Trends in extreme learning machines

Artificial intelligence (AI) has transformed key aspects of human life. Machine learning (ML), which is a subset of AI wherein machines autonomously acquire information by extracting patterns from large databases, has been increasingly used within the medical community, and specifically within the domain of cardiovascular diseases. In this review, we present a brief overview of ML methodologies that are used for the construction of inferential and predictive data-driven models. We highlight several domains of ML application such as echocardiography, electrocardiography, and recently developed non-invasive imaging modalities such as coronary artery calcium scoring and coronary computed tomography angiography. We conclude by reviewing the limitations associated with contemporary application of ML algorithms within the cardiovascular disease field.

Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging

Recently, a novel learning algorithm for single-hidden-layer feedforward neural networks (SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The essence of ELM is that the learning parameters of hidden nodes, including input weights and biases, are randomly assigned and need not be tuned while the output weights can be analytically determined by the simple generalized inverse operation. The only parameter needed to be defined is the number of hidden nodes. Compared with other traditional learning algorithms for SLFNs, ELM provides extremely faster learning speed, better generalization performance and with least human intervention. This paper firstly introduces a brief review of ELM, describing the principle and algorithm of ELM. Then, we put emphasis on the improved methods or the typical variants of ELM, especially on incremental ELM, pruning ELM, error-minimized ELM, two-stage ELM, online sequential ELM, evolutionary ELM, voting-based ELM, ordinal ELM, fully complex ELM, and symmetric ELM. Next, the paper summarized the applications of ELM on classification, regression, function approximation, pattern recognition, forecasting and diagnosis, and so on. In the last, the paper discussed several open issues of ELM, which may be worthy of exploring in the future.

Extreme learning machine and its applications

With the increase of an ageing population and chronic diseases, society becomes more health conscious and patients become "health consumers" looking for better health management. People's perception is shifting towards patient-centered, rather than the classical, hospital-centered health services which has been propelling the evolution of telemedicine research from the classic e-Health to m-Health and now is to ubiquitous healthcare (u-Health). It is expected that mobile & ubiquitous Telemedicine, integrated with Wireless Body Area Network (WBAN), have a great potential in fostering the provision of next-generation u-Health. Despite the recent efforts and achievements, current u-Health proposed solutions still suffer from shortcomings hampering their adoption today. This paper presents a comprehensive review of up-to-date requirements in hardware, communication, and computing for next-generation u-Health systems. It compares new technological and technical trends and discusses how they address expected u-Health requirements. A thorough survey on various worldwide recent system implementations is presented in an attempt to identify shortcomings in state-of-the art solutions. In particular, challenges in WBAN and ubiquitous computing were emphasized. The purpose of this survey is not only to help beginners with a holistic approach toward understanding u-Health systems but also present to researchers new technological trends and design challenges they have to cope with, while designing such systems.

u-Healthcare system: state-of-the-art review and challenges.

The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart1. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event, 1-year all-cause mortality, from ECG voltage–time traces. By using ECGs collected over a 34-year period in a large regional health system, we trained a DNN with 1,169,662 12-lead resting ECGs obtained from 253,397 patients, in which 99,371 events occurred. The model achieved an area under the curve (AUC) of 0.88 on a held-out test set of 168,914 patients, in which 14,207 events occurred. Even within the large subset of patients (n = 45,285) with ECGs interpreted as ‘normal’ by a physician, the performance of the model in predicting 1-year mortality remained high (AUC = 0.85). A blinded survey of cardiologists demonstrated that many of the discriminating features of these normal ECGs were not apparent to expert reviewers. Finally, a Cox proportional-hazard model revealed a hazard ratio of 9.5 (P < 0.005) for the two predicted groups (dead versus alive 1 year after ECG) over a 25-year follow-up period. These results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians. By using data from electrocardiograms, a deep learning algorithm outperforms traditional risk scores in predicting death over the course of the next year and identifies at-risk individuals with seemingly normal electrocardiograms.

Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network

ECG Feature Extraction plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. This feature extraction scheme determines the amplitudes and intervals in the ECG signal for subsequent analysis. The amplitudes and intervals value of P-QRS-T segment determines the functioning of heart of every human. Recently, numerous research and techniques have been developed for analyzing the ECG signal. The proposed schemes were mostly based on Fuzzy Logic Methods, Artificial Neural Networks (ANN), Genetic Algorithm (GA), Support Vector Machines (SVM), and other Signal Analysis techniques. All these techniques and algorithms have their advantages and limitations. This proposed paper discusses various techniques and transformations proposed earlier in literature for extracting feature from an ECG signal. In addition this paper also provides a comparative study of various methods proposed by researchers in extracting the feature from ECG signal.

ECG Feature Extraction Techniques - A Survey Approach

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem, the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper, a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) that is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, and they are the k-nearest Neighbor Classifier and the radial basis function neural network classifier, with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared with traditional classifiers.

Classification of electrocardiogram signals with support vector machines and extreme learning machine

An Electrocardiogram or ECG is an electrical recording of the heart and is used in the investigation of heart disease. This ECG can be classified as normal and abnormal signals. The classification of the ECG signals is presently performed with the support vector machine. The generalization performance of the SVM classifier is not sufficient for the correct classification of ECG signals. To overcome this problem the ELM classifier is used which works by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. The experiments were conducted on the ECG data from the Physionet arrhythmia database to classify five kinds of abnormal waveforms and normal beats. In this paper a thorough experimental study was done to show the superiority of the generalization capability of the Extreme Learning Machine (ELM) is presented and compared with support vector machine (SVM) approach in the automatic classification of ECG beats. In particular, the sensitivity of the ELM classifier is tested and that is compared with SVM combined with two classifiers, they are the k-nearest neighbor classifier (kNN) and the radial basis function neural network classifier (RBF), with respect to the curse of dimensionality and the number of available training beats. The obtained results clearly confirm the superiority of the ELM approach as compared to traditional classifiers.

/pdf/classification-of-ecg-signals-using-extreme-learning-machine-4zv1bk1q79.pdf

Classification of ECG Signals Using Extreme Learning Machine

Retinal exudates classification and identification of diabetic retinopathy to diagnose the eyes using fundus images requires automation. This research work proposes retinal exudates classification. Approach: Representative features are obtained from the fundus images using contextual clustering (CC) segmentation methods. The number of features obtained is two. The radial basis function (RBF) network is trained by the features. Final weights are obtained and subsequently used for testing. Results: The presence of exudates is identified more clearly as the CC uses neighbourhood information. By knowing the outputs of RBF during testing, accurate diagnosis and prescription for treatment of the affected eyes can be done. One hundred fundus images are used for testing. The performance of RBF is 96%(48 images are classified). Conclusion: Simulation results show the effectiveness of RBF in retinopathy classification. Very large database can be created from the fundus images collected from the diabetic retinopathy patients that can be used for future work

/pdf/detection-of-diabetic-retinopathy-using-radial-basis-2b0id0m94i.pdf

Detection of diabetic retinopathy using radial basis function

Growth of Spirulina sp. (MCRC-A0003), a cyanobacterium, was evaluated under different concentrations of carbon-dioxide (CO2) (4–50 %) in a closed glass photobioreactor. Although significant CO2 utilization by the cyanobacterial strain was observed up to 50 % concentration, complete utilization was observed only at 4, 10 and 20 % concentrations on 3rd, 6th and 8th day respectively. However, considerable reduction was witnessed in reactors containing 30–50 % CO2 only between 6th and 9th day. A corresponding increase in the biomass and primary metabolites like chlorophyll-a, carbohydrate and protein were observed. Biomass productivity of Spirulina in reactors sparged with 4, 10 and 20 % CO2 were 13.7, 43 and 44 % more than that in control reactor without CO2. While CO2 increased the levels of primary metabolites in the cyanobacterial cells, it was quite prominent in 10 % CO2 concentration with the chlorophyll-a, carbohydrate and protein contents were 64, 183 and 626 mg g−1 respectively. While 10 and 6.6 % increase were noticed in chlorophyll-a and protein, 17 % increase in carbohydrate levels was observed in Spirulina cells, which could be attributed to the conversion of CO2 to carbohydrate by the cyanobacterium.

M. Sivakumar

Papers

ECG Feature Extraction Techniques - A Survey Approach

Classification of electrocardiogram signals with support vector machines and extreme learning machine

Classification of ECG Signals Using Extreme Learning Machine

Detection of diabetic retinopathy using radial basis function

Influence of carbon-dioxide on the growth of Spirulina sp. (MCRC-A0003) isolated from Muttukadu backwaters, South India.