Dual-Tree Complex Wavelet Packet Transform and Feature Selection Techniques for Infant Cry Classification

A Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction has been used in infant cry signal classification to extract the feature. Total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band after five level decomposition by DT-CWPT. Feature selection techniques used to deal with massive information obtained from DT-CWPT extraction. The feature selection techniques reduced the number of features by select and form feature subset for classification phase. ELM classifier with 10-fold cross-validation scheme was used to classify the infant cry signal. Three experiments were conducted with different feature sets for three binary classification problems (Asphyxia versus Normal, Deaf versus Normal, and Hunger versus Pain). The results reported that features selection techniques reduced the number of features and achieved high accuracy

Cry-Based Classification of Healthy and Sick Infants Using Adapted Boosting Mixture Learning Method for Gaussian Mixture Models

We make use of information inside infant’s cry signal in order to identify the infant’s psychological condition. Gaussian mixture models (GMMs) are applied to distinguish between healthy full-term and premature infants, and those with specific medical problems available in our cry database. Cry pattern for each pathological condition is created by using adapted boosting mixture learning (BML) method to estimate mixture model parameters. In the first experiment, test results demonstrate that the introduced adapted BML method for learning of GMMs has a better performance than conventional EM-based reestimation algorithm as a reference system in multipathological classification task. This newborn cry-based diagnostic system (NCDS) extracted Mel-frequency cepstral coefficients (MFCCs) as a feature vector for cry patterns of newborn infants. In binary classification experiment, the system discriminated a test infant’s cry signal into one of two groups, namely, healthy and pathological based on MFCCs. The binary classifier achieved a true positive rate of 80.77% and a true negative rate of 86.96% which show the ability of the system to correctly identify healthy and diseased infants, respectively

COMPARISON OF K-NN AND NAÏVE BAYES CLASSIFIER FOR ASPHYXIA FACTOR

Asphyxia is influenced by several factors, including the factors affecting the Immediate Was maternal factors That relates Conditions mother Pregnancy and childbirth such as hypoxia mother, Asphyxia factor data can be modeled using the classification approach. this paper will be compared k-nearest neighbor algorithm  and Naive Bayes classifier to classify asphyxia factor. Naive Bayes uses the concept of Bayes’ Theorem which assuming the independency between predictors. Basically, Bayes theorem is used to compute the subsequent probabilities. Analysis of the two algorithms has been done on several parameters such as Kappa statistics, classification error, precision, recall, F-measure and AUC. We achieved the best classification accuracy with KNN algorithm, 92,27%, for k=4. are lower than the rates achieved with Naïve Bayes 83,19%

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges

Neonatal pain detection in videos using the iCOPEvid dataset and an ensemble of descriptors extracted from Gaussian of Local Descriptors

Abstract Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Caractérisation des cris des nourrissons en vue du diagnostic précoce de différentes pathologies

L’utilisation des signaux de cris dans le diagnostic se base sur les théories qui ont été proposées par les différents chercheurs dans le domaine. Le principal objectif de leurs travaux était l’analyse spectrographique ainsi que la modélisation des signaux de cris. Ils ont démontré que les caractéristiques acoustiques des cris des nouveau-nés sont liées à des conditions médicales particulières. Cette thèse est destinée à contribuer à l’amélioration de la précision de la reconnaissance des cris pathologiques par la combinaison de plusieurs paramètres acoustiques issus de l'analyse spectrographique et des paramètres qui qualifient les cordes et le conduit vocal. Car les caractéristiques acoustiques représentant le conduit vocal ont été largement utilisées pour la classification des cris, alors que les caractéristiques des cordes vocales pour la reconnaissance automatique des cris, ainsi que leurs techniques efficaces d’extraction n’ont pas été exploitées. Pour répondre à cet objectif, nous avons procédé en premier lieu à une caractérisation qualitative des cris des nouveau-nés sains et malades en utilisant les caractéristiques qui ont été définies dans la littérature et qui qualifient le comportement des cordes et du conduit vocal pendant le cri. Cette étape nous a permis d’identifier les caractéristiques les plus importantes dans la différenciation des cris pathologiques étudiés. Pour l’extraction des caractéristiques sélectionnées, nous avons implémenté des méthodes de mesures efficaces permettant de dépasser la surestimation et la sous-estimation des caractéristiques. L’approche de quantification proposée et utilisée dans ce travail facilite l’analyse automatique des cris et permet une utilisation efficace de ces caractéristiques dans le système de diagnostic. Nous avons procédé aussi à des tests expérimentaux pour la validation de toutes les approches introduites dans cette thèse. Les résultats sont satisfaisants et montrent une amélioration dans la reconnaissance des cris par pathologie. Les travaux réalisés sont présentés dans cette thèse sous forme de trois articles publiés dans différents journaux. Deux autres articles publiés dans des comptes rendus de conférences avec comité de lecture sont présentés en annexes

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies