AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.

Reseek-Arrhythmia: Empirical Evaluation of ResNet Architecture for Detection of Arrhythmia

Arrhythmia is a cardiac condition characterized by an irregular heart rhythm that hinders the proper circulation of blood, posing a severe risk to individuals' lives. Globally, arrhythmias are recognized as a significant health concern, accounting for nearly 12 percent of all deaths. As a result, there has been a growing focus on utilizing artificial intelligence for the detection and classification of abnormal heartbeats. In recent years, self-operated heartbeat detection research has gained popularity due to its cost-effectiveness and potential for expediting therapy for individuals at risk of arrhythmias. However, building an efficient automatic heartbeat monitoring approach for arrhythmia identification and classification comes with several significant challenges. These challenges include addressing issues related to data quality, determining the range for heart rate segmentation, managing data imbalance difficulties, handling intra- and inter-patient variations, distinguishing supraventricular irregular heartbeats from regular heartbeats, and ensuring model interpretability. In this study, we propose the Reseek-Arrhythmia model, which leverages deep learning techniques to automatically detect and classify heart arrhythmia diseases. The model combines different convolutional blocks and identity blocks, along with essential components such as convolution layers, batch normalization layers, and activation layers. To train and evaluate the model, we utilized the MIT-BIH and PTB datasets. Remarkably, the proposed model achieves outstanding performance with an accuracy of 99.35% and 93.50% and an acceptable loss of 0.688 and 0.2564, respectively.

Utilizing variable auto encoder-based TDO optimization algorithm for predicting loneliness from electrocardiogram signals

Several seniors and a substantial part of the general population are living in social isolation. This frequently occurs in vulnerability, isolation, and depression, which then have a poor impact on other health-related factors. A number of health problems, including a higher risk of cardio problems, are brought on by social isolation and loneliness. Electrocardiogram (ECG) usage for mental condition recognition enables accurate determination of a person's internal representation. The electrocardiogram (ECG) signals can be thoroughly analyzed to uncover hidden data that may be helpful for the precise identification of cardiac problems. ECG time-series information typically have great dimensions and complicated componentry. Using relevant information to guide training is among the main achievements of this type of learning. An ECG signal plays a significant part in the individual body's ability to manage behavior. Furthermore, loneliness identification is crucial since it has the worse effect on the circumstances that afflict persons. This study suggested an approach for detecting loneliness from an ECG signal to use a variable auto encoder-based optimization algorithm for ESN technique. The suggested approach consists of three phases for identifying a person's loneliness. Firstly, undecimated discrete wavelet transform is used to preprocess the acquired ECG data. Next, further characteristics are extracted from the precompiled signals using a variable auto encoder. For the precise categorization of loneliness in the ECG signal, a metaheuristic optimized ESN is, therefore, presented. The outcomes of the tests demonstrate that the suggested system with suitable ECG representations produces improved accuracy as well as performance.

Electrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction

An electrocardiogram (ECG) is a basic and quick test for evaluating cardiac disorders and is crucial for remote patient monitoring equipment. An accurate ECG signal classification is critical for real-time measurement, analysis, archiving, and transmission of clinical data. Numerous studies have focused on accurate heartbeat classification, and deep neural networks have been suggested for better accuracy and simplicity. We investigated a new model for ECG heartbeat classification and found that it surpasses state-of-the-art models, achieving remarkable accuracy scores of 98.5% on the Physionet MIT-BIH dataset and 98.28% on the PTB database. Furthermore, our model achieves an impressive F1-score of approximately 86.71%, outperforming other models, such as MINA, CRNN, and EXpertRF on the PhysioNet Challenge 2017 dataset.

