-193 DOI: 10.5923/j.ajbe.20130306.07 Frequential Characterization of Healthy and Pathologic Newborns Cries Yasmina Kheddache*, Chakib Tadj Electrical Engineering Department, École de technologie supérieure, M ontreal, Canada Abstract In this paper, we present recent developments in the characterizat ion of healthy and pathologic cries of newborns. We have identified and quantified acoustic characteristics that appear the most relevant in differentiating between pathological and healthy cries; such as fundamental frequency (F0), irregularity of F0 and presence of hyper-phonic modes. The results obtained are very encouraging, since the characteristics measured actually differentiate pathological cries fro m the cries of healthy babies. Keywords Newborns’ Cry, Cry Characterization, Fundamental Frequency 1. Introduction Cry ing is the first sign of life at birth. It is an innate behavior that plays a fundamental role in the survival, health and development of the child. It is also the least explicit expression of distress or pain. Tests for evaluating the health of the newborn, such as Apgar score (Appearance, Pulse, Grimace, Activ ity, and Respiration) are now routine during birth. Ho wever, these tests do not incorporate quantitative measurement of acoustic characteristics of cries, providing only a qualitative su mmary based on observation. Our goal is therefore to utilise cries in order to improve mon itoring of the first days of infant life[1, 2, 3]. Cry analysis allows identification of enfant with med ical syndromes when no symptoms are present[2, 4]. Early diagnosis before the onset of clinical sympto ms will increase the likelihood of successful intervention before the illness has serious impact on the health of the infant. In the case of pathologies that are not detectable without in-depth examination and specialized tests, if medical treat ment is begun soon enough, enfant can heal comp letely. Early diagnosis of various pathologies that can afflict newborn using the spontaneous cries during the first four weeks of life is crucial because from 4 weeks, infants acquired a voluntary control of these vocal tracts[2]. The cry itself contains a wea lth of information. Its acoustic properties reflect the conditions that caused it to be produced[4]. In order to evaluate infant cries, acoustic parameters must be measured quantitatively and deviations fro m “normality” must be interpreted. * Corresponding author: yasmina.kheddache.1@ens.etsmtl.ca (Yasmina Kheddache) Published online at https://www.eduzhai.net Copyright © 2013 Scientific & Academic Publishing. All Rights Reserved The classification of the cries is a basic techniques used to design a newborn’s PCIS (pathological cry identification system). It fa lls within the automation of a natural perception of cries and its modelling using advanced signal processing techniques. It contains two main parts namely a cry signal characterizat ion and modelling. There have been numerous fundamental studies related to this research area. The thrust of one of these lines of study has been to analyze infant cries in order to catalogue acoustic man ifestations of pathological symptoms[1, 4, 5, 6, 7]. Our study is based on several hypotheses that these researchers have formu lated. These hypotheses assert essentially that infant cries stem fro m physical and psychological status as well as both internal and external stimuli[4]. Both temporal and acoustic characteristics contain biological alarms. By observing spectrograms and spectra of audio signals of cries, associations have been made with pathological conditions. This visual approach is applied after the fact. To our best knowledge, there is no software tool that alerts health-care workers to the development of pathologies in newborns by direct monitoring of cries. Therefore, we propose to conduct a basic e xperimental research through the following: ● Rev iew the most pertinent characteristics of infant cries, based on scientific consensus. ● Identify the most promising characteristics and formally define them. ● Associate these characteristics with pathologies of in teres t. ● Automatic measurement of these characteristics. ● Test the influence of use the studied characteristics in addition to other features in performance of PCIS. One of the aims of this research is therefore to define as precisely as possible these characteristics, to create a tool for estimating them without human intervention and then quantify them for each pathological condition examined. The American Journal of Biomedical Engineer ing 2013, 3(6): 182-193 183 principal advantage of this approach is that once the tool is developed, it will evolve through updates as other pathologies are characterized. Our main contribution is therefore the quantification of acoustic characteristics associated with pathologies and ultimately provides a basis for alerting health-care