AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals

Abstract

Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. First, it is essential to detect in real time the precursor symptoms of stroke, which occur differently for each individual, and to provide professional treatment by a medical institution within the proper treatment window. However, prior studies have focused on developing acute treatment or clinical treatment guidelines after the onset of stroke rather than detecting the prognostic symptoms of stroke. In particular, in recent studies, image analysis such as magnetic resonance imaging (MRI) or computed tomography (CT) has mostly been used to detect and predict prognostic symptoms in stroke patients. Not only are these methodologies difficult to diagnose early in real-time, but they also have limitations in terms of a long test time and a high cost of testing. In this paper, we propose a system that can predict and semantically interpret stroke prognostic symptoms based on machine learning using the multimodal bio-signals of electrocardiogram (ECG) and photoplethysmography (PPG) measured in real-time for the elderly. To predict stroke disease in real-time while walking, we designed and implemented a stroke disease prediction system with an ensemble structure that combines CNN and LSTM. The proposed system considers the convenience of wearing the bio-signal sensors for the elderly, and the bio-signals were collected at a sampling rate of 1,000Hz per second from the three electrodes of the ECG and the index finger for PPG while walking. According to the experimental results, C4.5 decision tree showed a prediction accuracy of 91.56% while RandomForest showed a prediction accuracy of 97.51% during walking by the elderly. In addition, the CNN-LSTM model using raw data of ECG and PPG showed satisfactory prediction accuracy of 99.15%. As a result, the real-time prediction of the elderly stroke patients simultaneously showed high prediction accuracy and performance.INDEX TERMS Deep learning, machine learning, electrocardiogram (ECG), photo plethysmography (PPG), multi-modal bio-signal, real-time stroke prediction, stroke disease analysis.

ECG-ViT: A Transformer-Based ECG Classifier for Energy-Constraint Wearable Devices

“…Lastly, no experiments have been performed to indicate any performance enhancement gained from using the generated 2 Journal of Sensors ECG instead of the available PPG (for example, a comparison of measured HR). Other works related to ECG and PPG are [26][27][28][29][30][31], but they do not show how to synthesis ECG from PPG.…”

Multimodal Sensing in Stroke Motor Rehabilitation

“…There are some other studies whose application periods are not clear, which can also pro-vide some reference possibilities for the future combination of medicine and medicine. [224][225][226][227][228][229][230][231] Study characteristics related to the multimodal sensor applications in stroke rehabilitation and its placement, evaluation content, performance or result, algorithm, and feature for the included papers are presented in Table 4.…”

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

“…ECG contains a wealth of information related to heart health and is widely used for heart disease monitoring and prediction, such as acute myocardial infarction [ 32 ], sepsis onset [ 33 ], and stroke [ 34 ]. To obtain ECG in real-time and remotely, an IoT-based wireless communication system is needed to transmit the collected heart to a smart terminal.…”