A Literature Review: ECG-Based Models for Arrhythmia Diagnosis Using Artificial Intelligence Techniques

In the health care and medical domain, it has been proven challenging to diagnose correctly many diseases with complicated and interferential symptoms, including arrhythmia. However, with the evolution of artificial intelligence (AI) techniques, the diagnosis and prognosis of arrhythmia became easier for the physicians and practitioners using only an electrocardiogram (ECG) examination. This review presents a synthesis of the studies conducted in the last 12 years to predict arrhythmia's occurrence by classifying automatically different heartbeat rhythms. From a variety of research academic databases, 40 studies were selected to analyze, among which 29 of them applied deep learning methods (72.5%), 9 of them addressed the problem with machine learning methods (22.5%), and 2 of them combined both deep learning and machine learning to predict arrhythmia (5%). Indeed, the use of AI for arrhythmia diagnosis is emerging in literature, although there are some challenging issues, such as the explicability of the Deep Learning methods and the computational resources needed to achieve high performance. However, with the continuous development of cloud platforms and quantum calculation for AI, we can achieve a breakthrough in arrhythmia diagnosis.

A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.

ECG-based real-time arrhythmia monitoring using quantized deep neural networks: A feasibility study

Continuous ambulatory cardiac monitoring plays a critical role in early detection of abnormality in at-risk patients, thereby increasing the chance of early intervention. In this study, we present an automated ECG classification approach for distinguishing between healthy heartbeats and pathological rhythms. The proposed lightweight solution uses quantized one-dimensional deep convolutional neural networks and is ideal for real-time continuous monitoring of cardiac rhythm, capable of providing one output prediction per second. Raw ECG data is used as the input to the classifier, eliminating the need for complex data preprocessing on low-powered wearable devices. In contrast to many compute-intensive approaches, the data analysis can be carried out locally on edge devices, providing privacy and portability. The proposed lightweight solution is accurate (sensitivity of 98.5% and specificity of 99.8%), and implemented on a smartphone, it is energy-efficient and fast, requiring 5.85 mJ and 7.65 ms per prediction, respectively.

[An arrhythmia classification method based on deep learning parallel network model]

OBJECTIVE

We propose a parallel neural network classification method to improve the performance of classification of 4 types of arrhythmias: normal beat, supraventricular ectopic beat, ventricular ectopic beat and fused beat.

METHODS

Preprocessing was performed including denoising of ECG signal, segmentation of small-scale heartbeat and large-scale heartbeat and data enhancement. Based on deep learning theory, densely connected convolutional network was applied to improve the limitation of waveform feature extraction, and bidirectional long short-term memory network and efficient channel attention network were combined to enhance the function of time series features and important features of the waveform. The parallel network structure was adopted, and the waveform features of small- scale heartbeat and large-scale heartbeat were input to improve the accuracy of arrhythmia classification at the same time. Softmax was used to carry out the 4 classification tasks of arrhythmia by the parallel network model.

RESULTS

The proposed method was verified using MIT-BIH Arrhythmia Database and 3 groups of experiments. The experiments for comparing the classification performance of multiple parallel network models and that of each classification model under different heartbeat input methods showed that the proposed classification model had an overall accuracy, average sensitivity and average specificity of 99.36%, 96.08% and 99.41%, respectively. Convergence performance analysis of the parallel network classification model showed that the training time of the classification model was 41 s.

CONCLUSION

The parallel multi-network classification method can improve the average sensitivity, specificity and training time while maintaining a high overall accuracy, and may thus provide a new technical solution for clinical diagnosis of arrhythmia.

A deep learning algorithm to translate and classify cardiac electrophysiology

The development of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) has been a critical in vitro advance in the study of patient-specific physiology, pathophysiology, and pharmacology. We designed a new deep learning multitask network approach intended to address the low throughput, high variability, and immature phenotype of the iPSC-CM platform. The rationale for combining translation and classification tasks is because the most likely application of the deep learning technology we describe here is to translate iPSC-CMs following application of a perturbation. The deep learning network was trained using simulated action potential (AP) data and applied to classify cells into the drug-free and drugged categories and to predict the impact of electrophysiological perturbation across the continuum of aging from the immature iPSC-CMs to the adult ventricular myocytes. The phase of the AP extremely sensitive to perturbation due to a steep rise of the membrane resistance was found to contain the key information required for successful network multitasking. We also demonstrated successful translation of both experimental and simulated iPSC-CM AP data validating our network by prediction of experimental drug-induced effects on adult cardiomyocyte APs by the latter.