wo rkers to intervene. Of course the developed tool and system is not being proposed to replace health-care personnel. However, it could be highly appreciated for alerting personnel, especially in the hectic hospital setting. Once developed, the operation of the system is simple and does not require many resources. It can be set up quickly and at minima l cost. This paper is divided into seven sections. In section 2, we present a succinct review of the scientific literature providing acoustic definitions of infant cries and their characteristics and modes. We describe in section 3 known associations between infant medical conditions and cry characteristics, with focus on healthy infants, premature b irths and certain health problems. Section 4 presents the database used in this study and discusses our adopted methodology for qualitative and quantitative characterizat ion of cries with pathological implications and cries without such implications among the healthy infants studied. Section 5 is devoted to the analysis of the results obtained for an initial quantitative characterizat ion of healthy and pathological cries. The next section presents experimental va lidation of use the proposed characteristics concatenated with MFCC coefficients (Mel Frequency Cepstral Coefficients) in PNN classifier (Probabilistic Neural Network). We conclude this paper with a conclusion. Infant crying consists primarily o f rhyth mic alternation of phonation and breaths. The cry co incides with exhaling, the phonation being produced by the laryn x[4]. Each infant cry is acoustically unique. In most children, the fundamental frequency F0 varies fro m 250 Hz to 450 Hz, with a first formant at a frequency F1 of 1100 Hz and a second formant at a frequency F2 of 3100 Hz[4]. Lester et al. define three identifiable modes of cries due to vocal cord vibrations: a). Basic cry or phonation with F0 (350–750) Hz, b ) High pitched cry F0 (750–1000Hz) or Hyper-phonation F0 (1000–2000Hz) and c) Noisy, turbulent or dysphonic cries. A certain terminology has been developed in various studies to describe the acoustic characteristics of cries[2, 4, 5, 8]. The generally accepted terms are shown in Table1. 3. The Characteristics of Cries under Certain Medical Conditions 3.1. The Cries of Healthy Newborns During the first months of life of an infant, cries are short with simple melodic forms and become longer and more complex with age[9]. At the age of one year, cries of infants born healthy are characterized by F0 varying fro m 400 to 600 H z with the average value of 450 Hz, often with a decreasing or increasing-decreasing melody shape[8], with superimposed symmetric harmonics and an average duration of 1–1.5s[6]. The variat ion of F0 is regular. A shift is frequent at the beginning of the signal in the case of cries produced by pain and spontaneous cries, but rare during the first days of the newborn[10]. 2. Acoustic Definition of Cries Characte ristic Fundamental Frequency F0 Hyp er-pho nat ion Ph on at ion F0 Irregularity Dy spho nat ion Utterances Number of changes in cry mode Shift Glide First and second formant F1, F2 Duration of Inhalation Amp lit ude Bi-ph on at ion F0, F1, F2, amplitude variability Table 1. Cry characteristics De finition The average vibratory frequency (in Hz) of the vocal folds The average percentage of 25ms blocks having an F0 >1000 Hz. The average percentage of 25ms blocks having an F0 in the 350 –750 Hz range Sudden change in F0 > 100 Hz within 20ms The average percentage of 25ms blocks containing noise or aperiodic sound The number of vocal sounds produced by exhaling during the cry The number of blocks that change bet ween phonat ion and dysphonat ion Sudden change in F0 >100 Hz Very fast increase or decrease in F0 of 600Hz or more during a time of 0.1s The average resonance frequencies produced by filtering the upper vocal passage The interval in seconds between the first and second vocalization The average energy in dB during a vocalization Characterized by the presence of two F0 Inter-quartile spread of each parameter 184 Yasmina Kheddache et al.: Frequential Characterization of Healthy and Pathologic Newborns Cries 3.2. The Cries of Premature Newborns The differences in the characteristics of these cries are proportional to the number of weeks by wh ich the birth was premature, the mo re premature, the higher fundamental frequency[1]. So me cries are short and piercing, while others may be of durat ion and F0 similar to the cries of full-term newborns. This can be due to some disorders not detected at birth[1, 8]. 