Tham khảo tài liệu 'biomedical engineering trends research and technologies part 13', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 470 Biomedical Engineering Trends Research and Technologies physiology. Unlike other methods like HRV the value of DFA was that it has a baseline value of one 1 like a standard body temperature 37 a standard blood pH and so on. Thus we thought DFA was a simple tool. One 1 is nonlinearly determined a healthy outcome resulting from complex interactions between the structure and function of molecules cells and organs. Thereby we hoped that DFA could determine the state of health numerically. DFA seemed to not only reflect the state of the heart itself but also the cardiac nervous system. We considered that DFA might be used to detect the onset of cardiac problems including disorders of the autonomic nervous system. In this chapter we provide empirical evidence of the practical usefulness of DFA and a new EKG amplification device that facilitates automatic DFA computation in practical use. The fluctuation analysis . DFA was a potentially helpful early detection tool as it revealed information that was not provided by EKG data. 2. Materials and methods Peak detection of the heartbeat Interval analysis requires detection of the precise timing of the heartbeat. A consecutive and perfect detection without miscounting is desirable. According to our preliminary test approximately 2 000 consecutive heartbeats are required to obtain a reliable scaling exponent computation. We thought that the longer recording time resulted in a more accurate diagnosis. However we found out that recording for longer than 2 000 beats was not helpful. We first reached this conclusion in model animal experiments. The ideal number of about 2 000 consecutive heartbeats is also applicable in human subjects. To detect the timing of the heartbeats both EKG recording and blood flow pulse recording are useful. Figure 1 shows an example of the premature ventricular contraction registered by both EKG and finger pulse recordings. Note the difference between Figure 1 A and B. Electrical excitation
