Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Acquisition and Preprocessing
2.2. Hemodynamic Parameter Estimation Model
2.3. Hemodynamic Parameter Estimation Schemes
2.3.1. Scheme I: Five-Fold Cross-validation of Recordings within Subjects
2.3.2. Scheme II: Hemodynamic Parameter Estimation between Subjects
2.4. Performance Metrics
3. Results
3.1. Results of Scheme I (within Subject)
3.2. Results of Scheme II (across Subjects)
3.3. Results with Calibration for Scheme II
4. Discussions
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Record Index | No. of Cardiac Cycles | SBP (mmHg) (Min–Max) | DBP (mmHg) (Min–Max) | MRR (mmHg/s) (Min–Max) | MRD (mmHg/s) (Min–Max) | |
---|---|---|---|---|---|---|
Subject 1 | 1 | 72 | [128, 158] | [2, 8] | [1812, 1981] | [−1998, −1900] |
2 | 567 | [115, 231] | [−22, −4] | [1452, 7595] | [−4795, −1450] | |
3 | 587 | [106, 268] | [−25, −3] | [1467, 9080] | [−5781, −1546] | |
4 | 563 | [112, 264] | [−22, −3] | [1430, 8946] | [−5756, −1437] | |
5 | 523 | [101, 252] | [−22, −3] | [481, 8564] | [−5431, −365] | |
6 | 419 | [102, 254] | [−26, −6] | [1329, 7910] | [−4501, −1524] | |
7 | 512 | [138, 258] | [−47, 9] | [1383, 6522] | [−4633, −1722] | |
8 | 574 | [152, 258] | [−30, 6] | [1749, 6496] | [−4715, −1699] | |
9 | 533 | [157, 250] | [−28, 5] | [1841, 6312] | [−5031, −1116] | |
10 | 502 | [153, 238] | [−29, 2] | [107, 5778] | [−4321, −70] | |
11 | 558 | [97, 234] | [−18, 7] | [1732, 7502] | [−5140, −855] | |
12 | 578 | [77, 268] | [−34, 10] | [1810, 9181] | [−5896, −859] | |
13 | 510 | [92, 264] | [−33, 2] | [1711, 8946] | [−5845, −1010] | |
14 | 503 | [122, 239] | [−26, 0] | [1535, 7942] | [−5041, −1539] | |
15 | 528 | [117, 241] | [−25, −3] | [1465, 7981] | [−5070, −1503] | |
Subject 2 | 16 | 118 | [147, 160] | [−1, 14] | [2258, 273] | [−2552, −2166] |
17 | 142 | [144, 212] | [−14, 18] | [106, 6000] | [−3820, −175] | |
18 | 341 | [133, 218] | [−10, 12] | [1849, 6137] | [−3746, −1680] | |
19 | 315 | [137, 224] | [−8, 14] | [2664, 6097] | [−3654, −1676] | |
20 | 272 | [131, 213] | [−7, 11] | [2230, 5743] | [−3451, −1782] | |
21 | 247 | [122, 194] | [−7, 10] | [2594, 5624] | [−3390, −1384] | |
22 | 287 | [141, 235] | [−35, 17] | [699, 8198] | [−4793, −1143] | |
23 | 275 | [155, 272] | [−30, 17] | [2371, 7470] | [−4973, −2336] | |
24 | 254 | [164, 255] | [−24, 10] | [2753, 7091] | [−4257, −2287] | |
25 | 270 | [166, 250] | [−20, 9] | [3362, 6833] | [−4128, −2158] | |
26 | 439 | [162, 239] | [−22, 9] | [3293, 6731] | [−4290, −1974] | |
27 | 472 | [158, 231] | [−20, 9] | [3221, 6567] | [−4190, −1806] | |
28 | 38 | [149, 221] | [−17, 15] | [3094, 6137] | [−3847, −1799] | |
29 | 465 | [148, 218] | [−15, 14] | [3021, 6183] | [−3904, −1776] | |
Subject 3 | 30 | 50 | [122, 138] | [4, 8] | [849, 1205] | [−1242, −1051] |
31 | 164 | [137, 255] | [−12, 11] | [6, 7896] | [−3838, −189] | |
32 | 107 | [140, 248] | [−12, 12] | [−53, 7258] | [−3693, −97] | |
33 | 85 | [137, 236] | [−11, 20] | [−48, 6828] | [−3496, −238] | |
34 | 42 | [142, 231] | [−11, 17] | [2, 6705] | [−3159, 4] | |
35 | 85 | [124, 200] | [−5, 7] | [−35, 5717] | [−3330, 0] | |
36 | 71 | [134, 217] | [−3, 15] | [100, 5035] | [−2784, −20] | |
37 | 49 | [137, 202] | [−1, 8] | [160, 4247] | [−1950, −32] | |
38 | 90 | [164, 272] | [−11, 12] | [136, 8019] | [−4104, −212] | |
39 | 118 | [159, 266] | [−11, 12] | [−442, 8319] | [−3674, −246] | |
40 | 3 | [235, 263] | [−12, −8] | [7237, 8089] | [−3673, −343] | |
Total | 12,328 | [77, 272] | [−47, 20] | [106, 9181] | [−5896, −70] |
Residual Blocks | No. of Conv | Kernel Length | Kernel Number | Stride of Conv | Pooling Size |
---|---|---|---|---|---|
1st | #1 | 16 | 32 | 1 | 2 |
#2 | 16 | 32 | 2 | ||
2nd | #1 | 16 | 32 | 1 | 1 |
#2 | 16 | 32 | 1 | ||
3rd | #1 | 16 | 32 | 1 | 2 |
#2 | 16 | 32 | 2 | ||
4th | #1 | 16 | 32 | 1 | 1 |
#2 | 16 | 32 | 1 | ||
5th | #1 | 16 | 64 | 1 | 2 |
#2 | 16 | 64 | 2 | ||
6th | #1 | 16 | 64 | 1 | 1 |
#2 | 16 | 64 | 1 |
Performance Metrics | Subject 1 | |||
---|---|---|---|---|
SBP (mmHg) | DBP (mmHg) | MRR (mmHg/s) | MRD (mmHg/s) | |
ME | −3.41 | −0.1 | −39 | 59 |
MAE | 6.22 | 1.54 | 329 | 175 |
SD | 6.69 | 2.42 | 428 | 226 |
CC | 0.984 | 0.916 | 0.979 | 0.972 |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 |
95% CI for CC | 0.982–0.985 | 0.908–0.924 | 0.977–0.981 | 0.969–0.975 |
Performance metrics | Subject 2 | |||
SBP (mmHg) | DBP (mmHg) | MRR (mmHg/s) | MRD (mmHg/s) | |
ME | −0.12 | −0.4 | 37 | −0.9 |
MAE | 8.23 | 2.77 | 267 | 169 |
SD | 9.97 | 3.59 | 383 | 546 |
CC | 0.897 | 0.812 | 0.922 | 0.873 |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 |
95% CI for CC | 0.883–0.910 | 0.787–0.834 | 0.911–0.932 | 0.855–0.888 |
Performance metrics | Subject 3 | |||
SBP (mmHg) | DBP (mmHg) | MRR (mmHg/s) | MRD (mmHg/s) | |
ME | 3.81 | 0.20 | 48.8 | −49.2 |
MAE | 6.82 | 1.94 | 389 | 215 |
SD | 6.99 | 2.92 | 468 | 446 |
CC | 0.923 | 0.856 | 0.939 | 0.886 |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 |
95% CI for CC | 0.921–0.942 | 0.848–0.864 | 0.927–0.941 | 0.869–0.895 |
Performance Metrics | SBP (mmHg) | DBP (mmHg) | MRR (mmHg/s) | MRD (mmHg/s) | |
---|---|---|---|---|---|
Subject 1 | ME | −0.675 | 2.365 | 147 | 226 |
MAE | 13.14 | 5.04 | 776.5 | 414 | |
SD | 17.47 | 5.595 | 975.5 | 561 | |
CC | 0.908 | 0.715 | 0.937 | 0.919 | |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 | |
95% CI for CC | 0.903–0.914 | 0.699–0.731 | 0.933–0.941 | 0.914–0.924 | |
Subject 2 | ME | 7.895 | −4.905 | −419 | 73.5 |
MAE | 12.52 | 5.74 | 541 | 314 | |
SD | 14.71 | 4.605 | 549 | 378 | |
CC | 0.759 | 0.556 | 0.848 | 0.657 | |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 | |
95% CI for CC | 0.740–0.776 | 0.526–0.586 | 0.836–0.860 | 0.632–0.681 | |
Subject 3 | ME | −0.386 | −2.895 | −234 | −236 |
MAE | 13.90 | 5.09 | 792 | 412 | |
SD | 18.41 | 5.260 | 950 | 526 | |
CC | 0.885 | 0.693 | 0.903 | 0.862 | |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 | |
95% CI for CC | 0.870–0.896 | 0.686–0.706 | 0.896–0.910 | 0.852–0.871 |
