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Fulltext Automatic infant cry classification using radial basis function network

Wahid, N. S. A., Saad, P., Hariharan, M.

Journal of Advanced Research in Applied Sciences and Engineering Technology, 2016;4(1):12-28.
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– This paper proposes the automatic infant cry classification to analyse infant cry signals.
The cry classification system consists of three stages: (1) feature extraction, (2) feature selection, and
(3) pattern classification. We extract features such as Mel Frequency Cepstral Coefficients (MFCC),
Linear Prediction Cepstral Coefficients (LPCC), and dynamic features to represent the acoustic
characteristics of the cry signals. Due to the high dimensionality of data resulting from the feature
extraction stage, we perform feature selection in order to reduce the data dimensionality by selecting
only the relevant features. In this stage, five different feature selection techniques are experimented. In
pattern classification stage, two Artificial Neural Network (ANN) architectures: Multilayer Perceptron
(MLP) and Radial Basis Function Network (RBFN) are used for classifying the cry signals into binary
classes. Experimental results show that the best classification accuracy of 99.42% is obtained with
RBFN. Copyright © 2016 Penerbit Akademia Baru - All rights reserved.
A new hybrid intelligent system for accurate detection of Parkinson's disease

Hariharan M, Polat K, Sindhu R

Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004

Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
Normal and hypoacoustic infant cry signal classification using time-frequency analysis and general regression neural network

Hariharan M, Sindhu R, Yaacob S

Comput Methods Programs Biomed, 2012 Nov;108(2):559-69.
PMID: 21824676 DOI: 10.1016/j.cmpb.2011.07.010

Crying is the most noticeable behavior of infancy. Infant cry signals can be used to identify physical or psychological status of an infant. Recently, acoustic analysis of infant cry signal has shown promising results and it has been proven to be an excellent tool to investigate the pathological status of an infant. This paper proposes short-time Fourier transform (STFT) based time-frequency analysis of infant cry signals. Few statistical features are derived from the time-frequency plot of infant cry signals and used as features to quantify infant cry signals. General Regression Neural Network (GRNN) is employed as a classifier for discriminating infant cry signals. Two classes of infant cry signals are considered such as normal cry signals and pathological cry signals from deaf infants. To prove the reliability of the proposed features, two neural network models such as Multilayer Perceptron (MLP) and Time-Delay Neural Network (TDNN) trained by scaled conjugate gradient algorithm are also used as classifiers. The experimental results show that the GRNN classifier gives very promising classification accuracy compared to MLP and TDNN and the proposed method can effectively classify normal and pathological infant cries.
Luminance sticker based facial expression recognition using discrete wavelet transform for physically disabled persons

Nagarajan R, Hariharan M, Satiyan M

J Med Syst, 2012 Aug;36(4):2225-34.
PMID: 21465183 DOI: 10.1007/s10916-011-9690-5

Developing tools to assist physically disabled and immobilized people through facial expression is a challenging area of research and has attracted many researchers recently. In this paper, luminance stickers based facial expression recognition is proposed. Recognition of facial expression is carried out by employing Discrete Wavelet Transform (DWT) as a feature extraction method. Different wavelet families with their different orders (db1 to db20, Coif1 to Coif 5 and Sym2 to Sym8) are utilized to investigate their performance in recognizing facial expression and to evaluate their computational time. Standard deviation is computed for the coefficients of first level of wavelet decomposition for every order of wavelet family. This standard deviation is used to form a set of feature vectors for classification. In this study, conventional validation and cross validation are performed to evaluate the efficiency of the suggested feature vectors. Three different classifiers namely Artificial Neural Network (ANN), k-Nearest Neighborhood (kNN) and Linear Discriminant Analysis (LDA) are used to classify a set of eight facial expressions. The experimental results demonstrate that the proposed method gives very promising classification accuracies.
Effect of laparoscopic sterilization and insertion of Multiload Cu 250 and Progestasert IUDs on serum ferritin levels

