A real-time data mining technique applied for critical ECG rhythm on handheld device

Sudden cardiac arrest is often caused by ventricular arrhythmias and these episodes can lead to death for patients with chronic heart disease. Hence, detection of such arrhythmia is crucial in mobile ECG monitoring. In this research, a systematic study is carried out to investigate the possible limitations that are preventing the realisation of a real-time ECG arrhythmia data-mining algorithm suitable for application on mobile devices. Based on the findings, a computationally lightweight algorithm is devised and tested. Ventricular tachycardia (VT) is the most common type of ventricular arrhythmias and is also the deadliest.. A ventricular tachycardia (VT) episode is due to a disorder ofthe regular contractions ofthe heart. It occurs when the human heart ventricles generate a rapid heartbeat which disrupts the regular physiology cycle. The normal sinus rhythm (NSR) of a regular human heart beat signal has its signature PQRST waveform and in regular pattern. Whereas, the characteristics of a ventricular tachycardia (VT) signal waveforms are short R-R intervals, widen QRS duration and the absence of P-waves. Each type of ECG arrhythmia previously mentioned has a unique waveform signature that can be exploited as features to be used for the realization of an automated ECG analysis application. In order to extract this known ECG waveform feature, a time-domain analysis is proposed for feature extraction. Cross-correlation allows the computation of a co-efficient that quantifies the similarity between two times-series. Hence, by cross-correlating known ECG waveform templates with an unknown ECG signal, the coefficient can indicate the similarities. In previous published work, a preliminary study was carried out. The cross-correlation coefficient wave (CCW) technique was introduced for feature extraction. The outcome ofthis work presents CCW as a promising feature to differentiate between NSR, VT and Vfib signals. Moreover, cross-correlation computation does not require high computational overhead. Next, an automated detection algorithm requires a classification mechanism to make sense of the feature extracted. A further study is conducted and published, a fuzzy set k-NN classifier was introduced for the classification of CCW feature extracted from ECG signal segments. A training set of size 180 is used. The outcome of the study indicates that the computationally light-weight fuzzy k-NN classifier can reliably classify between NSR and VT signals, the class detection rate is low for classifying Vfib signal using the fuzzy k-NN classifier. Hence, a modified algorithm known as fuzzy hybrid classifier is proposed. By implementing an expert knowledge based fuzzy inference system for classification of ECG signal; the Vfib signal detection rate was improved. The comparison outcome was that the hybrid fuzzy classifier is able to achieve 91.1% correct rate, 100% sensitivity and 100% specificity. The previously mentioned result outperforms the compared classifiers. The proposed detection and classification algorithm is able to achieve high accuracy in analysing ECG signal feature of NSR, VT and Vfib nature. Moreover, the proposed classifier is successfully implemented on a smart mobile device and it is able to perform data-mining of the ECG signal with satisfiable results

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

Advances in fuzzy rule-based system for pattern classification

Ph.DDOCTOR OF PHILOSOPH

Automated Classification for Electrophysiological Data: Machine Learning Approaches for Disease Detection and Emotion Recognition

Smart healthcare is a health service system that utilizes technologies, e.g., artificial intelligence and big data, to alleviate the pressures on healthcare systems. Much recent research has focused on the automatic disease diagnosis and recognition and, typically, our research pays attention on automatic classifications for electrophysiological signals, which are measurements of the electrical activity. Specifically, for electrocardiogram (ECG) and electroencephalogram (EEG) data, we develop a series of algorithms for automatic cardiovascular disease (CVD) classification, emotion recognition and seizure detection. With the ECG signals obtained from wearable devices, the candidate developed novel signal processing and machine learning method for continuous monitoring of heart conditions. Compared to the traditional methods based on the devices at clinical settings, the developed method in this thesis is much more convenient to use. To identify arrhythmia patterns from the noisy ECG signals obtained through the wearable devices, CNN and LSTM are used, and a wavelet-based CNN is proposed to enhance the performance. An emotion recognition method with a single channel ECG is developed, where a novel exploitative and explorative GWO-SVM algorithm is proposed to achieve high performance emotion classification. The attractive part is that the proposed algorithm has the capability to learn the SVM hyperparameters automatically, and it can prevent the algorithm from falling into local solutions, thereby achieving better performance than existing algorithms. A novel EEG-signal based seizure detector is developed, where the EEG signals are transformed to the spectral-temporal domain, so that the dimension of the input features to the CNN can be significantly reduced, while the detector can still achieve superior detection performance

Нечеткие логические выводы и заключения в экспертных системах медицинской диагностики

The main problems in making a correct diagnosis are: subjectivity and insufficient qualifications of the doctor, difficulties in correctly assessing the patient’s complaints, signs and symptoms of the disease observed in the patient, as well as individual manifestations of the symptoms of the disease. In publications on the use of expert systems for medical diagnostics using fuzzy logic, the main attention was paid to the medical features of the problem. In this work, for the first time, general methodological aspects of building such systems, creating databases, representing by fuzzy sets of real numbers, digital scales, linguistic and Boolean data of symptom values are formulated. The types of membership functions that are advisable to use to represent the symptoms of diseases are proposed. In fuzzy-logical conclusions, not only the values of the characteristic functions of the logical terms of individual symptoms, but also complex arithmetic functions of their values are used

