Signal Processing Framework for the Detection of Ventricular Ectopic Beat Episodes.

The Holter monitor captures the electrocardiogram (ECG) and detects abnormal episodes, but physicians still use manual cross-checking. It takes a considerable time to annotate a long-term ECG record. As a result, research continues to be conducted to produce an effective automatic cardiac episode detection technique that will reduce the manual burden. The current study presents a signal processing framework to detect ventricular ectopic beat (VEB) episodes in long-term ECG signals of cross-database. The proposed study has experimented with the cross-database of open-source and proprietary databases. The ECG signals were preprocessed and extracted the features such as pre-RR interval, post-RR interval, QRS complex duration, QR slope, and RS slope from each beat. In the proposed work, four models such as support vector machine, k-means nearest neighbor, nearest mean classifier, and nearest RMS (NRMS) classifiers were used to classify the data into normal and VEB episodes. Further, the trained models were used to predict the VEB episodes from the proprietary database. NRMS has reported better performance among four classification models. NRMS has shown the classification accuracy of 98.68% and F1-score of 94.12%, recall rate of 100%, specificity of 98.53%, and precision of 88.89% with an open-source database. In addition, it showed an accuracy of 99.97%, F1-score of 94.54%, recall rate of 98.62%, specificity of 99.98%, and precision of 90.79% to detect the VEB cardiac episodes from the proprietary database. Therefore, it is concluded that the proposed framework can be used in the automatic diagnosis system to detect VEB cardiac episodes.

Review of the Requirements for a Wearable, Low-Cost External Cardiac Loop Recorder with WBAN in Resource-Constrained Settings.

Cardiovascular disease (CVD) has become the most serious health concern in India and globally. The cost of treatment for CVD is very high and in a country like India, where most of the population belongs to rural area, affording treatment is not possible. Diagnosis and treatment are further hampered due to shortage of medical expertise as well as the unavailability of the wearable device. This makes the condition worst in rural areas. As a result of delay in diagnosis, patients do not receive appropriate treatment on time, thus risking lives. Hence, early detection of physiological abnormalities in patients is the best solution to avoid sudden death. In India, the majority of ECG diagnosis is done using a standard ECG machine or Holter monitor, which are not adequate to detect transient or infrequent arrhythmia as the window of detection is 30 s or up to 48 h. So, for arrhythmia diagnosis or syncope and palpitation, external cardiac loop recorder (ECLR) is preferred. ECLR is a monitoring device which records cardiac activities and detects infrequent arrhythmias with syncope and palpitation of a subject for longer period continuously. Due to recent improvements in technology, such as flexible electronics and wireless body area network (WBAN), wearable medical devices are progressively assisting people to monitor their health status while doing their day-to-day activities and furnishing more information to clinicians for early diagnosis and treatment. Flexible electronics allows to develop an electronic circuit on a flexible substrate hence making the device bendable and stretchable. WBAN is a wireless communication between different nodes like sensors and processors that are located at different points on the body. By incorporating technologies such as miniaturization of electronics, making flexible electronics and WBAN concept in ECLR, the device can be made wearable so as to not interfere with the patient's day-to-day activities. This review paper discusses the limitations of existing standard ECG machines as well as how to make the existing ECLR devices more robust, more advanced, more comfortable and also affordable.

A Computer-Aided Markov Random Field Segmentation Algorithm for Assessing Fetal Ventricular Chambers.

Congenital heart disease (CHD) is the most widely occurring congenital defect and accounts to about 28% of the overall congenital defects. Analysis of the development of the fetal heart thus plays an important role for detection of abnormality in early stages and to take corrective measures. Cardiac chamber analysis is one of the important diagnosing methods. Segmentation of the cardiac chambers must be done appropriately to avoid false interpretations. Effective segmentation of fetal ventricular chambers is a challenging task as the speckle noise inherent in ultrasound images cause blurring of the boundaries of anatomical structures. Several segmentation techniques have been proposed for extracting the fetal cardiac chambers. This article discusses the performance evaluation of automated, probability based segmentation approach, and Markov random field (MRF) for segmenting the fetal ventricular chambers of ultrasonic cineloop sequences. 837 ultrasonic biometery sequences of various gestations were collected from local diagnostic center after due ethical clearance and used for the study. In order to assess the efficiency of the segmentation technique, four metrics such as dice coefficient, true positive ratio (TPR), false positive ratio (FPR), similarity ratio (SIR), and precision (PR) were used. In order to perform ground truth validation, 56% of the data used in this study were annotated by clinical experts. The automated segmentation yielded comparable results with manual annotation. The technique results in average value of 0.68 for Dice coefficient, 0.723 for TPR, 0.604 for SIR, and 0.632 for PR.