3.3. Cries of Newborns with Various Pathologies The cries of a newborn with a pathological condition are persistent with little punctuation, reflecting high irritability and poor physiological stability. When a central nervous system (CNS) disorder is involved, the cry exhib its auditory abnormalities. However, its F0 is high and its melodic contour is irregular[2]. In spite of differences in measurement procedures, all cry studies have shown that high F0 is an indicator of a neurological problem[4]. Other markers also associated with neonatal risk include hyper-phonic cries, very high-pitched cries, noisy or dysphonic cries as well as changing mode between phonation, dysphonic cries and variability of F0 and F1[11]. Spectrographic analysis studies carried out on the cries of infants with disorders such as meningitis, hydrocephaly, congenital abnormalities, chro mosomal aberrations or metabolic dysfunction indicate abnormal cry features[8]. In the syndrome known as cat cry, the cry is intense and monotone with a flat melodic contour, while in infants with a hypothyroid condition, the cries are of weak intensity. In newborns with herpetic encephalitis, the cry is significantly dysphonic and marked by increased noise concentration[4]. 4. Methodology for Qualitative and Quantitative Characterization Our approach for characterizat ion of healthy and pathologic cries is represented on the simp lified blocks diagram illustrated in Figure 1. It consists of four steps: 1) choosing of the pathologies to be studied and recording of cries of newborns in order to build an experimental database, 2) qualitative characterization of the cries, based on identification of important characteristics in studied cries, 3) quantitative characterization based on estimation of selected characteristics and 4) establishing quantitative relationships between these characteristics and the pathologies studied. 4.1. Cry Database The database used contains 3146 cry samples of 1s duration fro m 66 newborn babies aged 1 day to 4 weeks. 1774 cry samples fro m 31 healthy newborn (among them 764 are premature) and 1372 fro m 35 newborn who present some diseases (among them 829 are premature). The Table 2 shows the pathologies studied by categories of diseases. These cries were collected with the aid of medical collaborators of neonatology department at Saint-Justine Hospital in Montreal. Each infant is recorded two or three times, with at least one hour between each recording over a period of not more than ten days. The date and time of recording, gender identificat ion, birth date, diagnosis, and ethnicity are noted for each crying episode. Cry signal recording Qualitative characterization of cries Quantitative characterization of cries Identification of relations between characteristics and studied pathologies Figure 1. Newborn Cries Characterization Table 2. The pathologies studied Category Heart Defect Respiratory Infect io us Neurological Co n gen it al Malfo rmat ion Pret erm newborn Full-term newborn Path olog y Tetralogy of Fallot Thrombosis in the vena cava RDS (Respiratory Distress Syndrome) Men in git is P erito n it is Asphyxia IUGR - asphyxia Intra-uterine Growth Ret ardat io n) IUGR- microcephaly Gast ro sch isis Lingual frenum Healthy Sample size 53 77 270 115 20 190 148 78 280 141 1010 Healthy 764 The recording of cries is done using a small recorder, placed at a distance of 10 cm of babies’ mouth with a sampling rate of 44.1 kHz. The conditions in which the cries are recorded are : hunger, sampling blood and change of diapers. In order to confirm the health status of the infant or to determine that an undiagnosed health problem was in fact p resent during recording; we consult the follow-up examination conducted six months after recording of the cries. In view of the spectacular increase in the numbers of pre-mature births recorded throughout much of the world, we decided to compare this category of cries to the cries of full-term newborns. Pre-mature newborns face major risks associated with organ functional immaturity. They are also more vu lnerable to infection and face an increased risk of cerebral lesion when a significant jaundice is present. Early diagnosis of the various pathologies that can afflict this category of infant is crucial. American Journal of Biomedical Engineer ing 2013, 3(6): 182-193 185 4.2. Qualitati ve Characterization of Cries The principal aim of this part of the work was to identify the acoustic characteristics that best serve to characterize the cries studied and to establish qualitative and relat ionships between these characteristics and the various pathologies studied. This work is co mpleted using PRAAT, a freeware program for the analysis and the reconstruction of acoustic speech signals[12]. The spectrographic analysis based on observation of pathological cry signals as well as cries of healthy infants to identify acoustic characteristics of these cries, allows us to establish the initial qualitative relationships between acoustic characteristics of cries and pathologies shown in Table 3. This analysis leads us to the conclusion that cries of infants with pathological conditions are indeed quite different fro m those of healthy infants. Examples of spectrogram and estimated F0 for full-term, premature, healthy and sick newborns are shown in Figure 2. We note raising-falling pitch contour for healthy newborn’s cries (preterm and full-term) and lower spectral intensity, height and irregular pitch contour in pathologic cries. 