АНАЛИЗ ПРОЦЕССА КОНТРОЛЯ МЕДИЦИНСКОЙ ПОМОЩИ В РАМКАХ ПРОГРАММЫ ОБЯЗАТЕЛЬНОГО МЕДИЦИНСКОГО СТРАХОВАНИЯ

The relevance of the presented paper deals with the necessity of determining problems and effective solutions for medical organizations on the stage of medical documentation reports control purposely for forecasting the average of financial resources that can be obtained in the scope of compulsory health insurance program.The aim of the study – for the purpose of further issues definition present formal model of the analyzed process using a set of system analysis methods.Material and methods. System analysis methods, especially IDEF0 diagrams and activity diagrams, for estimation of medical expert’s agreement Cohen’s kappa was used.The results. Based on the specification documents and expert’s experience the spread description on the process the control of volume, duration, quality and conditions of medical service assignment by medical organizations conducted by medical insurance organization was presented. Inputs, outputs, elements of management and executives were determined. As a result of decomposition, subprocesess were presented within activity diagrams.Conclusions. The obtained results of research allows to conclude that there is a set of problems which appear when medical organizations send reports for getting financial resources for clinical service realization in the scope of compulsory health insurance program. On the grounds of determined problems, we can conclude that it is necessary to develop an intellectual information system for estimating clinical records concerning getting financial resources for clinical service. In respect that human factor influence on the main stages of the analyzed process, we propose to use fuzzy logic as an inference engine. The self-learning function of the system will provide case-based reasoning.Актуальность работы обусловлена необходимостью выявления проблем и выработки эффективных решений для медицинских организаций на этапе контроля отчетной медицинской документации с целью прогнозирования получения денежных средств по программе обязательного медицинского страхования.Цель работы – используя средства аппарата системного анализа, представить формальную модель исследуемого процесса для дальнейшего выявления проблем.Материал и методы. Применяются методы системного анализа, в частности диаграммы IDEF0, а также диаграммы деятельности (activitydiagram), для оценки согласованности медицинских экспертов используется каппа Коэна.Результаты. На основе нормативных документов, а также опыта экспертов представлено развернутое описание процесса контроля объемов, сроков, качества и условий предоставления медицинской помощи медицинскими организациями, проводимого страховой медицинской организацией. Определены входные и выходные параметры, а также элементы управления и исполнения процесса. Полученные в результате декомпозиции подпроцессы представлены с применением диаграмм последовательности.Выводы. Полученные результаты исследования позволяют сделать вывод о наличии ряда проблем, с которыми сталкиваются медицинские организации при подаче на проверку медицинской отчетности для последующего получения денежных средств за оказанные медицинские услуги по программе обязательного медицинского страхования. На основании выявленных проблем можно сделать вывод о необходимости разработки интеллектуальной информационной системы, способной проводить оценку историй болезни на предмет получения денежных средств за оказанные медицинские услуги. Принимая во внимание наличие человеческого фактора на наиболее важных итерациях иследуемого процесса, предлагается в качестве метода логического вывода будущей системы использовать нечеткую логику, а функцию самообучаемости системы обеспечит реализованный в перспективе анализ прецеденто

Automated Classification Model With OTSU and CNN Method for Premature Ventricular Contraction Detection

Premature ventricular contraction (PVC) is one of the most common arrhythmias which can cause palpitation, cardiac arrest, and other symptoms affecting the work and rest activities of a patient. However, patients hardly decipher their own feelings to determine the severity of the disease thus, requiring a professional medical diagnosis. This study proposes a novel method based on image processing and convolutional neural network (CNN) to extract electrocardiography (ECG) curves from scanned ECG images derived from clinical ECG reports, and segment and classify heartbeats in the absence of a digital ECG data. The ECG curve is extracted using a comprehensive algorithm that combines the OTSU algorithm with erosion and dilation. This algorithm can efficiently and accurately separate the ECG curve from the ECG background grid. The performance of the classification model was evaluated and optimized using hundreds of clinical ECG data collected from Fujian Provincial Hospital. Additionally, thousands of clinical ECG reports were scanned to digital images as the test set to confirm the accuracy of the algorithm for practical application. Results showed that the average sensitivity, specificity, positive predictive value, and accuracy of the proposed model on the MIT-BIH dataset were 95.47%, 97.72%, 98.75%, and 98.25%, respectively. The classification average sensitivity, specificity, positive predictive value, and accuracy based on clinical scanned ECG images can reach to 97.24%, 81.6%, 83.8%, and 89.33%, respectively, and the clinical feasibility is high. Overall, the proposed method can extract ECG curves from scanned ECG images efficiently and accurately. Furthermore, it performs well on heartbeat classification of normal (N) and ventricular premature heartbeat

Heart Diseases Diagnosis Using Artificial Neural Networks

Information technology has virtually altered every aspect of human life in the present era. The application of informatics in the health sector is rapidly gaining prominence and the benefits of this innovative paradigm are being realized across the globe. This evolution produced large number of patients’ data that can be employed by computer technologies and machine learning techniques, and turned into useful information and knowledge. This data can be used to develop expert systems to help in diagnosing some life-threating diseases such as heart diseases, with less cost, processing time and improved diagnosis accuracy. Even though, modern medicine is generating huge amount of data every day, little has been done to use this available data to solve challenges faced in the successful diagnosis of heart diseases. Highlighting the need for more research into the usage of robust data mining techniques to help health care professionals in the diagnosis of heart diseases and other debilitating disease conditions. Based on the foregoing, this thesis aims to develop a health informatics system for the classification of heart diseases using data mining techniques focusing on Radial Basis functions and emerging Neural Networks approach. The presented research involves three development stages; firstly, the development of a preliminary classification system for Coronary Artery Disease (CAD) using Radial Basis Function (RBF) neural networks. The research then deploys the deep learning approach to detect three different types of heart diseases i.e. Sleep Apnea, Arrhythmias and CAD by designing two novel classification systems; the first adopt a novel deep neural network method (with Rectified Linear unit activation) design as the second approach in this thesis and the other implements a novel multilayer kernel machine to mimic the behaviour of deep learning as the third approach. Additionally, this thesis uses a dataset obtained from patients, and employs normalization and feature extraction means to explore it in a unique way that facilitates its usage for training and validating different classification methods. This unique dataset is useful to researchers and practitioners working in heart disease treatment and diagnosis. The findings from the study reveal that the proposed models have high classification performance that is comparable, or perhaps exceed in some cases, the existing automated and manual methods of heart disease diagnosis. Besides, the proposed deep-learning models provide better performance when applied on large data sets (e.g., in the case of Sleep Apnea), with reasonable performance with smaller data sets. The proposed system for clinical diagnoses of heart diseases, contributes to the accurate detection of such disease, and could serve as an important tool in the area of clinic support system. The outcome of this study in form of implementation tool can be used by cardiologists to help them make more consistent diagnosis of heart diseases

ECG analysis and classification using CSVM, MSVM and SIMCA classifiers

Reliable ECG classification can potentially lead to better detection methods and increase accurate diagnosis of arrhythmia, thus improving quality of care. This thesis investigated the use of two novel classification algorithms: CSVM and SIMCA, and assessed their performance in classifying ECG beats. The project aimed to introduce a new way to interactively support patient care in and out of the hospital and develop new classification algorithms for arrhythmia detection and diagnosis. Wave (P-QRS-T) detection was performed using the WFDB Software Package and multiresolution wavelets. Fourier and PCs were selected as time-frequency features in the ECG signal; these provided the input to the classifiers in the form of DFT and PCA coefficients. ECG beat classification was performed using binary SVM. MSVM, CSVM, and SIMCA; these were subsequently used for simultaneously classifying either four or six types of cardiac conditions. Binary SVM classification with 100% accuracy was achieved when applied on feature-reduced ECG signals from well-established databases using PCA. The CSVM algorithm and MSVM were used to classify four ECG beat types: NORMAL, PVC, APC, and FUSION or PFUS; these were from the MIT-BIH arrhythmia database (precordial lead group and limb lead II). Different numbers of Fourier coefficients were considered in order to identify the optimal number of features to be presented to the classifier. SMO was used to compute hyper-plane parameters and threshold values for both MSVM and CSVM during the classifier training phase. The best classification accuracy was achieved using fifty Fourier coefficients. With the new CSVM classifier framework, accuracies of 99%, 100%, 98%, and 99% were obtained using datasets from one, two, three, and four precordial leads, respectively. In addition, using CSVM it was possible to successfully classify four types of ECG beat signals extracted from limb lead simultaneously with 97% accuracy, a significant improvement on the 83% accuracy achieved using the MSVM classification model. In addition, further analysis of the following four beat types was made: NORMAL, PVC, SVPB, and FUSION. These signals were obtained from the European ST-T Database. Accuracies between 86% and 94% were obtained for MSVM and CSVM classification, respectively, using 100 Fourier coefficients for reconstructing individual ECG beats. Further analysis presented an effective ECG arrhythmia classification scheme consisting of PCA as a feature reduction method and a SIMCA classifier to differentiate between either four or six different types of arrhythmia. In separate studies, six and four types of beats (including NORMAL, PVC, APC, RBBB, LBBB, and FUSION beats) with time domain features were extracted from the MIT-BIH arrhythmia database and the St Petersburg INCART 12-lead Arrhythmia Database (incartdb) respectively. Between 10 and 30 PCs, coefficients were selected for reconstructing individual ECG beats in the feature selection phase. The average classification accuracy of the proposed scheme was 98.61% and 97.78 % using the limb lead and precordial lead datasets, respectively. In addition, using MSVM and SIMCA classifiers with four ECG beat types achieved an average classification accuracy of 76.83% and 98.33% respectively. The effectiveness of the proposed algorithms was finally confirmed by successfully classifying both the six beat and four beat types of signal respectively with a high accuracy ratio