A Personalized Point-of-Care Platform for Real-Time ECG Monitoring

With the advancement in personalized healthcare technology, the usage of wearable devices for continuous monitoring and analysis of long-term biomedical signals, such as electrocardiography (ECG) has shown explosive growth. However, the existing ECG monitoring devices exhibit limited performance, such as they only store the ECG data, have low accuracy and their inability to perform event-by-event diagnosis at the place of data acquired. Therefore, the personalized healthcare demands an efficient method and point-of-care platform capable of providing real-time feedback to consumers as well as subjects. In this paper, a novel ECG signal analysis method using discrete cosine stockwell transform for feature extraction and artificial bee colony optimized least-square twin support vector machines as classifier is developed and prototyped using commercially available 32-bit microcontroller test platform. The prototype is evaluated under two schemes, i.e., the class and personalized schemes and validated on the benchmark MIT-BIH arrhythmia data. A higher overall accuracy of 96.14% and 86.5% respectively is reported by the prototype in the aforesaid two evaluation schemes than the existing works. The platform can be utilized as an early warning system in detecting abnormal ECG in home care environment to the state-of-art diagnosis.