Due to the pressure from work load and daily life, there is an increase in geriatric depression and arrhythmia population. However, some people may not notice or have no idea about the symptom of depression and arrhythmia. More research input is needed to diagnose severity of depression and arrhythmia at an early stage. To help users examine their physical fitness and mental health condition before outpatient service, we apply data mining strategy to discover association rules from responded questionnaire, including geriatric depression, BAI, ASRM, and PSQI. To obtain informative analytical results, multitudes of simulations are performed on 25,000 data stored in our database. We also propose an effective heartbeat monitoring ECG real-time detection system for homecare service, which uses the ECG sensors and a wireless sensor network technology to detect the subject's heartbeats and their variations. In addition, the MIT-BIH database is used to analyze arrhythmia. A fuzzy model is proposed to discriminate between normal heartbeats and arrhythmia. Experimental results show that an average accuracy of 95.42% is achieved by the proposed system. This evidence verifies that the hybrid intelligent model is effective in medical related applications. 
Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis