A novel ternary pattern-based automatic psychiatric disorders classification using ECG signals.

Neuropsychiatric disorders are one of the leading causes of disability. Mental health problems can occur due to various biological and environmental factors. The absence of definitive confirmatory diagnostic tests for psychiatric disorders complicates the diagnosis. It's critical to distinguish between bipolar disorder, depression, and schizophrenia since their symptoms and treatments differ. Because of brain-heart autonomic connections, electrocardiography (ECG) signals can be changed in behavioral disorders. In this research, we have automatically classified bipolar, depression, and schizophrenia from ECG signals. In this work, a new hand-crafted feature engineering model has been proposed to detect psychiatric disorders automatically. The main objective of this model is to accurately detect psychiatric disorders using ECG beats with linear time complexity. Therefore, we collected a new ECG signal dataset containing 3,570 ECG beats with four categories. The used categories are bipolar, depression, schizophrenia, and control. Furthermore, a new ternary pattern-based signal classification model has been proposed to classify these four categories. Our proposal contains four essential phases, and these phases are (i) multileveled feature extraction using multilevel discrete wavelet transform and ternary pattern, (ii) the best features selection applying iterative Chi2 selector, (iii) classification with artificial neural network (ANN) to calculate lead wise results and (iv) calculation the voted/general classification accuracy using iterative majority voting (IMV) algorithm. tenfold cross-validation is one of the most used validation techniques in the literature, and this validation model gives robust classification results. Using ANN with tenfold cross-validation, lead-by-lead and voted results have been calculated. The lead-by-lead accuracy range of the proposed model using the ANN classifier is from 73.67 to 89.19%. By deploying the IMV method, the general classification performance of our ternary pattern-based ECG classification model is increased from 89.19 to 96.25%. The findings and the calculated classification accuracies (single lead and voted) clearly demonstrated the success of the proposed ternary pattern-based advanced signal processing model. By using this model, a new wearable device can be proposed.

Deep convolutional neural networks based ECG beats classification to diagnose cardiovascular conditions.

Medical practitioners need to understand the critical features of ECG beats to diagnose and identify cardiovascular conditions accurately. This would be greatly facilitated by identifying the significant features of frequency components in temporal ECG wave-forms using computational methods. In this study, we have proposed a novel ECG beat classifier based on a customized VGG16-based Convolution Neural Network (CNN) that uses the time-frequency representation of temporal ECG, and a method to identify the contribution of interpretable ECG frequencies when classifying based on the SHapley Additive exPlanations (SHAP) values. We applied our model to the MIT-BIH arrhythmia dataset to classify the ECG beats and to characterise of the beats frequencies. This model was evaluated with two advanced time-frequency analysis methods. Our results indicated that for 2-4 classes our proposed model achieves a classification accuracy of 100% and for 5 classes it achieves a classification accuracy of 99.90%. We have also tested the proposed model using premature ventricular contraction beats from the American Heart Association (AHA) database and normal beats from Lobachevsky University Electrocardiography database (LUDB) and obtained a classification accuracy of 99.91% for the 5-classes case. In addition, SHAP value increased the interpretability of the ECG frequency features. Thus, this model could be applicable to the automation of the cardiovascular diagnosis system and could be used by clinicians.

Local feature descriptors based ECG beat classification.

ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained results show that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.