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  1. 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.
  2. 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.
  3. Rasuli R, Mohamad M, Yaacob SS
    Med J Malaysia, 2023 Dec;78(7):883-889.
    PMID: 38159922
    INTRODUCTION: Despite substantial progress in reducing hepatitis B prevalence in the general population, the indigenous population in Malaysia continues to face a significant burden of infection, with high seroprevalence rates. It is hypothesised that transmission patterns differ between the indigenous and non-indigenous populations. This study aimed to compare key risk factors for hepatitis B transmission in indigenous and non-indigenous cases.

    MATERIALS AND METHODS: This is a comparative crosssectional study using secondary data from the eNotifikasi system and hepatitis B case investigation forms between 2018 and 2022 from four district health offices in Pahang, Malaysia. Demographic data, hepatitis B vaccination status and risk factors were assessed. Data analysis employed were independent chi-squared tests, t-tests and binary logistic regression.

    RESULTS: The study included 285 cases (141 indigenous and 145 non-indigenous). Among the indigenous cases, 72.3% were unvaccinated and 59.6% reported a history of infected mother, followed by percutaneous exposure, multiple sexual partners, and sharing syringe. The odds for those with a history of an infected mother being indigenous group is 2.5 times (95% CI: 1.4-4.4) compared to those with a history of an infected mother being non-indigenous group.

    CONCLUSION: Significant difference exists in hepatitis B risk factors between indigenous and non-indigenous populations. The main risk factor for indigenous community is history of infected mother. Thus, the necessity of incorporating hepatitis B screening into the current practice of antenatal HIV screening, specifically targeting the indigenous community, should be given consideration.

  4. Abdollahi A, Abu Talib M, Yaacob SN, Ismail Z
    J Psychiatr Ment Health Nurs, 2014;21(9):789-96.
    PMID: 24661763 DOI: 10.1111/jpm.12142
    The relevance of the study of happiness and stress in nurses has been emphasized. In this sense, the intelligent use of hardiness is enable nurses to cope better with stress and contribute to being happier. This study aimed to examine the relationship among hardiness, perceived stress, and happiness in nurses. Moreover, we examined the mediator role of hardiness on the relationship between perceived stress and happiness in nurses. Our study revealed that hardi-attitude nurses evaluate situations as less stressful which results in a higher happiness. This study showed hardiness as being a protective factor against perceived stress and a facilitating factor for happiness in nurses. The findings could be important in training future nurses so that hardiness can be imparted, thereby giving them the ability to control their stress. Nursing is a stressful occupation with high levels of stress within the health professions. Given that hardiness is an important construct to enable nurses to cope better with stress and contribute to being happier; therefore, it is necessary we advance our knowledge about the aetiology of happiness, especially the role of hardiness in decreasing stress levels and increasing happiness. The present study sought to investigate the role of hardiness as a mediator between perceived stress and happiness. The participants, comprising 252 nurses from six private hospitals in Tehran, completed the Personal Views Survey, the Perceived Stress Scale, and the Oxford Happiness Inventory. Structural Equation Modelling (SEM) was used to analyse the data and answer the research hypotheses. As expected, hardiness partially mediated between perceived stress and happiness among nurses, and nurses with low levels of perceived stress were more likely to report greater hardiness and happiness. In addition, nurses with high levels of hardiness were more likely to report happiness. This study showed hardiness as being a protective factor against perceived stress and a facilitating factor for happiness in nurses. The findings could be important in training future nurses so that hardiness can be imparted, thereby giving them the ability to control their stress.
  5. Selvaraj J, Murugappan M, Wan K, Yaacob S
    Biomed Eng Online, 2013;12:44.
    PMID: 23680041 DOI: 10.1186/1475-925X-12-44
    Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.
  6. 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.
  7. Muthusamy H, Polat K, Yaacob S
    PLoS One, 2015;10(3):e0120344.
    PMID: 25799141 DOI: 10.1371/journal.pone.0120344
    In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature.
  8. 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.
  9. Hema CR, Paulraj MP, Yaacob S, Adom AH, Nagarajan R
    Adv Exp Med Biol, 2011;696:565-72.
    PMID: 21431597 DOI: 10.1007/978-1-4419-7046-6_57
    A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
  10. 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.
  11. Oung QW, Muthusamy H, Lee HL, Basah SN, Yaacob S, Sarillee M, et al.
    Sensors (Basel), 2015 Aug 31;15(9):21710-45.
    PMID: 26404288 DOI: 10.3390/s150921710
    Parkinson's Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.
  12. Aziz Z, Huin WK, Badrul Hisham MD, Tang WL, Yaacob S
    Complement Ther Med, 2018 Aug;39:49-55.
    PMID: 30012392 DOI: 10.1016/j.ctim.2018.05.011
    OBJECTIVE: To present a systematic review of randomised controlled trials (RCTs) examining the effects of MPFF in the management of haemorrhoid symptoms.

    METHODS: Electronic databases including CENTRAL, CINAHL, EMBASE, MEDLINE were searched up to April 2018 for relevant RCTs. Journal and conference proceedings were also searched. Two review authors independently selected trials, extracted data, assessed the risks of bias in included trials and graded the quality of evidence. Meta-analyses were conducted for studies presenting similar outcomes.

    RESULTS: Ten RCTs involving 1164 participants were included. These RCTs varied in terms of patients' grade of haemorrhoids, length of trials, and outcome assessed. Most of the studies did not describe adequately the process of randomisation and allocation concealment. The pooled analysis of data from three studies indicated that there was significant difference between groups for the bleeding outcome, favoring the MPFF group (RR 1.46; 95% CI 1.10-1.93; p = 0.008). Except for bleeding, the current evidence did not show MPFF has significant effects on all the other outcomes examined when compared with placebo. Even then, the quality of evidence for bleeding was judged as low due to the small number and inconsistent results among the included studies.

    CONCLUSION: This review highlights the need for further rigorous research if MPFF was to be routinely used for the treatment of haemorrhoid symptoms.

  13. 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.

  14. Zakaria Z, Abdul Rahim R, Mansor MS, Yaacob S, Ayub NM, Muji SZ, et al.
    Sensors (Basel), 2012;12(6):7126-56.
    PMID: 22969341 DOI: 10.3390/s120607126
    Magnetic Induction Tomography (MIT), which is also known as Electromagnetic Tomography (EMT) or Mutual Inductance Tomography, is among the imaging modalities of interest to many researchers around the world. This noninvasive modality applies an electromagnetic field and is sensitive to all three passive electromagnetic properties of a material that are conductivity, permittivity and permeability. MIT is categorized under the passive imaging family with an electrodeless technique through the use of excitation coils to induce an electromagnetic field in the material, which is then measured at the receiving side by sensors. The aim of this review is to discuss the challenges of the MIT technique and summarize the recent advancements in the transmitters and sensors, with a focus on applications in biological tissue imaging. It is hoped that this review will provide some valuable information on the MIT for those who have interest in this modality. The need of this knowledge may speed up the process of adopted of MIT as a medical imaging technology.
  15. Lachman JM, Juhari R, Stuer F, Zinser P, Han Q, Gardner F, et al.
    BMC Public Health, 2023 Feb 04;23(1):241.
    PMID: 36737719 DOI: 10.1186/s12889-023-15065-4
    BACKGROUND: Despite impressive strides in health, social protection, and education, children continue to experience high rates of child maltreatment in Malaysia. This mixed-methods study assessed the feasibility of a five-session, social learning-based parenting program delivered by government staff in a community setting to reduce violence against children.

    METHODS: Parents of children from birth to 17 years were recruited from two communities near Kuala Lumpur to participate in the government-run program called the Naungan Kasih Positive Parenting Program ("Protecting through Love" in Bahasa Melayu). Quantitative data from female caregivers (N = 74) and children ages 10-17 (N = 26) were collected along with qualitative interviews and focus groups with parents, children, and facilitators. The primary outcome was child maltreatment with secondary outcomes including neglect, positive parenting, acceptability of corporal punishment, harsh parenting, positive discipline, and child behavior problems. Multilevel Poisson regression and multilevel linear regression were conducted to compare baseline and post-test outcomes. Qualitative interviews and focus groups examined how participants experienced the program utilizing a thematic analysis approach.

    RESULTS: Quantitative analyses found pre-post reductions in overall child maltreatment, physical abuse, emotional abuse, attitudes supporting corporal punishment, parent sense of inefficacy, and child behavior problems. There were no reported changes on positive and harsh parenting, parental mental health, and marital satisfaction, nor were there any other significant changes reported by children. Qualitative findings suggested that the program had tangible benefits for female caregivers involved in the program, with the benefits extending to their family members.

    CONCLUSIONS: This feasibility study is one of the few studies in Southeast Asia that examined the feasibility and initial program impact of a parenting program delivered by government staff to families with children across the developmental spectrum from birth to 17 years. Promising results suggest that the program may reduce child maltreatment across a range of child ages. Findings also indicate areas for program improvement prior to further delivery and testing, including additional training and content on sexual and reproductive health, parenting children with disabilities, and online child protection.

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