Epileptic Seizures Detection Using Deep Learning Techniques: A Review

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

Diagnostic Interpretation of Non-Uniformly Sampled Electrocardiogram

We present a set of three fundamental methods for electrocardiogram (ECG) diagnostic interpretation adapted to process non-uniformly sampled signal. The growing volume of ECGs recorded daily all over the world (roughly estimated to be 600 TB) and the expectance of long persistence of these data (on the order of 40 years) motivated us to challenge the feasibility of medical-grade diagnostics directly based on arbitrary non-uniform (i.e., storage-efficient) ECG representation. We used a refined time-independent QRS detection method based on a moving shape matching technique. We applied a graph data representation to quantify the similarity of asynchronously sampled heartbeats. Finally, we applied a correlation-based non-uniform to time-scale transform to get a multiresolution ECG representation on a regular dyadic grid and to find precise P, QRS and T wave delimitation points. The whole processing chain was implemented and tested with MIT-BIH Database (probably the most referenced cardiac database) and CSE Multilead Database (used for conformance testing of medical instruments) signals arbitrarily sampled accordingly to a perceptual model (set for variable sampling frequency of 100-500 Hz, compression ratio 3.1). The QRS detection shows an accuracy of 99.93% with false detection ratio of only 0.18%. The classification shows an accuracy of 99.27% for 14 most frequent MIT-BIH beat types and 99.37% according to AAMI beat labels. The wave delineation shows cumulative (i.e., sampling model and non-uniform processing) errors of: 9.7 ms for P wave duration, 3.4 ms for QRS, 6.7 ms for P-Q segment and 17.7 ms for Q-T segment, all the values being acceptable for medical-grade interpretive software.

ECG Language processing (ELP): A new technique to analyze ECG signals

BACKGROUND

A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, the Electrocardiogram (ECG) signal, the most common noninvasive tool to study the functionality of the heart and diagnose several abnormal arrhythmias, is made up of sequences of three or four distinct waves, including the P-wave, QRS complex, T-wave, and U-wave. An ECG signal may contain several different varieties of each wave (e.g., the QRS complex can have various appearances). For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies.

METHODS

Analogous to natural language processing (NLP), which is used to help computers understand and interpret the human's natural language, it is possible to develop methods inspired by NLP to aid computers to gain a deeper understanding of Electrocardiogram signals. In this work, our goal is to propose a novel ECG analysis technique, ECG language processing (ELP), focusing on empowering computers to understand ECG signals in a way physicians do.

RESULTS

We evaluated the proposed approach on two tasks, including the classification of heartbeats and the detection of atrial fibrillation in the ECG signals. Overall, our technique resulted in better performance or comparable performance with smaller neural networks compared to other deep neural networks and existing algorithms.

CONCLUSION

Experimental results on three databases (i.e., PhysioNet's MIT-BIH, MIT-BIH AFIB, and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that the proposed approach as a general idea can be applied to a variety of biomedical applications and can achieve remarkable performance.

A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network

Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.

Wavelet Scattering Transform for ECG Beat Classification

An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k = 4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

Arrhythmia Classification using Deep Learning and Machine Learning with Features Extracted from Waveform-based Signal Processing

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When performing WBSP, we first filtered ECG signals, searched local minima, and removed baseline wandering. Subsequently, we fit the processed ECG signals with Gaussians and extracted the parameters. Afterwards, we exploited the products of WBSP to accomplish arrhythmia classification with our proposed machine learning-based and deep learning-based classifiers. We utilized MIT-BIH Arrhythmia Database to validate WBSP. Our best classifier achieved 98.8% accuracy. Moreover, it reached 96.3% sensitivity in class V and 98.6% sensitivity in class Q, which both share one of the best among the related works. In addition, our machine learning-based classifier accomplished identifying four waveform components essential for automated arrhythmia classification: the similarity of QRS complex to a Gaussian curve, the sharpness of the QRS complex, the duration of and the area enclosed by P-wave.Clinical relevance- Early diagnosis and automated classification of arrhythmia is clinically essential.