4.3. Quantitati ve Characterization of Cries The quantitative characterization of cries contains an automated measurement of acoustic characteristics of cries that make major contributions to the differentiation between pathological cries and healthy cries. In order to establish quantitative relationships between cry characteristics and the pathologies, we began by estimating or measuring certain acoustic characteristics cited in the literature in association with severe medica l conditions, such as F0, the presence of hyper-phonation and irregularity of F0. The principal means used by researchers to analyze infant cries involve software in itially dedicated to adult voice analysis. Since the adult and infant vocal passages differ in shape, these tools should be used with precaution[4]. We developed our own measuring tool using Matlab. This tool estimates F0 and percentage of hyper-phonic segment as well as the F0 irregularity, as de fined in Tab le 4. The following approach was taken when estimat ing these ch aracteris tics : ● Noise filtering and segmenting recordings into useful and non-useful portions. ● Estimation of F0 in short segments typically of 20ms. ● Identificat ion of hyper-phonic segments, as well as irregularity of F0 as defined in Table 4. ● Calculation of average percentage of hyper-phonic segments ( APhyp ) and irregularity of F0 ( APirrg ). This has been performed : 1) for healthy and pathologic cries, 2) by category of pathologies and finally 3) by pathology and gestational age. We used the following fo r mu las : APhyp = Nhyp N total , APirrg = Nirrg N total Where Ntotal , Nhyp and Nirrg are the total number of segments, the total number of hyper-phonic segments and the total number of F0 irregula rity respectively. ● Utilizat ion of standard deviation for estimated characteristics by category of pathologies and application of ANOVA variance analysis for healthy and pathologic cries to carry out the analysis of the estimated characteristics by gestational age (Full-term, Preterm). Table 3. Characterist ics of cries examined by spect rographic analysis Category Path olog y Cry characteristics Heart Defect Respiratory Tetralogy of Fallot Thrombosis in the vena cava RDS Shift ↑ Hyper-phonation, ↑ F0 irregularity, ↑dysphonation, ↑shift ↑ Hyper-phonation,↑ F0 irregularity, ↑dysphonation Infect io us Men in git is P erito n it is IUGR - asphyxia ↑ F0, Hyper-phonation , F0 irregularity ↑ Hyper-phonation, ↑ F0 irregularity, ↑ dysphonation, ↑Hyper-phonation, ↑F0 irregularity, ↑dysphonation Neurological Congenital malformation Healthy IUGR- microcephaly Asphyxia Gast ro sch isis Lingual frenum - Hyper-phonation, F0 irregularity ↑ Hyper-phonation, ↑ F0 irregularity ,↑ dysphonation ↑F0,↑ Hyper-phonation , F0 irregularity, dysphonation ↑ Hyper-phonation, ↑ F0 irregularity, ↑dysphonation ↓Hyper-phonation,↓ F0 irregularity, ↓dysphonation 186 Yasmina Kheddache et al.: Frequential Characterization of Healthy and Pathologic Newborns Cries (a) Full-term newborn suffering from asphyxia (b) Premature newborn suffering from IUGR- microcephaly (c) Healthy full-term newborn American Journal of Biomedical Engineer ing 2013, 3(6): 182-193 187 (d) Healthy premature newborn Figure 2. Waveform and spectrogram of healthy and pathologic cries 4.4. Fundamental Frequency Esti mati on Since F0 is one of the most widely used characteristics for distinguishing cries and measurement of most other characteristics stems fro m it, precise measurement of F0 and its variations over t ime is v iewed as an essential co mponent of reliable information on the health status of newborns and its variations over t ime is v iewed as an essential co mponent of reliab le in formation on the health status of newborns. In this paper, the modified SIFT algorithm (Simple Inverse Filtering Tracking) is used for estimat ing the fundamental frequency. It was demonstrated that this algorith m includes the autocorrelation properties and the ceptral pitch analysis technique[13]. In addit ion, the performance of this algorithm has been tested on a real newborn’s cry database[6, 14]. The main steps of this algorithm are: ● Div ision of the signal into overlapping frames of 20ms and N samp les with 10ms recovering and mult iplying each frame by Hamming window. ● Performing glottal inverse filtering to attenuate the influence of vocal tract. ● Estimation of the autocorrelation sequence. ● Performing a peak p icking and decision algorith m in which the peak value is co mpared to voiced threshold. ● The fundamental frequency is estimated using: T0= 1/F0 = arg maxη{ri (η)}, where ri (η) is the autocorrelation sequence with i=[1,..,N]. ● Smoothing of the result fundamental frequency contour using median filter. Details about the SIFT algorithm can be found in[13, 14]. Table 4. Measured characteristics of cries Ch aract erist ic Fun dament al frequency F0 Hyp er-pho nat ion F0 irregularity Defin it ion Vibratory frequency (in Hz) of the vocal folds. Blocks of 20ms having an F0 >1000 Hz. Sudden change in F0 >100 Hz within 20ms. 5. Results Analysis Examples of estimated F0 for full-term, premature, healthy and sick newborns are shown in Figure 3. Figure 3(a) shows the estimat ion of F0 for the cry of a healthy, full-term newborn. The frequency range is 400–500 Hz and no hyper-phonic segments, shifts or gliding are noted. Figure 3(b) shows the estimated F0 for the cry of a full-term newborn suffering fro m undiagnosed vena cava thrombosis during the days following birth. The measured values characterize a pathological cry. The pathological condition was confirmed six months after birth. Variat ion of F0 in this case is irregular. We note the presence of long hyper-phonic segments with gliding and shifts of F0 with in the 700–2200 Hz range. Figure 3(c) shows the estimated F0 for the cry of a premature but otherwise apparently healthy newborn. We did not note the presence of hyper-phonic segments. F0 varies somewhat more and with less regularity than in the case of the full-term healthy infant. Figure 3(d) shows the estimated F0 for the cry of a premature newborn suffering fro m Gastroschisis. As in case (b), the measured values correspond to a cry with pathological imp lications. The presence of several hyper-phonic segments is noted. The F0 variat ions are less regular than are those of the healthy premature infant cries. The estimated percentages of hyper-phonic segments as well as the irregularity of F0 are shown in Figure 4. The recordings used for this estimat ion are of premature infants (p) and full-term infants (t), including healthy infants and infants suffering fro m pathologies listed in Tab le 2. Figure 4(a) indicates that the estimated characteristics allo w well an initial distinction between a healthy newborn and a sick newborn, because the average percentage of the hyper-phonation and the irregularity of F0 are clearly h igher for sick babies compared to healthy ones. These results show that the cries of healthy newborns (full-term or preterm) contain around 6.5% of hyper-phonic 188 Yasmina Kheddache et al.: Frequential Characterization of Healthy and Pathologic Newborns Cries segments and around 6.5% of irregularity of F0. On the other hand, the cries of sick newborns contain around 11% of hyper-phonic segments and around 9% of irregularity of F0. These preliminary results led us to estimate the characteristics by category of pathologies. The estimated characteristics according to the category of pathologies are presented in Figure 4(b). We notice that the average percentage of the hyper-phonation and the irregularity of F0 are higher for all categories compared to that of healthy babies. Similarly, the highest average percentage of the hyper-phonation and the irregularity of F0 are found in the category of the newborn with neurological p roblems. As shown in Figure.5, the calculation of the standard deviation for estimated characteristics by category of pathologies shows a large dispersion of measured characteristics for Heart De fect and Neurologica l categories in both case of average percentage of hyper-phonic segments and irregularity of F0. The estimation of studied characteristics by pathologies and gestational age indicates a better variat ion of the newborns cries characteristics. This estimation is presented in Figure 4(c). It shows clearly that the average percentages of hyper-phonic segments in the cry samples are similar for both healthy premature infants and healthy full-term newborns. It also shows that the average percentages of irregularity of F0 are slightly more important for premature infants than healthy full-term newborns. This result is consistent with spectrographic studies of crying newborns[2, 8]. The results shown in Figure 4(c) also indicate that the average percentages of hyper-phonic segments and irregularity in the cry samp les fro m infants with a pathological condition are clearly higher co mpared to healthy newborns. These percentages vary fro m one pathological condition to another and are dependant to level of prematurity. We thus are inferring that the characteristics of the cries do not vary according to the categories of diseases but the pathology itself. For examp le, in the case of the category Heart Defect, where the percentages of the characteristics estimated for pathology Thrombosis in the Vena Cava is much h igher co mpared to the percentages of the characteristics for pathology Tetralogy of Fallot. The calculation of the standard deviation