Abstract
Electrocardiogram (ECG) is sensitive to autonomic dysfunction and cardiac complications derived from ischemic or hemorrhage stroke and is supposed to be a potential prognostic tool in stroke identification and post-stroke treatment. ECG data generated cannot be real-time accumulated, processed, and used for enterprise-level healthcare and wellness services with the existing cardiovascular monitoring system used in hospitals. This study aims to assess the feasibility of a cyber-physical cardiac monitoring system to classify stroke patients with altered cardiac activity and healthy adults. Here, we propose Big-ECG, a cyber-physical cardiac monitoring system for stroke management, consisting of a wearable ECG sensor, data storage and data analysis in a big data platform, and health advisory services using data analytics and medical ontology. We investigated our proposed ECG-based patient monitoring system with 45 stroke patients (average age 70.8 years old, 68% men) admitted to the rehabilitation center of the hospital and 40 healthy elderly volunteers (average age 75.4 years old, 38% men). We recorded ECG at resting state using a single-channel ECG patch within three months of diagnosis of ischemic stroke (clinically confirmed). In statistical results, ECG fiducial features, RR-I, QRS, QT, ST, and heart rate variability (HRV) features, SDSD, LF/HF, LF/(LF + HF), and HF/(LF + HF) are observed as significantly distinctive biomarkers for the stroke group relative to the healthy control group. The Random Trees model presented the best classification performance (overall accuracy: 95.6%) utilizing ECG fiducial variables. This system may assist healthcare enterprises in prognosis and rehabilitation management during post-stroke treatment.
Highlights
Stroke, a primary neurovascular disease in adulthood, is the world's second leading cause of death in the elderly community [1]
We investigated the cardiac features through statistical analysis and hypothesis tests to identify the significant important ECG features associated with ischemic stroke
We investigated the association of the electrocardiographic features with post-stroke ECG in two methods
Summary
A primary neurovascular disease in adulthood, is the world's second leading cause of death in the elderly community [1] Hemorrhagic events, such as a stroke, occurs due to the blood vessel's rupture in the brain and hamper the supply of oxygen to brain tissue at the lesion site causing brain cell death. The vital components of these CPS are composed of a blend of enabling technologies, comprising smart medical devices, diagnostics process automation, autonomous robots, Internet of Things (IoT) devices, medical Big Data, Fog, and Cloud Computing [14]. Patients' medical history, doctor's interrogation records contribute to accumulating vast unstructured datasets Hospitals are handling this Big medical Data acquisition, processing and analytics, storage, retrieving real-time data, and collecting historical medical data using suitable Big data technologies [16]. The Hadoop ecosystem comprises of Hadoop distributed file system (HDFS), MapReduce algorithm, and other analytical tools for handling, analyzing Big Data to make it mature and enterprise-ready
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