A Review of Atrial Fibrillation Detection Methods as a Service

Atrial Fibrillation (AF) is a common heart arrhythmia that often goes undetected, and even if it is detected, managing the condition may be challenging. In this paper, we review how the RR interval and Electrocardiogram (ECG) signals, incorporated into a monitoring system, can be useful to track AF events. Were such an automated system to be implemented, it could be used to help manage AF and thereby reduce patient morbidity and mortality. The main impetus behind the idea of developing a service is that a greater data volume analyzed can lead to better patient outcomes. Based on the literature review, which we present herein, we introduce the methods that can be used to detect AF efficiently and automatically via the RR interval and ECG signals. A cardiovascular disease monitoring service that incorporates one or multiple of these detection methods could extend event observation to all times, and could therefore become useful to establish any AF occurrence. The development of an automated and efficient method that monitors AF in real time would likely become a key component for meeting public health goals regarding the reduction of fatalities caused by the disease. Yet, at present, significant technological and regulatory obstacles remain, which prevent the development of any proposed system. Establishment of the scientific foundation for monitoring is important to provide effective service to patients and healthcare professionals.

Automated pre-screening of arrhythmia using hybrid combination of Fourier-Bessel expansion and LSTM

Health care in developing countries demands systems-based screening solutions. In view of this, we present a new rhythm-based methodology for the point-of-care diagnosis of cardiac arrhythmia at a primary level. Such a system will reduce the workload of cardiologists significantly. The method begins by computing the RR-interval sequences from the electrocardiogram(ECG) signals. Then, the Fourier-Bessel (FB) expansion is used to obtain the intelligent series by converting the RR-interval sequences into more meaningful sequences that can characterize the underlying pathology of cardiac arrhythmia with a unique pattern. Ultimately, the obtained intelligent series are used as input to train the long short-term memory (LSTM) model for ECG classification. We have obtained an accuracy of 90.07% in classifying normal and the arrhythmia classes using MIT-BIH database. The results demonstrate that the proposed intelligent series can reveal remarkable differences between the normal and arrhythmia ECG signals. Thus, the proposed algorithm can be used as a primary screening tool for detecting cardiac arrhythmia. Potentially, the developed system can be used by paramedics in rural outreach programs with limited funding and expertise. Moreover, the use of single-lead and short-length ECG signals in the proposed system makes it a suitable candidate for applications that are intended for mobile and other hand-held or wearable devices.

A novel hybrid approach for automated detection of retinal detachment using ultrasound images

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.

A Lightweight Deep Learning Model for Fast Electrocardiographic Beats Classification With a Wearable Cardiac Monitor: Development and Validation Study

Background

Electrocardiographic (ECG) monitors have been widely used for diagnosing cardiac arrhythmias for decades. However, accurate analysis of ECG signals is difficult and time-consuming work because large amounts of beats need to be inspected. In order to enhance ECG beat classification, machine learning and deep learning methods have been studied. However, existing studies have limitations in model rigidity, model complexity, and inference speed.

Objective

To classify ECG beats effectively and efficiently, we propose a baseline model with recurrent neural networks (RNNs). Furthermore, we also propose a lightweight model with fused RNN for speeding up the prediction time on central processing units (CPUs).

Methods

We used 48 ECGs from the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database, and 76 ECGs were collected with S-Patch devices developed by Samsung SDS. We developed both baseline and lightweight models on the MXNet framework. We trained both models on graphics processing units and measured both models’ inference times on CPUs.

Results

Our models achieved overall beat classification accuracies of 99.72% for the baseline model with RNN and 99.80% for the lightweight model with fused RNN. Moreover, our lightweight model reduced the inference time on CPUs without any loss of accuracy. The inference time for the lightweight model for 24-hour ECGs was 3 minutes, which is 5 times faster than the baseline model.

