Low Cost, Portable ECG Monitoring and Alarming System Based on Deep Learning

Ahsanuzzaman, S.M; Ahmed, Toufiq; Rahman, Md. Atiqur

doi:10.1109/tensymp50017.2020.9231005

Cited by 20 publications

(7 citation statements)

References 12 publications

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“…In contrast, the authors in [ 114 ] used the DS18B20 temperature sensor and ADXL1335 accelerometer to develop the desired wearable system. In addition, the authors in [ 52 , 57 , 65 , 67 , 69 , 71 , 72 , 74 , 75 , 77 , 79 , 81 , 89 , 94 , 100 , 101 , 113 , 122 ] used the AD8232 ECG sensor to collect vital signs data. In these studies, as discussed in Table 2 , different processing units, connector modules, and power sources were used to build the wearable device.…”

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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“…They have concluded that the commercial implementation is realistic with minimal changes in design and size of the model. [4] in their paper have designed and developed a method for predicting arrythmia along with monitoring the ECG signals. To make this system, long short-term memories neural network, Recurrent neural network, TensorFlow and Keras library are applied.…”

Section: Arushi Goyal Shimony Mittal Rugved Sawant Ajatmentioning

confidence: 99%

ECG Monitoring Using Deep Learning – A Review

Journal¹

2023

IJSREM

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Electrocardiogram (ECG) is used to detect heart's rhythm and electrical activity. Heart rhythm problems (heart arrhythmias) occur when the electrical signals that coordinate the heart's beats don't work properly and faulty signaling causes the heart to beat too fast, too slow or irregularly. Patients facing arrhythmia have no indications of having an arrhythmia and only a doctor will be able to diagnose arrhythmias in a routine test. Therefore, low cost portable monitoring system plays a important role. The main objective of this project is to design and develop a model for predicting arrhythmia (atrial fibrillation) along with monitoring the ECG signals. To create an arrhythmia prediction model and an ECG monitoring system, Deep Learning algorithms, TensorFlow and Keras library are applied here. The system is being designed with Raspberry pi 3, Arduino UNO, AD8232 single lead ECG sensor, biomedical sensor pad and battery. This system will be helpful for remote ECG monitoring and make easier for doctors to monitor the ECG of their patients outside the hospital. Key Words: ECG, Deep Learning, arrhythmia.

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“…This DL model achieves an overall accuracy of 97.57% for the prediction of CVDs. Ahsanuzzman et al [ 35 ] investigated the classification and prediction of a single arrhythmia class, atrial fibrillation (AFib), using ECG signals. A hybrid long short-time memory (LSTM) and RNN was used for this task.…”

Section: Related Workmentioning

confidence: 99%

“…In this study, we choose to combine four public databases to confirm the efficacy of the model proposed. In this paper, the proposed model has succeeded to diagnose the majority of 27 classes, including 26 CVDs and normal sinus rhythm, which will assist domain experts in identifying patient records, while other researches used ECG to classify just one or two anomalies [ 35 , 38 ].…”

Section: Related Workmentioning

confidence: 99%

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis

Sakli

Ghabri

Soufiene

et al. 2022

Computational Intelligence and Neuroscience

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Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achieving a spectacular performance in healthcare applications. According to the World Health Organization (WHO), in 2020 there were around 25.6 million people who died from cardiovascular diseases (CVD). Thus, this paper aims to shad the light on cardiology since it is widely considered as one of the most important in medicine field. The paper develops an efficient DL model for automatic diagnosis of 12-lead electrocardiogram (ECG) signals with 27 classes, including 26 types of CVD and a normal sinus rhythm. The proposed model consists of Residual Neural Network (ResNet-50). An experimental work has been conducted using combined public databases from the USA, China, and Germany as a proof-of-concept. Simulation results of the proposed model have achieved an accuracy of 97.63% and a precision of 89.67%. The achieved results are validated against the actual values in the recent literature.

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Low Cost, Portable ECG Monitoring and Alarming System Based on Deep Learning

Cited by 20 publications

References 12 publications

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

ECG Monitoring Using Deep Learning – A Review

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis

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