for estimated characteristics by pathologies is shown in Figure 6. The results indicate a large dispersion for measured hyper-phonic segments in the case of Lingual frenum and Gastroschisis. They also indicate a best estimat ion of average percentage of hyper-phonic segment in the case of healthy preterm newborn, thro mbosis in the vena cava, asphyxia, RSD and Tetralogy of Fallot diseases. The large dispersion for measured irregularity of F0 is found in asphyxia, IUGR – microcephaly, Gastroschisis and, IUGR – asphyxia diseases. The best estimation for this characteristic is found in full term and preterm newborn, Lingual frenum, RSD, Tetralogy of Fallot diseases. With this study, we should also be able to demonstrate that some pathological conditions do not manifest themselves in cries and are therefore not detectable using the characteristics studied such as Tetralogy of Fallot. More characteristics may be necessary to better distinguish and/or detect these pathologies. (a) Healthy full-term newborn American Journal of Biomedical Engineer ing 2013, 3(6): 182-193 189 (b) Full-term newborn suffering from thrombosis in the vena cava (c) Healthy premature newborn 190 Yasmina Kheddache et al.: Frequential Characterization of Healthy and Pathologic Newborns Cries (d) Premature newborn suffering from Gastroschisis Figure 3. Estimation of F0 using the modified SIFT algorithm % Hyperphonation 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% (a) Healthy %Irregularity Pathologic 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% (b) % Hyperphonation 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% % Hyperphonation % Irregularity %Irregularity Fi gure 4. The average percent age of hyper-phonic segment s and irregularit y of F0 by (a): Pat hologic and healthy cries, (b): Cat egory of pat hologies, (c): P athologies and gestat ional age American Journal of Biomedical Engineer ing 2013, 3(6): 182-193 191 25,00% 20,00% 15,00% 10,00% 5,00% 0,00% Mean Max Min 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Mean Max Min (a) (b) Figure 5. The mean percentage and standard deviation of (a): hyper-phonic segments and (b): irregularity of F0 by category of pathologies Mean Max Min 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% 18.00% 16.00% 14.00% 12.00% 10.00% 8.00% 6.00% 4.00% 2.00% 0.00% Mean Max Min (a) (b) Figure 6. The mean percentage and standard deviation of (a): hyper-phonic segments and (b): irregularity of F0 by pathologies We performed ANOVA variance analysis for healthy and pathologic cries to compare behavior of the estimated characteristics(percentage of hyper-phonic segments and irregularity of F0) with respect to gestational age (preterm, full term). We notice fro m these results in Table 5, significant statistical d ifferences (F=7.27, p<0.05) between average percentage of hyper-phonic segment of healthy premature cries and full-term healthy cries. For all other cases no significant difference were found by gestational age. Table 5. Analysis of variance (ANOVA) according to gestational age Ch aract erist ics % Hper-phonation % Irregularity Healthy F p 7.27 0.0071 1.425 0.2327 P atho lo gic F p 2.116 0.176 1.233 0.29 Hence, the percentage of hyper-phonic segments is dependent on gestational age for healthy cries. For pathologic cries, this characteristic does not depend on gestational age but it’s depending on pathology itself. The percentage of irregularity of F0 does not dependent on gestational age in both cases of healthy and pathologic cries. 6. Experimental Validation The adopted methodology to test the influence of use the studied characteristics in addition to other features in performance of PCIS is represented on the simplified diagram blocks illustrated in Figure 7. Matlab is used for the development of all b locks of the system. In this context, we used studied characteristics (APhyp, APirrg) concatenated with the most used features in the most recent speaker recognition systems to characterize acoustic features of cries signals, namely M FCC (Mel Frequency Cepstral Coefficients) witch based on Fourier analysis and filter bank on a Mel scale. These characteristics once obtained are used as input of PNN classifier (Probabilistic Neural Network). PNN is generally used for classification problems in the medica l do main[15, 16]. The use of the PNN classifier is mot ivated by its speed and simp licity of the training process[17]. It is obtained using function newpnn () in Matlab. 6.1. Features Extraction MFCC coefficients are extracted using 20ms interlaced frame with 10ms recovering, provid ing 99 windows for one second cry samples. We ext racted 12 MFCC parameters for each 20ms of a cry signal. We obtained an M FCC matrix of 12 lines ×99 co lu mns for each sample of 1second. Thereafter we calculate the average percentages of hyper-phonic

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