Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing

Abstract

Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients

“…Furthermore, COVID-19 infects heart cells and provokes arrhythmia and heart failure. Researchers have utilized cloud computing as artificial intelligence-based detection of various cardiovascular illnesses [37] , [38] .…”

Recent Developments and Future Directions of Wearable Skin Biosignal Sensors

“…Similarly, with PPG signal data, prehypertension, stage 1 hypertension, and stage 2 hypertension could be classified with machine learning algorithms. [ 188 ] Other cardiovascular health signals could be detected through machine learning‐powered PPG. A notable heart disease, hypertrophic cardiomyopathy (HCM), which is a disease in which the heart muscle becomes thickened, was able to be discovered with a wearable biosensor ( Figure ).…”

“…As a well-known approach, blood pressure could be estimated through the calculation of pulse transit time by comparing ECG and PPG signals. [186][187][188] For instance, the wearable ear-ECG/PPG sensor detected heart rate while estimating blood pressure by utilizing machine learning. [186] The authors proposed situating sensors behind both ears to enhance wearability to successfully capture weak ear-ECG/PPG signals.…”

Intelligent Bi-LSTM with Architecture Optimization for Heart Disease Prediction in WBAN through Optimal Channel Selection and Feature Selection

“…Researchers [ 24 ] have investigated diverse expert systems like Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT), and also the 1D CNN-LSTM to implement a system that assists physicians through continuous monitoring purposes. Thus, the empirical outcomes reveled that it has yielded efficient, fully connected monitoring systems, and is cost-effective for cardiac patients.…”