Automatic analysis of electrocardiogram signal enables the monitoring and diagnosis of the state of a patient 24 hours a day, 7 days a week and reporting any abnormally event to the patient himself or a doctor. In this thesis, an electrocardiogram parameter extract and analysis system is proposed. The system consists of three parts: wireless biomedical signal acquisition subsystem, real-time FPGA base heart beat detector and ECG analysis software algorithms. The wireless biomedical signal acquisition subsystem measures and digitalize ECG signal of a subject and transmit it to device which store and process the obtained signal. The ECG collected by the first part of the system can be employed as input of the second and third part of the system. The FPGA based algorithm in the second part can process digitalized ECG data in real-time with a high detection accuracy and low hardware consumption. The detection accuracy of the proposed hardware algorithm can achieve high than 99% while validating with MITBIH arrhythmia database. The output of the algorithm, QRS complex index, can be used in HRV analysis. The ECG analysis software consists of a high accuracy QRS complex detector, an ECG signal delineator which is able to extra ECG signal component waves, and a HRV analysis software kit based on the detection result of QRS complex detector. The data collected by the biomedical signal acquisition subsystem can apply to this software set directly. The performance of the software algorithms are evaluated in MITBIH arrhythmia database and QT database respectively. Detection accuracy up to 99.66% is achieved by the proposed software QRS detection algorithm.