The Potential for High-Priority Care Based on Pain Through Facial Expression Detection with Patients Experiencing Chest Pain - Hsiang Kao; Rita Wiryasaputra; Yo-Yun Liao; Yu-Tse Tsan; Wei-Min Chu; Yi-Hsuan Chen; Tzu-Chieh Lin; Chao-Tung Yang (Diagnostics)
Background and Objective: Cardiovascular disease (CVD), one of the chronic non-communicable diseases (NCDs), is defined as a cardiac and vascular disorder that includes coronary heart disease, heart failure, peripheral arterial disease, cerebrovascular disease (stroke), congenital heart disease, rheumatic heart disease, and elevated blood pressure (hypertension). Having CVD increases the mortality rate. Emotional stress, an indirect indicator associated with CVD, can often manifest through facial expressions. Chest pain or chest discomfort is one of the symptoms of a heart attack. The golden hour of chest pain influences the occurrence of brain cell death; thus, saving people with chest discomfort during observation is a crucial and urgent issue. Moreover, a limited number of emergency care (ER) medical personnel serve unscheduled outpatients. In this study, a computer-based automatic chest pain detection assistance system is developed using facial expressions to improve patient care services and minimize heart damage. Methods: The You Only Look Once (YOLO) model, as a deep learning method, detects and recognizes the position of an object simultaneously. A series of YOLO models were employed for pain detection through facial expression. Results: The YOLOv4 and YOLOv6 performed better than YOLOv7 in facial expression detection with patients experiencing chest pain. The accuracy of YOLOv4 and YOLOv6 achieved 80–100%. Even though there are similarities in attaining the accuracy values, the training time for YOLOv6 is faster than YOLOv4. Conclusion: By performing this task, a physician can prioritize the best treatment plan, reduce the extent of cardiac damage in patients, and improve the effectiveness of the golden treatment time.