Pharmaceutical & medical
one per cent of the DC signal. These oscillating components contain the pulsatile activity in the capillaries. Any motion or other artifacts can easily override these signals and prevent an accurate reading. Over the years a great deal of time has been spent on methods to separate the signals from any artifacts. Often the methods have proven to be very complex and difficult to implement. It was for these reasons that this research was undertaken. The DPT algorithm resolves many of these challenges by employing a transform that requires a small number of samples yet achieves accurate measurements. Making the measurements in the period domain and spacing each bin by the sample period provides the required resolution. The period and amplitude information from the DPT could then be used to directly calculate heart rate and blood oxygen saturation without returning to the time domain.
CONCLUSION
Figure 15. PPG waveforms from the Raspberry Pi Zero oximeter prototype showing the red pulses on the top and infrared pulses on the bottom. The heart rate is approximately 58 bpm. Inverted waveforms are shown to more accurately represent the actual arterial pressure in the finger.
A Protype Oximeter System Using the SDPT Algorithm
Finally, a prototype oximeter was designed with an Arm microprocessor running with a bare metal operating system. The Raspberry Pi Zero was used as the computer platform with the MAX30102 integrated circuit as the sensor. The operating system and the sliding window DPT were implemented in the standard C language. Figure 13 shows the prototype. The entire oximeter was powered by a USB 3.0 connection. Two digital-to-analogue converters sent data, as deter- mined by the attending software, via a ribbon cable to a Tektronix DPO-4034 oscilloscope where they were plotted. The plots were then sent to a desktop computer using a network connec- tion. Figure 15 show the results from a single subject taken over a period of approximately 9 s, post a 10 s period to fill the recurrence buffer. The red and infrared DC signals were extracted from the raw signals using a first-order low-pass IIR filter, while the AC signals were extracted using a first-order high-pass IIR filter. See Figure 14. The time constant for these filters was set at approximately 1 s. The data was sampled at 100 SPS with the interrupt from the MAX30102 as the timing signal. The device’s output was in a 12-bit fixed point digital format for both the red and the infrared signals.
Once the red and infrared AC signals were extracted by the filters, they were processed by the DPT without any further signal preprocessing. The first harmonics of the spectrographic signals produce peaks as shown in Figure 16. The loca- tion of the data peaks on the abscissa determines the heart rate while the amplitudes of the red and
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infrared data peaks were used to directly calculate the SpO₂ using the ratio of ratios equations.
DISCUSSION
The raw optical signals generated by oximeters contain large steady DC components and small oscillating AC components that are approximately
Period domain analysis utilising an incremental DPT algorithm is an effective and efficient way to process periodic biomedical signals for spectral content. It provides the capabilities of frequency domain analysis, with advantages in implementa- tion. ADI’s MAX30101 integrated circuit sensor running the DPT algorithm was shown to be accu- rate enough to replace a Masimo oximeter in the practice of medicine.
Analog Devices
www.analog.com
Figure 16. The spectrums generated by Raspberry Pi Zero using the sliding window discrete period transform with an SpO2 value of 97% and a heart rate of 58 bpm. The cursor b (center vertical blue line) shows where the heart period is measured as 1.03 s. The rectangular signal at the top left points to where the 400 ms period is located on the abscissa and the rectangular signal at the top right points to where 2000 ms period is located on the abscissa.
January 2025 Instrumentation Monthly
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