Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents

Shuzan

et al. 2020

Sensors

Self Cite

168

110

Hypertension is a potentially unsafe health ailment, which can be indicated directly from the blood pressure (BP). Hypertension always leads to other health complications. Continuous monitoring of BP is very important; however, cuff-based BP measurements are discrete and uncomfortable to the user. To address this need, a cuff-less, continuous, and noninvasive BP measurement system is proposed using the photoplethysmograph (PPG) signal and demographic features using machine learning (ML) algorithms. PPG signals were acquired from 219 subjects, which undergo preprocessing and feature extraction steps. Time, frequency, and time-frequency domain features were extracted from the PPG and their derivative signals. Feature selection techniques were used to reduce the computational complexity and to decrease the chance of over-fitting the ML algorithms. The features were then used to train and evaluate ML algorithms. The best regression models were selected for systolic BP (SBP) and diastolic BP (DBP) estimation individually. Gaussian process regression (GPR) along with the ReliefF feature selection algorithm outperforms other algorithms in estimating SBP and DBP with a root mean square error (RMSE) of 6.74 and 3.59, respectively. This ML model can be implemented in hardware systems to continuously monitor BP and avoid any critical health conditions due to sudden changes.

Section: Introductionmentioning

confidence: 99%

Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques

Shuzan

et al. 2020

Sensors

Self Cite

168

110

“…In contrast, X-rays are the most common and widely available diagnostic imaging technique, playing a crucial role in clinical care and epidemiological studies [8,9]. There are several regions across the globe where there is a scarce availability of practiced healthcare workers and radiologists whose prediction on such diseases matter greatly [10][11][12]. Computer-aided diagnosis using artificial intelligence based solutions is becoming increasingly popular these days [13,14].…”

Section: Introductionmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

“…In recent years, technological advances in electronics, wireless communications, sensor materials and physiological data-processing tools have made possible ubiquitous computing available for healthcare applications [6]. As a result, a variety of experimental wearable smart devices have been developed for ECG monitoring; they include T-shirts or vests [7,8], belts [9,10], adhesive patches [11,12] and armbands [13]. In addition, there are commercially available systems in the form of portable Holter monitors (e.g., NH-301 (Norav Medical GmhH, Wiesbaden, Germany)), adhesive patches (e.g., Zio (iRhythm San Francisco, CA, USA)) and smartwatches (e.g., Apple Watch 5 (Apple Inc., Cupertino, CA, USA)), targeting similar applications.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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.