This　paper　presents　an　electrocardiogram　(ECG)　feature　extraction　with　wavelet　algorithm　for　personal　healthcare　monitoring.　Developing　a　wireless　ECG　examination　system　employed　to　monitor　the　cardiovascular　disease　(CVD)　is　signification,　especially　uses　a　low-power　device　anywhere　and　anytime　detecting　the　real-time　ECG　signal　for　self-examination　applications.　The　continuous　time　Mexican-Hat　wavelet　transform　(CTMHWT)　algorithm　is　quickly　and　easily　applied　to　analyze　the　approximation　of　P-QRS-T　complex　fiducial　points.　The　database,　MIT-BIH　discrete　points,　is　adopted　to　efficiently　extract　the　ECG　signal　according　to　CTMHWT　algorithm　with　Matlab　simulator.　Moreover,　the　simulation　result　reveals　that　the　feature　extraction　of　ECG　signal　is　satisfied　to　the　required　error　range.