Indicators | SBP (mmHg) | DBP (mmHg) | MRR (mmHg/s) | MRD (mmHg/s) | |
---|---|---|---|---|---|
Subject 1 | ME | 9.06 | 0.32 | 153 | 150 |
MAE | 14.180 | 3.970 | 773 | 379 | |
SD | 14.34 | 5.00 | 904.00 | 541.50 | |
CC | 0.94 | 0.72 | 0.95 | 0.94 | |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 | |
95% CI for CC | 0.934–0.942 | 0.705–0.736 | 0.949–0.955 | 0.931–0.939 | |
Subject 2 | ME | 6.93 | −0.98 | 54 | −37 |
MAE | 10.67 | 3.06 | 335 | 222 | |
SD | 12.595 | 3.995 | 446 | 294 | |
CC | 0.824 | 0.674 | 0.904 | 0.792 | |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 | |
95% CI for CC | 0.809–0.837 | 0.650–0.696 | 0.896–0.912 | 0.776–0.807 | |
Subject 3 | ME | 8.43 | 0.68 | 83 | 67 |
MAE | 12.87 | 4.46 | 547 | 306 | |
SD | 13.45 | 4.85 | 679 | 436 | |
CC | 0.854 | 0.71 | 0.913 | 0.832 | |
p-value | <<0.001 | <<0.001 | <<0.001 | <<0.001 | |
95% CI for CC | 0.849–0.867 | 0.696–0.721 | 0.906–0.922 | 0.826–0.847 |
References | Signal Sources | BP Type | Method | SBP Range (mmHg) | Performance | Performance Account for SBP Range |
---|---|---|---|---|---|---|
Tang et al. [18], 2017 | PCG | Left ventricular BP | Multi domain feature +SVM | same in this study | CC: 0.92 MAE: 6.86 mmHg SD: 8.96 mmHg | MAE: 3.5% SD: 4.6% |
Peng et al. [14], 2015 | PCG | Finger cuff BP | Fourier spectrum of second heart sound +SVM | about 90–140 | CC: 0.707 MAE: 4.339 mmHg SD:6.121 mmHg | MAE: 8.6% SD: 12.2% |
Kapur et al. [38], 2019 | PCG | Intra-arterial BP | Characteristics of S1 and S2 +ANN | 58–173 | 1. Without regularization: CC: 0.679 RMSE: 20.408 mmHg SD: 20 mmHg 2. Cuff BP regularization: CC: 0.964 RMSE: 7.305 mmHg SD: 7 mmHg | 1. RMSE: 17.7% SD: 17.4% 2. RMSE: 6.3% SD: 6.1% |
Esmaelpoor et al. [39], 2020 | PPG | Invasive BP | Deep neural network | 80–180 | MAE: 3.97 mmHg SD: 5.55 mmHg | MAE: 4.0% SD: 5.6% |
Yan et al. [21], 2019 | PPG + ECG | Arterial BP | Deep CNN | 80–180 | 1. Random split all subjects’ samples: MAE: 3.09 mmHg SD: 2.76 mmHg 2. Between subjects: MAE: 12.49 mmHg SD: 9.43 mmHg | 1. MAE: 3.1% SD: 2.8% 2. MAE: 12.5% SD: 9.4% |
Current study | PCG + PPG | Left ventricular BP | Deep learning model | 77–272 | 1. Within subject: CC: 0.94 MAE: 7.23 mmHg SD: 8.33 mmHg 2. Between subjects: MAE: 12.8 mmHg SD: 16.1 mmHg | 1. MAE: 3.7% SD: 4.3% 2. MAE: 6.6% SD: 8.3% |
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Mi, J.; Feng, T.; Wang, H.; Pei, Z.; Tang, H. Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study. Bioengineering 2024, 11, 842. https://doi.org/10.3390/bioengineering11080842
Mi J, Feng T, Wang H, Pei Z, Tang H. Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study. Bioengineering. 2024; 11(8):842. https://doi.org/10.3390/bioengineering11080842
Chicago/Turabian StyleMi, Jiachen, Tengfei Feng, Hongkai Wang, Zuowei Pei, and Hong Tang. 2024. "Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study" Bioengineering 11, no. 8: 842. https://doi.org/10.3390/bioengineering11080842