Goh TH, Hariharan M

Contraception, 1983 Oct;28(4):329-36.
PMID: 6667621

Blood haemoglobin and serum ferritin levels were measured at the initial visit and 12 months following sterilization and IUD insertion. Ferritin levels were unaltered in Progestasert users after 12 months but haemoglobin values increased though not significantly. Ferritin levels fell in Multiload Cu 250 users and in sterilized women; haemoglobin levels were also observed to fall but significantly only in the latter group. Iron-deficiency anaemia was prevalent at initial contact and there appeared to be an increased risk subsequently in Multiload Cu 250 users and in those who were sterilized. Screening and monitoring for anaemia is indicated. From the viewpoint of iron status, the Progestasert is preferable to the Multiload Cu 250 but it suffers the major disadvantages of needing frequent replacement and of causing menstrual disturbances which might compromise its acceptability. Menstrual blood loss studies may help explain why anaemia develops after sterilization.
Fulltext Iron-deficiency anaemia and serum ferritin levels in Malaysian women

Goh TH, Hariharan M

Med J Malaysia, 1986 Dec;41(4):300-4.
PMID: 3670151

Serum ferritin and blood haemoglobin levels were studied in 229 women attending a family planning clinic. Ferritin values ranged from 2 to 438 Jlg/l and was skewed with an arithmetic mean of 41.8 and geometric mean of 23.4 flg/l; 26.6% were iron-deficient (ferritin < 12 Jlg/l). Haemoglobin values were normally distributed with a mean of 11. 7 g/dl but 59% were anaemic (Hb < 12 gjdl]. The correlation between ferritin and haemoglobin values was poor (r = 0.147) but almost all women with a haemoglobin below 10 g/dl were iron-deficient. This study reaffirms the need for monitoring iron-deficiency anaemia in apparently healthy women seeking contraception.
Analysis of infant cry through weighted linear prediction cepstral coefficients and Probabilistic Neural Network

Hariharan M, Chee LS, Yaacob S

J Med Syst, 2012 Jun;36(3):1309-15.
PMID: 20844933 DOI: 10.1007/s10916-010-9591-z

Acoustic analysis of infant cry signals has been proven to be an excellent tool in the area of automatic detection of pathological status of an infant. This paper investigates the application of parameter weighting for linear prediction cepstral coefficients (LPCCs) to provide the robust representation of infant cry signals. Three classes of infant cry signals were considered such as normal cry signals, cry signals from deaf babies and babies with asphyxia. A Probabilistic Neural Network (PNN) is suggested to classify the infant cry signals into normal and pathological cries. PNN is trained with different spread factor or smoothing parameter to obtain better classification accuracy. The experimental results demonstrate that the suggested features and classification algorithms give very promising classification accuracy of above 98% and it expounds that the suggested method can be used to help medical professionals for diagnosing pathological status of an infant from cry signals.
Fulltext Locality sensitivity discriminant analysis-based feature ranking of human emotion actions recognition

Khair NM, Hariharan M, Yaacob S, Basah SN

J Phys Ther Sci, 2015 Aug;27(8):2649-53.
PMID: 26357453 DOI: 10.1589/jpts.27.2649

[Purpose] Computational intelligence similar to pattern recognition is frequently confronted with high-dimensional data. Therefore, the reduction of the dimensionality is critical to make the manifold features amenable. Procedures that are analytically or computationally manageable in smaller amounts of data and low-dimensional space can become important to produce a better classification performance. [Methods] Thus, we proposed two stage reduction techniques. Feature selection-based ranking using information gain (IG) and Chi-square (Chisq) are used to identify the best ranking of the features selected for emotion classification in different actions including knocking, throwing, and lifting. Then, feature reduction-based locality sensitivity discriminant analysis (LSDA) and principal component analysis (PCA) are used to transform the selected feature to low-dimensional space. Two-stage feature selection-reduction methods such as IG-PCA, IG-LSDA, Chisq-PCA, and Chisq-LSDA are proposed. [Results] The result confirms that applying feature ranking combined with a dimensional-reduction method increases the performance of the classifiers. [Conclusion] The dimension reduction was performed using LSDA by denoting the features of the highest importance determined using IG and Chisq to not only improve the effectiveness but also reduce the computational time.
A longitudinal study of serum iron indices and haemoglobin concentration following copper-IUD insertion