External Cardiac Loop Recorders: Functionalities, Diagnostic Efficacy, Challenges and Opportunities.

According to the World Health Organization's (WHO) report of 2016, cardiovascular diseases (CVDs) accounted for mortality of an estimated 17.9 million people globally. Of these deaths, 85% were due to myocardial infarction and stroke. Further, the pre-dominant atrial fibrillation (AF) arrhythmia has been the most suspected underlying cause of transient ischemic attack or stroke. Thus, the etiologies of early symptoms like syncope and palpitations in patients needs to be evaluated by employing proper diagnostic tests to make early treatment decisions. The most widely referred 24 to 48 hour Holter electrocardiographic (ECG) monitoring tests have not been proved to be much effective in recognizing infrequent intermittent arrhythmic episodes. These drawbacks have led to the development of long-term ambulatory ECG (AECG) monitoring devices. This review reports the state-of-the-art existing AECG monitoring devices and their role of long-term ECG recording in patients suspected with cardiac syncope and palpitations to understand the underlying arrhythmic cause, as well as in the diagnosis and management of AF. Primarily, the utility and diagnostic yield of external cardiac recorders or event loop recorders (ELRs) in capturing the symptom-rhythm correlation which constitutes a clinically useful recordings of heart's electrical activity during infrequent arrhythmic conditions was critically reviewed. Furthermore, a brief case study on challenges involved in clinical data acquisition at a cardiac care unit using ambulatory external monitoring device has been presented. Finally, improvements in design engineering and algorithmic developments to enhance the diagnostic yield and usability of ELRs in clinical settings have been proposed.

Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction.

OBJECTIVE: In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS: Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS: Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS: We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE: To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.

EEG based multi-class seizure type classification using convolutional neural network and transfer learning.

Recognition of epileptic seizure type is essential for the neurosurgeon to understand the cortical connectivity of the brain. Though automated early recognition of seizures from normal electroencephalogram (EEG) was existing, no attempts have been made towards the classification of variants of seizures. Therefore, this study attempts to classify seven variants of seizures with non-seizure EEG through the application of convolutional neural networks (CNN) and transfer learning by making use of the Temple University Hospital EEG corpus. The objective of our study is to perform a multi-class classification of epileptic seizure type, which includes simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. The 19 channels EEG time series was converted into a spectrogram stack before feeding as input to CNN. The following two different modalities were proposed using CNN: (1) Transfer learning using pretrained network, (2) Extract image features using pretrained network and classify using the support vector machine classifier. The following ten pretrained networks were used to identify the optimal network for the proposed study: Alexnet, Vgg16, Vgg19, Squeezenet, Googlenet, Inceptionv3, Densenet201, Resnet18, Resnet50, and Resnet101. The highest classification accuracy of 82.85% (using Googlenet) and 88.30% (using Inceptionv3) was achieved using transfer learning and extract image features approach respectively. Comparison results showed that CNN based approach outperformed conventional feature and clustering based approaches. It can be concluded that the EEG based classification of seizure type using CNN model could be used in pre-surgical evaluation for treating patients with epilepsy.

Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurrent and unpredictable interruption of healthy brain function. Diagnosis of patients with epilepsy requires monitoring and visual inspection of long-term EEG by the neurologist, which is found to be a time-consuming procedure. Therefore, this study proposes an automated seizure detection model using a novel computationally efficient feature named sigmoid entropy derived from discrete wavelet transforms. The sigmoid entropy was estimated from the wavelet coefficients in each sub-band and classified using a non-linear support vector machine classifier with leave-one-subject-out cross-validation. The performance of the proposed method was tested with the Ramaiah Medical College and Hospital (RMCH) database, which consists of the 58 Hours of EEG from 115 subjects, the University of Bonn (UBonn), and CHB-MIT databases. Results showed that sigmoid entropy exhibits lower values for epileptic EEG in contrary to other existing entropy methods. We observe a seizure detection rate of 96.34%, a false detection rate of 0.5/h and a mean detection delay of 1.2 s for the RMCH database. The highest sensitivity of 100% and 94.21% were achieved for UBonn and CHB-MIT databases respectively. The performance comparison confirms that sigmoid entropy was found to be better and computationally efficient as compared to other entropy methods. It can be concluded that the proposed sigmoid entropy could be used as a potential biomarker for recognition and detection of epileptic seizures.

