IoT Based Patient Monitoring System Using ECG Sensor

Rahman, Alvee; Rahman, Tahsinur; Ghani, Nawab Haider; Hossain, SK Alamgir; Uddin, Jia

doi:10.1109/icrest.2019.8644065

Cited by 73 publications

(36 citation statements)

References 11 publications

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“…A great number of researches proposed relevant IoT-based solutions for ECG monitoring [12,27,132,[136][137][138][139][140][141][142]; these researches are centered around the use of IoT devices for real-time ECG acquisition, processing, and analytics. The authors in [27,136,137], and [138] proposed an IoT-based patient-continuous monitoring system using the ECG sensor. All systems collect ECG data using sensors and process and analyze in real-time the collected ECG data.…”

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

“…Therefore, overlapping might be apparent with the other abovementioned categories, since they are embedding sensors within the ECG monitoring solution. Among these research initiatives include the subsequent propositions from [12,26,28,136,138,139,142]. For instance, Villarrubia et al [142] proposed a monitoring and tracking system that uses virtual organization of agents for easy integration of different devices.…”

Section: Monitoring Devicesmentioning

confidence: 99%

See 1 more Smart Citation

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

230

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

Section: Monitoring Devicesmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

230

View full text Add to dashboard Cite

show abstract

“…Alvee Rahman Et al. [1] proposes an intelligent ECG monitoring system to help the doctors, nurses and care takers to monitor the heart patients staying at home and hospitals remotely. This system comprises of Raspberry Pi processor and pulse rate sensor along with ESP8266, the internet module.…”

Section: Related Workmentioning

confidence: 99%

Low Cost Ecg Monitoring using Internet of Things

Chamundeeswari*¹,

Srinivasan²,

Bharathi³

2019

IJRTE

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Patient monitoring is the heart of the health care domain in day to day life either at home or at hospital. This paper focuses an Intelligent ECG Monitoring System to monitor the heart patients residing at distant places at low cost and complexity using Internet of Things. The proposed system automatically screens the health condition of the patient and records their Electrocardiogram through heart rate sensor and ATtiny Board. ATtiny is a low cost IoT device used along with heart rate sensor and ESP8266 to record the electrocardiogram of the patients. The ECG is sent to the doctors, nurses or the patient’s relatives residing in the remote places through the internet to help them in remote monitoring of the patients with ease.

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“…The most recent advances in portable and wearable medical devices may become highly useful in this premature screening of silent AF, because they are able to significantly increase the monitoring time window where the arrhythmia can be detected. Thus, fresh improvements in low-power embedded systems, communication protocols, and cloud computing technologies have allowed the development of numerous wearable systems with the ability for ECG monitoring over several weeks and even months, while the subject continues a normal active life [ 15 , 16 , 17 , 18 ]. However, these devices will usually work in highly dynamic and changing environments, thus providing ECG signals that are especially prone to be corrupted with different kinds of noises, such as motion artifacts, powerline interference, baseline wander, and high-frequency electromyography disturbances, among others [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…While a broad variety of techniques for ECG quality assessment have been proposed to date [ 20 ], most of them cannot deal with signals acquired via portable or wearable systems. On the one hand, many algorithms have been designed to simultaneously analyze the 12 leads found in the standard ECG, but portable and wearable systems often present a more reduced number of signals, commonly between one and three [ 15 , 16 , 17 , 18 ]. On the other hand, numerous methods raised to assess single-lead ECG quality are based on detecting fiducial points and morphological events in the signal, and then computing parameters such as mean RR interval, ratio of maximum to minimum RR interval, time consistency of PQRST waveforms, coherence of QRS complexes, etc.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Herraiz

Martínez-Rodrigo

Bertomeu-González

et al. 2020

Entropy

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Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.

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IoT Based Patient Monitoring System Using ECG Sensor

Cited by 73 publications

References 11 publications

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Low Cost Ecg Monitoring using Internet of Things

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

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