Innovative Pulse Monitoring: Exploring Auscultation-Based Diagnostic Technique

The proposed system comprises three main components: the sensing transmitter consisting of acoustic sensors; the signal conditioning unit (noise filter circuits and signal amplification circuits); and the waveform display and storage unit.

The device for capturing pulse signals includes an electret condenser microphone, including a pre-polarized diaphragm. The pulse signal acquisition design uses an electret condenser microphone with a sensitivity of -51 dB ± 4 dB, an impedance of 2.2 k \(\Omega\) and a signal-to-noise ratio (SNR) of 45 dB. Electret microphones exhibit excellent sensitivity and exceptional signal-to-noise ratio, producing remarkable fidelity. The condenser microphone provides enhanced sensitivity with precise output. A 10 k \(\Omega\) pull-up resistor is incorporated to bias the microphone, along with a high-pass filter featuring a cutoff frequency of 0.16 Hz. The high-pass filter comprises a 10 µF DC blocking capacitor and a 100 k \(\Omega\) resistor. Figure 1(a) shows the schematic of the designed circuit, and Figure 1(b) shows the PCB layout diagram. This circuit exhibits low power consumption at 8 mA, allowing for the use of standard batteries as the power supply, hence mitigating interference from conventional 50 Hz/60 Hz AC power. Figure 1(c) shows the measurement site and the setup of the microphone sensor module, circuit assembly, and DC power supply. The initial wrist line serves as the horizontal axis, while the vertical axis is decided by manually sensing the pulse (marked as (a) and (b), respectively in Figure 1(c)).

The input signal is transmitted to the hardware unit, where a filter is developed to remove unwanted signals, primarily noise. The sensor was positioned at the radial artery for signal capture during experimental validation. The specifications of the sensor technology are as follows: Capacitance can be determined if the geometries of the conductors and the dielectric properties of the insulator are known. As sound waves enter the microphone's diaphragm, the separation between the two components alters due to the diaphragm's oscillations. The alteration in distance induces an electric potential that creates a current through resistors, leading to a reduction in voltage. The alteration in the spacing distance between the capacitor plates induces voltage generation. The creation of pulse waves induces vibrations in the form of acoustic waves that penetrate the diaphragm, resulting in alterations in the space between the two plates due to variations and vibrations. The acoustic signals are then set up to be converted to electrical signals later.

ADInstruments’ Power Lab (8/35) digitiser with 16-bit accuracy was used to convert the analog signals into discrete values. The data acquisition system aims to collect, store, and analyse data. The first input signal usually manifests as an analog voltage that undergoes continuous variations in amplitude over time. Signal conditioning involves amplifying and filtering the voltage when it enters the Power Lab via the input connections. The analog voltage is periodically sampled following signal conditioning. The signals were acquired at a sampling frequency of 1kHz. The visualisation and analysis of the acquired signal was done using MATLAB (MathWorks). Once the signal is acquired, preprocessing of the signal is done. A low pass filter of 40 Hz was applied to remove any high-frequency noise from the signal.

A total of 30 volunteers participated in the study, including 14 diabetic and 16 age-matched control (AMC) participants. The participants were asked to refrain from intense physical activity for a minimum of two hours prior to the measurement. All the participants were asked to relax in a sitting position while maintaining the hand at the level of the heart before recording the pulse. During data acquisition, the participants were requested to maintain a stable and comfortable hand position. Written informed consent was obtained from all the participants before starting the measurements. Pulse waveform data was recorded for each participant over 2 minutes.

Two cohorts of participants were compared utilising distinct PRV measurements. The PRV was assessed using time-domain, frequency-domain, and non-linear metrics to compare the differences between the two groups of participants. Various time-domain measures like Average PP, Median PP, SDPP, RMSSD, where Average PP is the average of the time difference between two successive peaks, median PP is the median of the time difference between two consecutive peaks, SDPP is the measure that represents the standard deviation of all PP intervals over a given period of time. It reflects the total variability in the pulse rate and provides an estimate of the overall PRV. RMSSD calculates the square root of the mean of the squared differences between successive PP intervals. It measures short-term variability and is primarily influenced by parasympathetic (vagal) activity. pPP50 refers to the percentage of successive PP intervals that differ by more than 50 ms. It provides a measure of the short-term variability relative to the total variability. Frequency-domain measures estimating the power spectrum of the PP interval time series, such as very low frequency (VLF) 0.0033-0.04 Hz, low frequency (LF) 0.04-0.15 Hz, and high frequency (HF) 0.15-0.40 Hz, were also measured. Poincare plots were used for non-linear PRV analysis and time and frequency-domain analysis. It shows the beat-to-beat pulse rate change as a scatter plot of each PP interval versus the previous one. It assesses the overall variability and the short-term (SD1) and long-term (SD2) components of PRV, which can provide insights into the autonomic nervous system's regulation of the heart.