A convolutional neural network based framework for classification of seizure types.

Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to classify EEG as seizure and non-seizures, but the classification of seizure types has not been explored. Thus, in this paper, we have proposed the 8-class classification problem in order to classify different seizure types using convolutional neural networks (CNN). This research study suggests a CNN based framework for classification of epileptic seizure types that include simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. EEG time series was converted into spectrogram stacks and used as input for CNN. To the best of authors knowledge, ours is the very first study that classified the seizures types using the computational algorithm. The four CNN models, namely AlexNet, VGG16, VGG19, and basic CNN model was applied to study the performance of 8-class classification problem. The proposed study showed a classification accuracy of 84.06%, 79.71%, 76.81%, and 82.14% using AlexNet, VGG16, VGG19 and basic CNN models respectively. The experimental results suggest that the proposed framework could be helpful to the neurology community for recognition of seizures types.

A Novel Approach for Real-Time Recognition of Epileptic Seizures Using Minimum Variance Modified Fuzzy Entropy.

OBJECTIVE: Validation of epileptic seizures annotations from long-term electroencephalogram (EEG) recordings is a tough and tedious task for the neurological community. It is a well-known fact that computerized qualitative methods thoroughly assess the complex brain dynamics toward seizure detection and proven as one of the acceptable clinical indicators. METHODS: This research study suggests a novel approach for real-time recognition of epileptic seizure from EEG recordings by a technique referred as minimum variance modified fuzzy entropy (MVMFzEn). Multichannel EEG recordings of 4.36 h of epileptic seizures and 25.74 h of normal EEG were considered. Signal processing techniques such as filters and independent component analysis were appropriated to reduce noise and artifacts. Unlike, the predefined fuzzy membership function, the modified fuzzy entropy utilizes relative energy as a membership function followed by scaling operation to obtain the feature. RESULTS: Results revealed that MVMFzEn drops abruptly during an epileptic activity and this fact was used to set a threshold. An automated threshold derived from MVMFzEn assesses the classification efficiency of the given data during validation. It was observed from the results that the proposed method yields a classification accuracy of 100% without the use of any classifier. CONCLUSION: The graphical user interface was designed in MATLAB to automatically label the normal and epileptic segments in the long-term EEG recordings. SIGNIFICANCE: The ground truth clinical validation using validation specificity and validation sensitivity confirms the suitability of the proposed technique for automated annotation of epileptic seizures in real time.

Multi-feature characterization of epileptic activity for construction of an automated internet-based annotated classification.

Continuous monitoring of EEG is essential for the neurologist to detect the epileptic seizures that occur at various intervals. Since large volume of data need to be analyzed, visual analysis has been proven to be time consuming and subsequently automated detection techniques have gained importance in the recent years. For the biomedical research community, the major challenge lies in providing a solution to neurologists in terms of diagnosis and EEG database management. This paper discusses the automated detection of epileptic seizure using frequency domain and entropy parameters which helps in the construction of epileptic database for handling EEG data. Experimental study indicates that the suggested mode of operation can be used for internet based framework which contains pure epileptic patterns in the server. This can be retrieved and analyzed for detection and annotation of epileptic spikes in extensive EEG recordings.

Approximate entropy-based epileptic EEG detection using artificial neural networks.

The electroencephalogram (EEG) signal plays an important role in the diagnosis of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a time-consuming analysis of the entire length of the EEG data by an expert. The traditional methods of analysis being tedious, many automated diagnostic systems for epilepsy have emerged in recent years. This paper proposes a neural-network-based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that the value of the ApEn drops sharply during an epileptic seizure and this fact is used in the proposed system. Two different types of neural networks, namely, Elman and probabilistic neural networks, are considered in this paper. ApEn is used for the first time in the proposed system for the detection of epilepsy using neural networks. It is shown that the overall accuracy values as high as 100% can be achieved by using the proposed system.