Digital Volume Pulse Measured at the Fingertip as an Indicator of Diabetic Peripheral Neuropathy in the Aged and Diabetic

Figure 1. A lead II electrocardiogram (ECG) obtained using the conventional method and synchronous volume pulse from an infrared photoplethysmography (PPG) sensor attached to the dominant index finger. The PPG-derived digital volume pulse (DVP) signals for certain time periods are shown. R-R interval: the period between consecutive ECG R waves; peak-to-peak interval (PPI): the period between the peaks of two consecutive volume pulses. (a) Healthy subject (age: 52 years), (b) overweight and elderly subject (age: 66 years), and (c) type 2 diabetic patient (age: 42 years).

Figure 2. DVP amplitudes of the dominant finger {Amp(1), Amp(2), …, Amp(n)} and peak-to-peak intervals (PPIs) of IMF6 {PPI(1), PPI(2), …, PPI(n − 1)} were simultaneously acquired from a six-channel ECG-based pulse wave velocity system [28].

Figure 3. (a) The four fluctuation patterns of length two and (b) the eight fluctuation patterns of length three, representing fluctuations of {Amp} and {PPI} time series. Here, “1” represents Amp(i + 1) up from Amp(i); “0” represents Amp(i + 1) down from Amp(i) for the {Amp} series; “1” represents PPI(i + 1) increased from PPI(i); “0” represents PPI(i + 1) decreased from PPI(i) for the {PPI} series.

Figure 4. The modified percussion entropy index (PEIPPI) computation flow chart. Amp: DVP amplitudes of the dominant finger; PPI: peak-to-peak interval of IMF6. The standard deviation of the added noise was set as α = 0.2, and the trial number of the ensemble N = 200 for ensemble empirical mode decomposition (EEMD). After two synchronized series, {Amp} and {PPI} series were acquired. The computational length of the series {Amp} and {PPI} was set as 1000. Taking into account baroreflex sensitivity (BRS) regulation, the binary sequence transformations for {Amp} and {PPI} were conducted. Subsequently, the proposed PEIPPI was computed as Equation (10).

Figure 5. Digital volume pulse (DVP) signals and their corresponding decomposed 6th intrinsic mode function (IMF6) from one representative subject in each group showing: (a) subject A: healthy elderly subject in group 1 (age: 55 years, HbA1c: 5.5%, WC: 73 cm, BMI: 22.7); (b) subject B: diabetic patient without peripheral neuropathy in group 2 (age: 71 years, HbA1c: 8.2%, WC: 94 cm, BMI: 26.5); (c) subject C: type 2 diabetic patient with peripheral neuropathy within 5 years in group 3 (age: 62 years, HbA1c: 8.5%, WC: 98 cm, BMI: 28.6). The peaks of DVP and IMF6 were in phase for subject A, {PPI} series in (2) were the same from DVP or from IMF6. Nevertheless, it was difficult to calculate exact PPI from DVP for subject B and subject C. (a–c) For all IMF6 values, exact PPI could be calculated.

Figure 6. All Bland–Altman plots demonstrated that PPI (DVP) series have a good agreement with PPI (IMF6) series for (a) group 1, (b) group 2, and (c) group 3. Group 1: healthy aged subjects; group 2: diabetic subjects; group 3: diabetic peripheral neuropathy patients. The mean difference and the limits of agreement (mean ± 1.96SD) are also represented.

Figure 7. (a) Correlation between PPI (IMF6) and PPI (DVP) for test subjects in group 1 (r = 0.52, p = 0.001); (b) Correlation between PPI (IMF6) and PPI (DVP) for test subjects in group 2 (r = 0.30, p = 0.001); (c) Correlation between PPI (IMF6) and PPI (DVP) for test subjects in group 3 (r = 0.37, p = 0.001). Group 1: healthy aged subjects; group 2: diabetic patients without DPN; group 3: diabetic peripheral neuropathy patients within 5 years. The regression line describes the 95% confidence interval.