Goh TH, Hariharan M, Tan CH

Contraception, 1980 Oct;22(4):389-95.
PMID: 7449387

The increase in menstrual blood loss associated with copper-bearing IUDs may cause or aggravate pre-existing anaemia. In order to evaluate this risk, 84 Malaysian women wearing copper-IUDs were studied longitudinally by means of serial measurements of blood haemoglobin concentration (Hb), serum iron (S/Fe) and transferrin saturation (T/S). The initial Hb was under 12 gm% in 33.7% of patients. The mean Hb showed no significant change up to 12 months while S/Fe fell significantly at the end of this time; the T/S was significantly reduced as early as 6 months post-insertion. There is a significant risk of anaemia following copper-IUD insertion, particularly with long-term usage. Progestogen-releasing IUDs may offer the most feasible solution to this problem in our local context since oral medication with iron or drugs to reduce menstrual blood loss is not practicable.
Optimal selection of mother wavelet for accurate infant cry classification

Saraswathy J, Hariharan M, Nadarajaw T, Khairunizam W, Yaacob S

Australas Phys Eng Sci Med, 2014 Jun;37(2):439-56.
PMID: 24691930 DOI: 10.1007/s13246-014-0264-y

Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.
Classification of speech dysfluencies using LPC based parameterization techniques

Hariharan M, Chee LS, Ai OC, Yaacob S

J Med Syst, 2012 Jun;36(3):1821-30.
PMID: 21249515 DOI: 10.1007/s10916-010-9641-6

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.
Improved binary dragonfly optimization algorithm and wavelet packet based non-linear features for infant cry classification

Hariharan M, Sindhu R, Vijean V, Yazid H, Nadarajaw T, Yaacob S, et al.

Comput Methods Programs Biomed, 2018 Mar;155:39-51.
PMID: 29512503 DOI: 10.1016/j.cmpb.2017.11.021

BACKGROUND AND OBJECTIVE: Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals.
METHODS: Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well.
RESULTS: Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%.
CONCLUSION: The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals.
Effects of cryopreservation on gram-positive bacteria contaminants in umbilical cord blood

Boey KPY, Zhu P, Tan H, Abdullah MAB, Tang KF, Li MM, et al.

Transfus Med, 2022 Feb;32(1):82-87.
PMID: 34862686 DOI: 10.1111/tme.12834

OBJECTIVE: To evaluate the effects of cryopreservation in post-thaw umbilical cord blood units for the survivability of Gram-positive bacteria strains.
BACKGROUND: Microbial screening is required for all cord blood units (CBUs). Four gram-positive contaminants were documented to survive cryopreservation poorly and isolation of other contaminants were reported.
METHODS: Forty-eight contaminated CBUs detected with either Staphylococcus epidermidis, Corynebacterium species, Peptostreptococcus or Streptococcus species before cryopreservation were used in this study. CBUs were processed, DMSO-infused and microbial screened before cryopreservation. Post-thaw microbial screening was achieved using 1 and 10 ml inoculants in BACTEC culture bottles. Positive bottles were subjected for microbial identification and results were compared with those from pre-freeze.
RESULTS: A higher rate of microbial contamination was found using the 10 ml inoculant. Screening of 11 CBUs did not detect any contaminants while 30 CBUs screened detected more than one unknown contaminants and majority of contaminants were identified to be gram-negative species.
CONCLUSION: A higher inoculation volume used at post-thaw for microbial screening improves contamination detection but leads to the loss of precious cord blood. Some contaminants did not survive cryopreservation or were not identified due to their low microbial levels. Contrasting contaminants found at post-thaw suggest the improvements made in detection and identification of contaminants over the years.

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