A noninvasive and comprehensive method for continuous assessment of cerebral blood flow pulsation based on magnetic induction phase shift

Abstract

Cerebral blood flow (CBF) monitoring is of great significance for treating and preventing strokes. However, there has not been a fully accepted method targeting continuous assessment in clinical practice. In this work, we built a noninvasive continuous assessment system for cerebral blood flow pulsation (CBFP) that is based on magnetic induction phase shift (MIPS) technology and designed a physical model of the middle cerebral artery (MCA). Physical experiments were carried out through different simulations of CBF states. Four healthy volunteers were enrolled to perform the MIPS and ECG synchronously monitoring trials. Then, the components of MIPS related to the blood supply level and CBFP were investigated by signal analysis in time and frequency domain, wavelet decomposition and band-pass filtering. The results show that the time-domain baseline of MIPS increases with blood supply level. A pulse signal was identified in the spectrum (0.2-2 Hz in 200-2,000 ml/h groups, respectively) of MIPS when the simulated blood flow rate was not zero. The pulsation frequency with different simulated blood flow rates is the same as the squeezing frequency of the feeding pump. Similar to pulse waves, the MIPS signals on four healthy volunteers all had periodic change trends with obvious peaks and valleys. Its frequency is close to that of the ECG signal and there is a certain time delay between them. These results indicate that the CBFP component can effectively be extracted from MIPS, through which different blood supply levels can be distinguished. This method has the potential to become a new solution for non-invasive and comprehensive monitoring of CBFP.

Keywords: Cerebral blood flow; Magnetic induction phase shift; Noninvasive continuous assessment; Strokes.

Figure 1. Measurement principle of magnetic induction phase shift.

(A) Schematic diagram of MIPS. (B) Equivalent circuit diagram.

Figure 2. The measurement system for CBFP.

Figure 3. Measurement location of electromagnetic field strength.

Figure 4. Experiments setup based on self-made physical MCA model.

Figure 5. MIPS and ECG synchronously monitoring trial on healthy volunteers.

Figure 6. Flow chart of signal processing and analysis.

Figure 7. MIPS trend in each group.

(A) MIPS signal within 30 s. (B) MIPS trend as a function of flow rates and fitting line.

Figure 8. Time domain and frequency domain signal of MIPS.

(A) MIPS at the flow rate of 1,000 ml/h. (B) Spectrum of 1,000 ml/h group. (C) Spectrum of 0 ml/h.

Figure 9. Spectroscopy analysis results of MIPS signal in each flow rate.

Figure 10. Filtered MIPS signal in each flow rate.

Figure 11. Spectroscopy analysis results of filtered MIPS signal in each flow rate.

Figure 12. MIPS signal before and after processing.

(A) Original MIPS signal of No.1 healthy volunteer. (B) MIPS signal of No.1 healthy volunteer after removing baseline drift. (C) MIPS signal of No.1 healthy volunteer after high-pass and low-pass filtering (D) MIPS signal of No.1 healthy volunteer in frequency domain.

Figure 13. MIPS and ECG synchronously monitoring outcomes.

(A–D) represent the monitoring outcomes of four subjects respectively.