An Android-based self-diagnostic electrocardiogram system using detrended fluctuation analysis was developed and experimentally verified for real-time ECG diagnostics on a smartphone.
An Android-based self-diagnostic ECG system using detrended fluctuation analysis was developed and experimentally validated for real-time mobile healthcare monitoring.
BACKGROUND: Cardiovascular diseases are the most common cause of death worldwide and are characterized by arrhythmia (i.e. irregular rhythm of heartbeat). Arrhythmia occasionally happens under certain conditions, such as stress. Therefore, it is difficult to be diagnosed using electrocardiogram (ECG) devices available in hospitals for just a few minutes. Constant diagnosis and monitoring of heartbeat is required to reduce death caused by cardiovascular diseases. OBJECTIVE: Mobile healthcare system has emerged as a potential solution to assist patients in monitoring their own heart condition, especially those who are isolated from the reference hospital. This paper proposes a self-diagnostic electrocardiogram system for mobile healthcare that has the capability to perform a real-time ECG diagnostic. METHODS: The self-diagnostic capability of a real-time ECG signal is achieved by implementing a detrended fluctuation analysis (DFA) method. The result obtained from DFA is used to display the patient's health condition on a smartphone anytime and anywhere. If the health condition is critical, the system will alert the patient and his medical practitioner for further diagnosis. RESULTS: Experimental results verified the validity of the developed ECG diagnostic application on a smartphone. CONCLUSION: The proposed system can potentially reduce death caused by cardiovascular diseases by alerting the patient possibly undergoing a heart attack.
Choo et al. (Sun,) conducted a other in Arrhythmia. Android based self-diagnostic electrocardiogram system was evaluated on Validity of the developed ECG diagnostic application. An Android-based self-diagnostic electrocardiogram system using detrended fluctuation analysis was developed and experimentally verified for real-time ECG diagnostics on a smartphone.