Conclusions

Both our baseline and lightweight models achieved cardiologist-level accuracies. Furthermore, our lightweight model is competitive on CPU-based wearable hardware.

Towards Real-Time Heartbeat Classification: Evaluation of Nonlinear Morphological Features and Voting Method

Abnormal heart rhythms are one of the significant health concerns worldwide. The current state-of-the-art to recognize and classify abnormal heartbeats is manually performed by visual inspection by an expert practitioner. This is not just a tedious task; it is also error prone and, because it is performed, post-recordings may add unnecessary delay to the care. The real key to the fight to cardiac diseases is real-time detection that triggers prompt action. The biggest hurdle to real-time detection is represented by the rare occurrences of abnormal heartbeats and even more are some rare typologies that are not fully represented in signal datasets; the latter is what makes it difficult for doctors and algorithms to recognize them. This work presents an automated heartbeat classification based on nonlinear morphological features and a voting scheme suitable for rare heartbeat morphologies. Although the algorithm is designed and tested on a computer, it is intended ultimately to run on a portable i.e., field-programmable gate array (FPGA) devices. Our algorithm tested on Massachusetts Institute of Technology- Beth Israel Hospital(MIT-BIH) database as per Association for the Advancement of Medical Instrumentation(AAMI) recommendations. The simulation results show the superiority of the proposed method, especially in predicting minority groups: the fusion and unknown classes with 90.4% and 100%.

A review on the pattern detection methods for epilepsy seizure detection from EEG signals

Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.

Automated detection of glaucoma using optical coherence tomography angiogram images

Glaucoma is a malady that occurs due to the buildup of fluid pressure in the inner eye. Detection of glaucoma at an early stage is crucial as by 2040, 111.8 million people are expected to be afflicted with glaucoma globally. Feature extraction methods prove to be promising in the diagnosis of glaucoma. In this study, we have used optical coherence tomography angiogram (OCTA) images for automated glaucoma detection. Ocular sinister (OS) from the left eye while ocular dexter (OD) were obtained from right eye of subjects. We have used OS macular, OS disc, OD macular and OD disc images. In this work, local phase quantization (LPQ) technique was applied to extract the features. Information fusion and principal component analysis (PCA) are used to combine and reduce the features. Our method achieved the highest accuracy of 94.3% using LPQ coupled with PCA for right eye optic disc images with AdaBoost classifier. The proposed technique can aid clinicians in glaucoma detection at an early stage. The developed model is ready to be tested with more images before deploying for clinical application.

A new approach for arrhythmia classification using deep coded features and LSTM networks

BACKGROUND AND OBJECTIVE

For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

METHODS

A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

RESULTS

Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

CONCLUSIONS

One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

A NOVEL TWO-LEAD ARRHYTHMIA CLASSIFICATION SYSTEM BASED ON CNN AND LSTM

Arrhythmia classification is useful during heart disease diagnosis. Although well-established for intra-patient diagnoses, inter-patient arrhythmia classification remains difficult. Most previous work has focused on the intra-patient condition and has not followed the Association for the Advancement of Medical Instrumentation (AAMI) standards. Here, we propose a novel system for arrhythmia classification based on multi-lead electrocardiogram (ECG) signals. The core of the design is that we fuse two types of deep learning features with some common traditional features and select discriminating features using a binary particle swarm optimization algorithm (BPSO). Then, the feature vector is classified using a weighted support vector machine (SVM) classifier. For a better generalization of the model and to draw fair comparisons, we carried out inter-patient experiments and followed the AAMI standards. We found that, when using common metrics aimed at multi-classification either macro- or micro-averaging, our system outperforms most other state-of-the-art methods.

ECG BEAT DETECTION AND CLASSIFICATION SYSTEM USING WAVELET TRANSFORM AND ONLINE SEQUENTIAL ELM

Electrocardiogram (ECG) signals consist of data containing measurements of electrical activity in the heartbeats. These signals include relevant information used to detect abnormalities such as arrhythmia. In this study, a recognition system is proposed for detection and classification of heartbeats in ECG signals. Heartbeats in the ECG data were detected by using the wavelet transform (WT) method and these beats are segmented with determined periods. For obtaining distinctive features from the beats, multi-resolution WT is applied to these segmented signals, and wavelet coefficients are obtained from different frequency levels. Feature vectors are generated on these coefficients by using various statistical methods. The proposed recognition system is trained on feature vectors by using the Online Sequential Extreme Learning Machine (OSELM) classifier during the learning phase to automatically recognize the signals. Five different beat types were obtained from the MIT-BIH arrhythmia dataset. The multi-class dataset that includes five classes and the binary-class dataset that includes two classes were created among these beat types. Performance tests of the proposed wavelet-based-OSELM (W-OSELM) method were realized with these two datasets. The proposed recognition system provided 97.29% correct beat detection rate from raw ECG signals. The classification accuracy is 99.44% for the binary-class dataset and 98.51% for the multi-class dataset. Furthermore, the proposed classifier has shown very fast recognition performance on ECG signals.

An Efficient and Automatic ECG Arrhythmia Diagnosis System using DWT and HOS Features and Entropy- Based Feature Selection Procedure

Primary recognition of heart diseases by exploiting computer aided diagnosis (CAD) machines, decreases the vast rate of fatality among cardiac patients. Recognition of heart abnormalities is a staggering task because the low changes in ECG signals may not be exactly specified with eyesight. In this paper, an efficient approach for ECG arrhythmia diagnosis is proposed based on a combination of discrete wavelet transform and higher order statistics feature extraction and entropy based feature selection methods. Using the neural network and support vector machine, five classes of heartbeat categories are classified. Applying the neural network and support vector machine method, our proposed system is able to classify the arrhythmia classes with high accuracy (99.83%) and (99.03%), respectively. The advantage of the presented procedure has been experimentally demonstrated compared to the other recently presented methods in terms of accuracy.

Epilepsy detection from EEG signals: a review

Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.

Real-Time Cardiac Arrhythmia Classification Using Memristor Neuromorphic Computing System

Cardiac arrhythmia is known to be one of the most common causes of death worldwide. Therefore, development of efficient arrhythmia detection techniques is essential to save patients' lives. In this paper, we introduce a new real-time cardiac arrhythmia classification using memristor neuromorphic computing system for classification of 5 different beat types. Neuromorphic computing systems utilize new emerging devices, such as memristors, as a basic building block. Hence, these systems provide excellent trade-off between real-time processing, power consumption, and overall accuracy. Experimental results showed that the proposed system outperforms most of the methods in comparison in terms of accuracy and testing time, since it achieved 96.17% average accuracy and 34 ms average testing time per beat.

Automated characterization of cardiovascular diseases using relative wavelet nonlinear features extracted from ECG signals

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. The rising mortality rate can be reduced by early detection and treatment interventions. Clinically, electrocardiogram (ECG) signal provides useful information about the cardiac abnormalities and hence employed as a diagnostic modality for the detection of various CVDs. However, subtle changes in these time series indicate a particular disease. Therefore, it may be monotonous, time-consuming and stressful to inspect these ECG beats manually. In order to overcome this limitation of manual ECG signal analysis, this paper uses a novel discrete wavelet transform (DWT) method combined with nonlinear features for automated characterization of CVDs. ECG signals of normal, and dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI) are subjected to five levels of DWT. Relative wavelet of four nonlinear features such as fuzzy entropy, sample entropy, fractal dimension and signal energy are extracted from the DWT coefficients. These features are fed to sequential forward selection (SFS) technique and then ranked using ReliefF method. Our proposed methodology achieved maximum classification accuracy (acc) of 99.27%, sensitivity (sen) of 99.74%, and specificity (spec) of 98.08% with K-nearest neighbor (kNN) classifier using 15 features ranked by the ReliefF method. Our proposed methodology can be used by clinical staff to make faster and accurate diagnosis of CVDs. Thus, the chances of survival can be significantly increased by early detection and treatment of CVDs.

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems.