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Detection of atrial fibrillation (AF) remains one of the major concerns in the field of medical research. AF is one of the main cause for stroke. AF is characterized by irregular heartbeats and absence of P waves in electrocardiogram (ECG) signal. In this article, we propose a method, combining Poincare plot derived and RR interval‐based features to classify given ECG signal into normal, AF and other rhythms. Classification and regression tree, K‐nearest neighbor, support vector machine, artificial neural network, ResNet18, convolutional neural network (CNN)‐long short term memory (LSTM) are implemented for classification of ECG signal. The class specific accuracies for the three rhythms are computed. Physionet challenge 2017 database is used for evaluation and testing of the developed algorithm. The database has 5154 normal, 771 AF, 2557 other rhythm and 46 noisy signals. Three Poincare derived features viz: SD1, SD2, and ratio of SD1 to SD2, three RR interval features viz: Mean stepping increment of RR interval, approximate entropy and sample entropy are computed and are given to classifiers. During fivefold cross‐validation, CNN‐LSTM classifier showed best result with class specific accuracy for normal of 96.65%, AF of 97.55%, other rhythms of 94.87%, overall accuracy of 96.17% and F1 score of 0.9589. The developed technique can bring change in conventional practice in AF diagnosis and can aid the physician as an assisted tool. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4.5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99.10% with area under the curve of 0.998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc. [ABSTRACT FROM AUTHOR]

Recognition of human emotion states for affective computing based on Electroencephalogram (EEG) signal is an active yet challenging domain of research. In this study we propose an emotion recognition framework based on 2-dimensional valence-arousal model to classify High Arousal-Positive Valence (Happy) and Low Arousal-Negative Valence (Sad) emotions. In total 34 features from time, frequency, statistical and nonlinear domain are studied for their efficacy using Artificial Neural Network (ANN). The EEG signals from various electrodes in different scalp regions viz., frontal, parietal, temporal, occipital are studied for performance. It is found that ANN trained using features extracted from the frontal region has outperformed that of all other regions with an accuracy of 93.25%. The results indicate that the use of smaller set of electrodes for emotion recognition that can simplify the acquisition and processing of EEG data. The developed system can aid immensely to the physicians in their clinical practice involving emotional states, continuous monitoring, and development of wearable sensors for emotion recognition. [ABSTRACT FROM AUTHOR]

Content‐based medical image retrieval (CBMIR) is a mechanism to handle a huge quantity of image data generated in various medical imaging modalities. In recent years, due to the evolution of computer vision and digital imaging modalities, a large number of medical images are generated. Consequently, the task of retrieving medical images from a large image database becomes more tedious due to variation in the size and shape of the images. Hence, it is necessary to design an appropriate system for medical image retrieval. In this paper a methodology for CBMIR using features of an image such as colour, shape, and texture is proposed to represent and retrieve the images from a large database that are relevant to a given query image. This methodology is evaluated for the application of retrieving the brain MRI images of different planes (coronal, sagittal, and transverse) from a dataset of normal and demented subjects. The features are determined in terms of Grey level co‐occurrence based Haralik's features and histogram based cumulative distribution function (CDF). The image retrieval mechanism is designed using the K‐Nearest Neighbour algorithm by finding the minimum distance between query and database images. The performance parameters such as precision and recall are calculated. The average accuracy of 95.5% are obtained. The results provided ensures the capability to use it as assistive framework for radiologists in radiology image retrieval and classification. [ABSTRACT FROM AUTHOR]

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential. [ABSTRACT FROM AUTHOR]