A Wireless Early Prediction System of Cardiac Arrest through IoT

ElSaadany, Yosuf; Majumder, Akm Jahangir Alam; Ucci, D.R.

doi:10.1109/compsac.2017.40

Cited by 26 publications

(18 citation statements)

References 10 publications

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“…Compared with similar devices presented in the literature, our prototype has a lower power consumption than other Wi-Fi based systems [34,35] which is similar to results achieved by other systems using Bluetooth [13,[31][32][33] and ISM wireless communication [19,29].…”

Section: Discussionsupporting

confidence: 79%

Arm-ECG Wireless Sensor System for Wearable Long-Term Surveillance of Heart Arrhythmias

2019

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This article presents the devising, development, prototyping and assessment of a wearable arm-ECG sensor system (WAMECG1) for long-term non-invasive heart rhythm monitoring, and functionalities for acquiring, storing, visualizing and transmitting high-quality far-field electrocardiographic signals. The system integrates the main building blocks present in a typical ECG monitoring device such as the skin surface electrodes, front-end amplifiers, analog and digital signal conditioning filters, flash memory and wireless communication capability. These are integrated into a comfortable, easy to wear, and ergonomically designed arm-band ECG sensor system which can acquire a bipolar ECG signal from the upper arm of the user over a period of 72 h. The small-amplitude bipolar arm-ECG signal is sensed by a reusable, long-lasting, Ag–AgCl based dry electrode pair, then digitized using a programmable sampling rate in the range of 125 to 500 Hz and transmitted via Wi-Fi. The prototype comparative performance assessment results showed a cross-correlation value of 99.7% and an error of less than 0.75% when compared to a reference high-resolution medical-grade ECG system. Also, the quality of the recorded far-field bipolar arm-ECG signal was validated in a pilot trial with volunteer subjects from within the research team, by wearing the prototype device while: (a) resting in a chair; and (b) doing minor physical activities. The R-peak detection average sensibilities were 99.66% and 94.64%, while the positive predictive values achieved 99.1% and 92.68%, respectively. Without using any additional algorithm for signal enhancement, the average signal-to-noise ratio (SNR) values were 21.71 and 18.25 for physical activity conditions (a) and (b) respectively. Therefore, the performance assessment results suggest that the wearable arm-band prototype device is a suitable, self-contained, unobtrusive platform for comfortable cardiac electrical activity and heart rhythm logging and monitoring.

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Section: Discussionsupporting

confidence: 79%

Arm-ECG Wireless Sensor System for Wearable Long-Term Surveillance of Heart Arrhythmias

2019

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“…The FPGA execution of the fuzzy classifier gave low execution time compared to KNN, SVM, decision tree, as well as Naive Bayes. YosufElSaadanyet al [22] presented a multi-sensory system utilizing IoT that gathered heart rates along with body temperature. An embedded sensory system with a Low Energy Bluetooth communication module was employed to gather ECG along with body temperature data utilizing a smartphone in common surroundings.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The FPGA presented in [21] attains the accuracy as well as lower execution time but the technique was only apposite for the prediction of the pathological setting of cardiovascular diseases. The work was rendered in [18] and [22] suggested another online healthcare services diagnosis framework for observing remote heart patients utilizing a cell phone and wearable sensors. All these techniques prove their efficiency in their ways, but none of them stress on the significance of IoT data security.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Sarmah¹

2020

IEEE Access

127

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The leading causes of death worldwide are chronic illnesses suchlike diabetes, Heart Disease (HD), cancer as well as chronic respiratory malady. It is remarkably intricate to diagnose HD with disparate symptoms or features. With the augmentation in popularity of smart wearable gadgets, a chance to render an Internet of Things (IoT) solution has turned out to be more. Unfortunately, the survival rates are low for the people suffering from sudden heart attacks. Consequently, a patient monitoring scheme intended for heart patients utilizing IoT centered Deep Learning Modified Neural Network (DLMNN) is proposed to assist in the HD diagnosis, and medication is given accordingly. This proposed technique is executed via '3' steps: I) Authentication, ii) Encryption, and iii) Classification. First, by utilizing the substitution cipher (SC) together with the SHA-512, the heart patient of the specific hospital is authenticated. Subsequently, the wearable IoT sensor device, which is fixed to the patient's body, concurrently transmits the sensor data to the cloud. This sensor data is encrypted and securely transmitted to the cloud utilizing the PDH-AES technique. After that, the encrypted data is finally decrypted, and by employing the DLMNN classifier, the classification is done. The classified outcomes comprise '2'types of data: i) normal and ii) abnormal. It denotes the patient's heart condition and if the outcome is abnormal, an alert text is passed to the physician for treating the patient. The investigational outcomes are estimated and the DLMNN for HD diagnosis shows improvement as compared to existing algorithms. Additionally, the proposed PDH-AES used in support of secure data transmission results in the highest level of security i.e. 95.87%, and it is achieved in the lowest time for encryption along with decryption when weighted against the existent AES. INDEX TERMS Disease prediction, Healthcare Monitoring System, Internet of Things (IoT), Advanced Encryption Standard (AES), Modified Huffman Algorithm (MHA), Deep Learning Modified Neural Network (DLMNN), and Cuttlefish Optimization Algorithm (CFOA).

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“…Several multisensory systems to collect physical activity have been developed. The use of the mobile phone to collect sensors data using a low energy Bluetooth communication module is available and easy to use for most of the people [ 19 ].…”

Section: Devices To Monitor Patient’s Healthmentioning

confidence: 99%

Review on Wearables to Monitor Foot Temperature in Diabetic Patients

Martín-Vaquero

Encinas

Dios

et al. 2019

Sensors

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One of the diseases that could affect diabetic patients is the diabetic foot problem. Unnoticed minor injuries and subsequent infection can lead to ischemic ulceration, and may end in a foot amputation. Preliminary studies have shown that there is a positive relationship between increased skin temperature and the pre–ulceration phase. Hence, we have carried out a review on wearables, medical devices, and sensors used specifically for collecting vital data. In particular, we are interested in the measure of the foot–temperature. Since there is a large amount of this type of medical wearables, we will focus on those used to measure temperature and developed in Spain.

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A Wireless Early Prediction System of Cardiac Arrest through IoT

Cited by 26 publications

References 10 publications

Arm-ECG Wireless Sensor System for Wearable Long-Term Surveillance of Heart Arrhythmias

Arm-ECG Wireless Sensor System for Wearable Long-Term Surveillance of Heart Arrhythmias

An Efficient IoT-Based Patient Monitoring and Heart Disease Prediction System Using Deep Learning Modified Neural Network

Review on Wearables to Monitor Foot Temperature in Diabetic Patients

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