Zhiqiang He^*

Journal of Intelligent Medicine and Healthcare, Vol.1, No.2, pp. 79-89, 2022, DOI:10.32604/jimh.2022.036624

Abstract Electrocardiogram (ECG) is widely used to detect arrhythmia. Atrial fibrillation, atrioventricular block, premature beats, etc. can all be diagnosed by ECG. When the distribution of training data and test data is inconsistent, the accuracy of the model will be affected. This phenomenon is called dataset shift. In the real-world heartbeat classification system, the heartbeat of the training set and test set often comes from patients of different ages and genders, so there are differences in the distribution of data sets. The main challenge in applying machine learning algorithms to clinical AI systems is dataset shift. Test-time adaptation (TTA) aims to… More >

Marwa Obayya¹, Nadhem NEMRI², Lubna A. Alharbi³, Mohamed K. Nour⁴, Mrim M. Alnfiai⁵, Mohammed Abdullah Al-Hagery⁶, Nermin M. Salem⁷, Mesfer Al Duhayyim^8,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3151-3166, 2023, DOI:10.32604/cmc.2023.032765

Abstract With new developments experienced in Internet of Things (IoT), wearable, and sensing technology, the value of healthcare services has enhanced. This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare. Bio-medical Electrocardiogram (ECG) signals are generally utilized in examination and diagnosis of Cardiovascular Diseases (CVDs) since it is quick and non-invasive in nature. Due to increasing number of patients in recent years, the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients. In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals. The current study… More >

Ishani Mishra^1,*, Sanjay Jain², Vivek Maik³

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1109-1123, 2023, DOI:10.32604/csse.2023.025999

Abstract In wireless body sensor network (WBSN), the set of electrocardiogram (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. While transmitting these collected data some adversaries may capture and misuse it due to the compromise of security. So, the major aim of this work is to enhance secure transmission of ECG signal in WBSN. To attain this goal, we present Pity Beetle Swarm Optimization Algorithm (PBOA) based Elliptic Galois Cryptography (EGC) with Chaotic Neural Network. To optimize the key generation process in Elliptic Curve Cryptography… More >

S. Sathishkumar^1,*, R. Devi Priya²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 129-148, 2023, DOI:10.32604/iasc.2023.023817

Abstract Electrocardiogram (ECG) is a diagnostic method that helps to assess and record the electrical impulses of heart. The traditional methods in the extraction of ECG features is inneffective for avoiding the computational abstractions in the ECG signal. The cardiologist and medical specialist find numerous difficulties in the process of traditional approaches. The specified restrictions are eliminated in the proposed classifier. The fundamental aim of this work is to find the R-R interval. To analyze the blockage, different approaches are implemented, which make the computation as facile with high accuracy. The information are recovered from the MIT-BIH dataset. The retrieved data… More >

Fuad A. M. Al-Yarimi^*

Computer Systems Science and Engineering, Vol.44, No.1, pp. 129-142, 2023, DOI:10.32604/csse.2023.024297

Abstract A critical component of dealing with heart disease is real-time identification, which triggers rapid action. The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias. Recent contributions to cardiac arrhythmia prediction using supervised learning approaches generally involve the use of demographic features (electronic health records), signal features (electrocardiogram features as signals), and temporal features. Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats, it is possible to detect some of the irregularities in the early stages of arrhythmia. This paper describes the training… More >

Ajina Mohamed Ameer^*, M. Victor Jose

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1349-1361, 2022, DOI:10.32604/iasc.2022.023924

Abstract This paper presents a novel approach for arrhythmia detection and classification using modified recurrent neural network. In medicine and analytics, arrhythmia detections is a hot topic, specifically when it comes to cardiac identification. In the research methodology, there are 4 main steps. Acquisition and pre-processing of data, electrocardiogram (ECG) feature extraction utilizing QRS (Quick Response Systems) peak, and ECG signal classification using a Modified Recurrent Neural Network (Modified RNN) for arrhythmia diagnosis. The Massachusetts Institute of Technology-Beth Israel Hospital. (MIT-BIH) Arrhythmia database was used, as well as the image accuracy. Medium filter is used in the pre-processing. Feature extraction is… More >

S. Karthiga^*, A. M. Abirami

Computer Systems Science and Engineering, Vol.42, No.3, pp. 851-866, 2022, DOI:10.32604/csse.2022.021935

Abstract Much attention has been given to the Internet of Things (IoT) by citizens, industries, governments, and universities for applications like smart buildings, environmental monitoring, health care and so on. With IoT, network connectivity is facilitated between smart devices from anyplace and anytime. IoT-based health monitoring systems are gaining popularity and acceptance for continuous monitoring and detect health abnormalities from the data collected. Electrocardiographic (ECG) signals are widely used for heart diseases detection. A novel method has been proposed in this work for ECG monitoring using IoT techniques. In this work, a two-stage approach is employed. In the first stage, a… More >

Ashraf Mohamed Ali Hassan¹, Mohammed S. Alzaidi², Sherif S. M. Ghoneim^2,3,*, Waleed El Nahal⁴

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4391-4408, 2022, DOI:10.32604/cmc.2022.024044

Abstract This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of… More >

Anas Bushnag^*

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 51-70, 2022, DOI:10.32604/iasc.2022.024005

Abstract A portable electrocardiogram (ECG) monitoring system is essential for elderly and remote patients who are not able to visit the hospital regularly. The system connects a patient to his/her doctor through an Internet of Things (IoT) cloud server that provides all the information needed to diagnose heart diseases. Patients use an ECG monitoring device to collect and upload information regarding their current medical situation via the Message Queue Telemetry Transport (MQTT) protocol to the server. The IoT cloud server performs further analysis that can be useful for both the doctor and the patient. Moreover, the proposed system has an alert… More >

Ishani Mishra^1,*, Sanjay Jain²

Intelligent Automation & Soft Computing, Vol.33, No.1, pp. 415-428, 2022, DOI:10.32604/iasc.2022.022860

Abstract In wireless body sensor network (WBSN), the set of electrocardiograms (ECG) data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient. However, due to the size of the ECG data, the performance of the signal compression and reconstruction is degraded. For efficient wireless transmission of ECG data, compressive sensing (CS) frame work plays significant role recently in WBSN. So, this work focuses to present CS for ECG signal compression and reconstruction. Although CS minimizes mean square error (MSE), compression rate and reconstruction probability of the CS is… More >