Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network (original) (raw)
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A Model For Electrocardiogram (ECG) Classification Using Convolutional Neural Network (CNN
IJARCCE, 2021
Electrocardiogram (ECG) is an intermittent sign, which mirrors the movement of the heart. From ECG a great deal data is gotten for typical and obsessive physiology of heart. The ECG signal is non-fixed in nature and extremely hard to dissect. Clinical perception takes long time and the sign is non-fixed. This paper presents a convolutional neural network algorithm for electrocardiogram signal classification. This system uses a ECG dataset which was downloaded from kaggle.com. The dataset signals were preprocessed to make sure that each segment conforms to a heartbeat. This dataset was read into the jupyter notebook by using pandas.read_csv function. The dataset was made into two, which are the training data and the testing data. After successful reading of the data signal from directory and solving of the in balance problem by means of data augmentation, the proposed model was trained using a convolutional neural network(CNN) algorithm with a total hidden layers of six, hiden size of 128, batch_size of 96, and number of epoch to be 10. After successful training, we had an accuracy of 99% at an epoch level of 10. 1. INTRODUCTION Diseases influencing heart have gotten normal on daily basis. Diseases influencing heart is expanding as a result of present day feeble way of life increment in ailments like diabetes, hypertension and tobacco smoking. Heart can be influenced because of various conditions. Electrocardiogram (ECG) is one of the basic, effectively accessible, more affordable, effectively feasible, non-obtrusive examinations accessible at all spot incorporating provincial territories with insignificant framework. Specialists at the far off spots with fundamental clinical information may not be all around prepared to decipher an ECG. Appropriately diagnosing cardiovascular (heart related) condition at the most punctual is of most extreme significance. During these ailments, time matters the most. Consistently delay in treating them, trail to more harm in heart muscle bringing about antagonistic result. Consequently, exact distinguishing proof and early diagnosing these heart infections is vital. Anticipating patients into low and high danger is vital. Okay patients can be dealt with locally at a similar clinic with insignificant framework itself. High-hazard patients require early reference to cardiovascular reference clinics. ECG affirms or associates the finding with myocardial localized necrosis, arrhythmias and different conditions. When affirmed, they are treated with prescriptions or methodology (medical procedures) contingent upon the patient subtype and the offices accessible [1]. Electrocardiogram (ECG) is an intermittent sign, which mirrors the movement of the heart. From ECG a great deal data is gotten for typical and obsessive physiology of heart. The ECG signal is non-fixed in nature and extremely hard to dissect. Clinical perception takes long time and the sign is non-fixed. In this way, computer based method is utilized in ECG investigation. The rule of ECG signal is electrical movement. This is sent through the body and pick up on the skin. At last, the ECG machine records the action utilizing terminals and show graphically. The ECG signal comprises of dreary complex waveform with a recurrence estimation of 1 Hz. Each cardiovascular cycle comprises of three waves, for example, P wave QRS wave and T wave. These waves are delivered because of the capacity of atria and ventricular pieces of the heart. Cardiovascular illness is one of the significant reasons for death in the western reality where in excess of 16 million kick the bucket every year [2]. An Electro cardio realistic sign is recorded on a long timescale to recognize discontinuously happening aggravations in the heart musicality. To diminish the cardiovascular illnesses the way of life changes, for example, decreased cholesterol admission and customary exercise are obligatory. Early discovery is an essential advance in the cardiovascular sickness. To gauge the electrical sign from various pieces of heart two sorts of terminals are utilized. They are appendage anodes and chest terminals. The P wave is created by atrial depolarization, the length is under 120 ms, and this can be considered as low recurrence wave. ORS complex wave has span of 70-110 ms and T wave is delivered by the repolarization of ventricles having length more noteworthy than 300 ms [3]. Electrocardiogram (ECG) accounts are frequently debased by a lot of commotion and ancient rarities that can be inside the recurrence band of helpful heart information and can show with comparable morphologies to the ECG waveform itself [4]. Not exclusively does the presence of commotion and antiquity meddle with the right acknowledgment of QRS,
Diagnostics
This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast...
Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
Applied Sciences
Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for telecare applications. This paper presents a deep convolutional autoencoder to compress the PCG signals. At the encoder side, seven convolutional layers were used to compress the PCG signals, which are collected on the patients in the rural areas, into the feature maps. At the decoder side, the doctors at the remote hospital use the other seven convolutional layers to decompress the feature maps and reconstruct the original PCG signals. To confirm the effectiveness of our method, we used an open accessed dataset on PHYSIONET. The achievable compress ratio (CR) is 32 when the percent root-mean-square difference (PRD) is less than 5%.
Deep convolutional neural network application to classify the ECG arrhythmia
Springer, 2020
The ECG signal is such a substantial means to reflect all the electrical activities of the cardiac system. Therefore, it is considered by the physician as the essential tools and materials to diagnose and treat heart diseases. To deal with different types of arrhythmia, the physician manually inspects the ECG heartbeat. Since there are tiny alternations in the amplitude, durations and therefore the morphology, the computer-based systems were needed to develop such solutions in order to help the physician to do their job. In this study, a novel tactic to automatically classify ten different arrhythmia types was developed depending on the deep learning theory. Consequently, the well-known convolutional neural network (CNN) approach was adopted to classify those different types of arrhythmia. The structure of the proposed model consists of 11 layers distributed as follows: four layers as convolution interchanged with other four layers of max pooling and finally three successfully connected layers. The experiment was conducted with the dataset which was downloaded from the Physionet in the Massachusetts Institute of Technology-Beth Israel Hospital database and then augmented to get sufficient and balanced dataset. To evaluate the performance of the proposed method and compare it with the previous algorithms, confusion matrix, sensitivity (SEN), specificity (SPE), precision (PRE), area under curve and receiver operating characteristic have been used and calculated. It has been found that performance from the proposed method is better than the existing methods based on CNN, and the accuracy is 99.84.
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Cardiac arrhythmia is one among a type of cardiovascular diseases (CVDs), which reports 12% of total deaths all-over the world. Even though there is a lot of growth in IoT-health monitoring, the manual method suffers from a lot of drawbacks. Hence, there is a need for an automatic method in health-care specifically, for classification of arrhythmia, for which an optimized deep convolutional neural network will be proposed. The IoT network will be simulated for collecting the ECG signals from the patients, and the signals will be processed for classification of arrhythmia in patients, which assures continuous health monitoring of patients. The proposed model named optimized deep convolutional neural network will be implemented and compared with the existing methods in order to reveal the effectiveness based on the performance metrics, such as accuracy, sensitivity, and specificity.
Cardiac Arrhythmia Classification in Electrocardiogram Signals with Convolutional Neural Networks
Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 2023
Electrocardiography is a frequently used examination technique for heart disease diagnosis. Electrocardiography is essential in the clinical evaluation of patients who have heart disease. Through the electrocardiogram (ECG), medical doctors can identify whether the cardiac muscle dysfunctions presented by the patient have an inflammatory origin and early diagnosis of serious diseases that primarily affect the blood vessels and the brain. The basis of arrhythmia diagnosis is the identification of normal and abnormal heartbeats and their classification into different diagnoses based on ECG morphology. Heartbeats can be divided into five categories: non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is difficult to distinguish these heartbeats apart on the ECG as these signals are typically corrupted by outside noise. The objective of this study is to develop a classifier capable of classifying a patient's ECG signals for the detection of arrhythmia in clinical patients. We developed a convolutional neural network (CNN) to identify five categories of heartbeats in ECG signals. Our experiment was conducted with ECG signals obtained from a publicly available MIT-BIH database. The number of instances was even out to five classes of heartbeats. The proposed model achieved an accuracy of 99.33% and an F1-score of 99.44% in the classification of ventricular ectopic beats (VEB).