Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals

Sundarasekar, Revathi; Thanjaivadivel, M.; Manogaran, Gunasekaran; Kumar, Priyan Malarvizhi; Varatharajan, R.; Chilamkurti, Naveen; Hsu, Ching‐Hsien

doi:10.1007/s10916-018-1093-4

Cited by 59 publications

(12 citation statements)

References 40 publications

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“…For example, Samuel et al [ 8 ] used an ensemble technique for the analysis of cardiac illness and achieved an accuracy of 87.0%. Sundarasekar et al [ 9 ] used a hybrid approach based on ANN and a fuzzy analytics hierarchy for the detection of cardiac disease and attained 88.3% of accuracy. Muhammad et al [ 10 ] used a computational framework for the identification and detection of cardiac illness and conquered promising outcomes.…”

Section: Related Workmentioning

confidence: 99%

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

Muhammad

Almoteri

Mujlid

et al. 2022

BioMed Research International

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Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient’s heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

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Section: Related Workmentioning

confidence: 99%

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

Muhammad

Almoteri

Mujlid

et al. 2022

BioMed Research International

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“…Nevertheless, other studies besides incorporating IoT devices, focus on improving data acquisition and processing. In this sense, Sundarasekar et al [25] proposed the use of Maximal Overlap Discrete Wavelet Transform to decompose the ECG and identify changes in the R waves of the noisy signals. Similarly, Djelouat et al [26] incorporated Compressive Sensing in an IoT-based ECG monitoring platform to leverage the ECG signal structure and achieve high efficiency in the acquisition.…”

Section: State Of the Artmentioning

confidence: 99%

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Rincon

Guerra-Ojeda

Carrascosa

et al. 2020

Sensors

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Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation.

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“…In order to augment the R-waves of the ECG and VCG signals, the maximal overlap discrete wavelet transform was applied. In this introduced structure, by default, wavelet 'sym4' was utilised to decompose the ECG signal below level 5 and wavelet coefficients were reconstructed just at levels 4 and 5 [21]. Figure 2b shows the diagnostic R-peak using the wavelet coefficients and other peaks of the ECG signal.…”

Section: Peak Detectionmentioning

confidence: 99%

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Hafshejani

Mehridehnavi

Hajian

et al. 2021

IET Signal Processing

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Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12-lead electrocardiogram (ECG) waves including T-wave, Q-wave, and ST-segment. The main goal of this study is to represent vectorcardiography (VCG) as a complementary diagnostic tool of the ECG method to discriminate the various type of MI from normal cases. The system proposed in this study was analysed on the Physikalisch-Technische Bundesanstalt diagnostic ECG database and a recorded signal database for 80 MI and 52 healthy cases. Each record consists of 15 ECG and VCG signals. In this study, tridimensional morphological features were applied to the classification and regression tree (CART) and the feedforward neural network classifier. To classify MI cases from healthy control cases of our recorded database, classification and regression tree achieved the same results when VCG features or ECG features were applied with an accuracy of 99.4%, a sensitivity of 100%, and a specificity of 98.7%. Further, by using VCG Octant features with this current method, anterior-MI and inferior-MI were separated with an accuracy of 98.9%, a sensitivity of 98%, and a specificity of 100%. The outcomes prove that the VCG features performed more accurately than ECG features in MI localisation. K E Y W O R D S classification and regression tree (CART), electrocardiogram (ECG), feedforward neural network (FFNN), morphological features, myocardial infarction (MI), vectorcardiogram (VCG)This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Internet of Things with Maximal Overlap Discrete Wavelet Transform for Remote Health Monitoring of Abnormal ECG Signals

Cited by 59 publications

References 40 publications

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

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