Objective: Heart abnormality detection using heart sound signals (phonocardiogram (PCG)) has been an active research area for the last few decades. In this paper, automatic heart sound classification using segmented and unsegmented PCG signals is presented. Approach: In this paper: (i) we perform an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance; (ii) both segmented and unsegmented PCG signals are studied and important results concerning the respective feature subsets and their classification performances are reported; and (iii) different classification algorithms, including the support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory network (LSTMs), are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. Main results: It is observed that LSTM performs better on mel-frequency cepstral coefficient (MFCC) features extracted from unsegmented PCG data, with an area under curve (AUC) score of 91.39%, however, the MFCC features do not show a consistent performance with other classifiers (the second highest AUC score is 62.08% with the decision tree classifier). In contrast, in the case of time-frequency features from segmented data, the performance of all the classifiers is appreciable with AUC scores over 70%. In particular, the conventional machine learning techniques shows consistency in achieving over 80% in AUC scores. Significance: The results of this study highlight the importance of time and frequency domain features. Thus it is necessary to employ both the time and frequency features of segmented PCG signals to achieve improved classification.
Cardiovascular diseases (CVDs) are the main cause of deaths all over the world. Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. In this work, we have used short-time Fourier transform (STFT) based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform three different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on combined PhysioNet-PASCAL dataset and (iii) finally, transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. We propose a novel, less complex and relatively light custom CNN model for the classification of PhysioNet, combined and PASCAL datasets. The first study achieves an accuracy, sensitivity, specificity, precision and F1 score of 95.4%, 96.3%, 92.4%, 97.6% and 96.98% respectively while the second study shows accuracy, sensitivity, specificity, precision and F1 score of 94.2%, 95.5%, 90.3%, 96.8% and 96.1% respectively. Finally, the third study shows a precision of 98.29% on the noisy PASCAL dataset with transfer learning approach. All the three proposed approaches outperform most of the recent competing studies by achieving comparatively high classification accuracy and precision, which make them suitable for screening CVDs using PCG signals.
Summary.-The microsporangium is tetrasporangiate. The anther wall development follows Dicotyledonous and Basic types. The endothecium is multi-layered and becomes fibrous in the tip region only. The tapetum is of dual origin. Hypodermal resorption tissue differentiates in the septal region of the anther lobe and facilitates the anther dehiscence. The dehiscence of anther is porous. ln Solanum citrul/ifolium and S. cornutum, the anther dehiscence by apical pore as weil as by pores formed in longitudinal suture.Résumé.-Le microsporange est tétrasporangié. Le développement de la paroi de l'anthère suit les types Dicotylédone et Basique. L'endothécium comporte plusieurs assises et devient fibreux seulement dans la région apical. Le tapis est d'origine double. Un tissu de résorption hypodermique se différencie dans la région septale du lobe de l'anthère et facilite sa déhiscence. Celle-ci est poricide. Chez Solanum citrullifo/ium et S. cornutum, la déhiscence s'effectue aussi bien par un pore apical que par des pores formés sur la ligne de suture longitudinale.
Remote monitoring of industrial machines is important in order to have high productivitycontinuous process control. New emerging technologies such as Internet of Things (IoT), arefacilitating the process of automation and remote controlling in multiple ways. In this researchwork, a robotic manipulator based on PUMA robot structure and an intelligent remotemaintenance system is developed aimed to ensure the continual accurate operation of a roboticmanipulator. Entire manipulator is designed from different types of motors like DC motors,stepper motors and servo motors along with their motor drivers. Various types of sensors likeTachometer, DHT11 Temperature and Humidity sensor, Capacitance meter and EMFDetector are also designed and interfaced with the manipulator for identification of differentfaults. Android App and Wi-Fi module are used for the remote-controlled automationpurposes. The authors were succeeded in detecting various faults in the robotic manipulatorand notifying the operator in real-time.
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