Computational Intelligence in Healthcare

The number of patient health data has been estimated to have reached 2314 exabytes by 2020. Traditional data analysis techniques are unsuitable to extract useful information from such a vast quantity of data. Thus, intelligent data analysis methods combining human expertise and computational models for accurate and in-depth data analysis are necessary. The technological revolution and medical advances made by combining vast quantities of available data, cloud computing services, and AI-based solutions can provide expert insight and analysis on a mass scale and at a relatively low cost. Computational intelligence (CI) methods, such as fuzzy models, artificial neural networks, evolutionary algorithms, and probabilistic methods, have recently emerged as promising tools for the development and application of intelligent systems in healthcare practice. CI-based systems can learn from data and evolve according to changes in the environments by taking into account the uncertainty characterizing health data, including omics data, clinical data, sensor, and imaging data. The use of CI in healthcare can improve the processing of such data to develop intelligent solutions for prevention, diagnosis, treatment, and follow-up, as well as for the analysis of administrative processes. The present Special Issue on computational intelligence for healthcare is intended to show the potential and the practical impacts of CI techniques in challenging healthcare applications

Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review.

Computer systems for Electrocardiogram (ECG) analysis support the clinician in tedious tasks (e.g., Holter ECG monitored in Intensive Care Units) or in prompt detection of dangerous events (e.g., ventricular fibrillation). Together with clinical applications (arrhythmia detection and heart rate variability analysis), ECG is currently being investigated in biometrics (human identification), an emerging area receiving increasing attention. Methodologies for clinical applications can have both differences and similarities with respect to biometrics. This paper reviews methods of ECG processing from a pattern recognition perspective. In particular, we focus on features commonly used for heartbeat classification. Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) because of their popularity; however, other techniques such as Hidden Markov Models and Kalman Filtering will be also mentioned

CLASSIFICATION OF ARRHYTHMIA DISEASES BY THE CONVOLUTIONAL NEURAL NETWORK METHOD BASED ON ECG IMAGES

Arrhythmia is a heart disorder that refers to an abnormal heartbeat rhythm. Arrhythmia detection uses an electrocardiogram (ECG) to describe the heart's electrical activity. This research aimed to know the performance of the Convolutional Neural Network method in classifying arrhythmia diseases based on ECG signal images. Several stages were used to classify arrhythmias: the pre-processing data stage, CNN model formation stage, model compiling, training, model testing, and evaluation. The CNN model architecture that is formed involves 7 Convolution Layers, 7 Pooling Layers, 2 Dropout Layers, 2 Dense Layers, and 1 Flatten Layer, as well as ReLu and Softmax activation functions. The input variable in the classification process with CNN is an ECG image. The output variable is the classification of ECG signals into 17 classes, including normal sinus and pacemaker rhythms. The processed data are 1000 images; the division scenario is 750 training data and 250 testing data. The result of arrhythmia's classification based on ECG image testing data using the CNN model shows the levels of Accuracy, Precision, Recall, and F1-score levels are 81%, 80%, 71%, and 73%, respectively, respectively. With the F1-score value as a measurement reference, the CNN model performs well in classifying ECG image