Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times’ Measurement in Heart Disease Diagnosis and Monitoring
Abstract
1. Introduction
- Review the scientific literature regarding wearable systems for discreetly acquiring phonocardiograms and other biosignals for detecting heart diseases.
- Analysis of the main features that can be extracted from the joint analysis of ECG and PCG, highlighting their clinical significance.
- Development of an experimental setup for ECG and PCG’s simultaneous acquisition.
- Development of an adaptive segmentation and feature-extracting algorithm for the joint processing of ECG and PCG based on the segmentation of both biosignals and the extraction of the corresponding systolic times.
- Testing of the developed algorithm on acquired signal and its validation on data provided by an open-source dataset to confirm its reliability.
1.1. Literature Analysis of Advanced Systems to Acquire PCG and ECG
1.1.1. Advanced Devices and Algorithms for Detecting and Processing Heart Sounds
1.1.2. Systems and Algorithms for Joint Analysis of ECG and PCG Signals
2. Materials and Methods
2.1. Description of the Used Materials and Devices
2.2. Description of the Experimental Setup for Simultaneous Acquisition of ECG and PCG
1 #include <Filters.h> 2 FilterTwoPole filterTwoLowpass_pcg(100,1,0); // set the LP filter for PCG 3 FilterTwoPole filterTwoLowpass_ecg(15,0.707,0);// set the LP filter for ECG 4 void setup() { 5 Serial.begin(115200); 6 analogReadResolution(12); 7 analogReference(AR_VDD); 8 } 9 void loop() { 10 int raw_pcg= analogRead(A0); 11 int ecg = analogRead(A1); 12 int raw1_pcg= analogRead(A0); 13 float pcg = 0.5*(raw_pcg + raw1_pcg);//estimates the synchronous sample of PCG with respect to the ECG 14 filterTwoLowpass_pcg.input(pcg); //2nd-order LP filter for PCG 15 float filtered2 = filterTwoLowpass_pcg.output(); // provide the output of LP filter 16 filterTwoLowpass_ecg.input(ecg); // 2nd-order LP filter for ECG 17 float ecg_filtered = filterTwoLowpass_ecg.output(); //provide the output of the LP filter 18 Serial.print(millis()); 19 Serial.print(“,”); 20 Serial.print(filtered2); 21 Serial.print(“,”); 22 Serial.println(ecg_filtered); 23 delay(1); 24 } |
2.3. Description of the MATLAB Script for Segmenting PCG and ECG
for s = 1:1:window_number 19 t = data.b(slice_*(s-1) + 1:slice_*s,1); %time loading 20 ecg = detrend(data.b(slice_*(s-1) + 1:slice_*s,8)); %ecg loading 21 hpFilt = designfilt(‘highpassiir’, ... 22 ‘FilterOrder’, 4, ... 23 ‘HalfPowerFrequency’, 2, ... 24 ‘SampleRate’, fs); 25 % Apply the filter 26 ecg = filtfilt(hpFilt, ecg);% ecg filtering 27 pcg = detrend(data.b(slice_*(s-1) + 1:slice_*s,3)- Mean(data.b(slice_*(s-1) + 1:slice_*s,3))) |
48 A_S1_th = 0.1*max(pcg.^2);% parametric threshold on S1 sound 49 peak_S1 = find(pcg.^2 >= A_S1_th); %find S1 peak 50 S1_sign = generate_signal(peak_S1,length(pcg));%generate logic signal 51 figure(2); 52 fig_ = gcf; 53 fig_.WindowState = ‘maximized’; 54 subplot(2,1,1) 55 plot(t,(pcg.^2));hold on 56 xlabel(‘Time [s]’, ‘fontweight’, ‘bold’, ‘fontsize’,16); 57 ylabel(‘Amplitude [V^2]’, ‘fontweight’, ‘bold’, ‘fontsize’,16); 58 legend(‘PCG^2[V^2]’,’S1’,’S2’); 59 subplot(2,1,2); 60 plot(t, pcg);hold on 61 %xlim([0 11]) 62 S1_time = find_center_points(S1_sign);% find center of logic signal 63 B_S1_th = 0.5*mean(pcg(S1_time));% amplitude threshold on S1 detection 64 S1_time = S1_time(pcg(round(S1_time)) >= B_S1_th); % amplitude filtering on S1 65 differences = diff(S1_time); 66 threshold = 0.425*fs; 67 keep_index = [true,abs(differences) >= threshold]; %time filtering on S1 sounds 68 S1_time = S1_time(keep_index); |
% Detection of S2 sounds 70 %Determines ranges around the S1 sounds 71 for j = 1:length(S1_time) % Prevent index overflow at j + 1 72 % Ensure range indices are integers 73 if j < length(S1_time) 74 start_idx = round(S1_time(j)); 75 end_idx = round(S1_time(j + 1)); 76 elseif j==length(S1_time) 77 start_idx = round(S1_time(j)); 78 end_idx = length(t); 79 end 80 if start_idx >= end_idx || end_idx > length(pcg) 81 S2_time(j) = NaN; % Or handle appropriately 82 continue; 83 end 84 range = start_idx:end_idx; 85 [~,peak_S2] = findpeaks(pcg(range).^2);% find S2 sounds on squared PCG 86 differences1 = peak_S2; % Relative to start of range 87 indices_logical= (differences1 < 0.5*fs) & (differences1 > 0.125*fs); %time filtering 88 peak_S2_index = find(indices_logical); 89 if ~isempty(peak_S2_index) 90 peak_S2 = peak_S2(peak_S2_index); 91 S2_time(j) = mean(start_idx + peak_S2); % Convert back to full signal index 92 else 93 S2_time(j) = NaN; % No valid peaks found 94 end 95 end |
203 min_peak_distance_sec = 0.2; 204 min_peak_distance_samples = round(min_peak_distance_sec * fs); 205 inv_signal = -pcg;% inversion of the signal 206 S1_start_th = 0.2*max(-pcg);% adaptive threshold on S1 onset 207 [neg_peaks,neg_locs] = findpeaks(inv_signal,’MinPeakHeight’, S1_start_th,’MinPeakDistance’,min_peak_distance_samples) %find peaks on inverted signal; 208 % Find beginning of each negative peak 209 beginning_locs = zeros(size(neg_locs)); 210 for i = 1:length(neg_locs) 211 idx = neg_locs(i); 212 %Go backward until slope is no longer negative (signal starts falling) 213 while idx > 1 && pcg(idx) < pcg(idx - 1) 214 idx = idx - 1; 215 end 216 beginning_locs(i) = idx; 217 end 218 time_vector = (0:length(pcg)-1) / fs; 219 peak_times = time_vector(neg_locs); 220 S1_start = time_vector(beginning_locs) + t(1); |
%Find ECG peaks 270 R_peak_th = 0.5*max(ecg); 271 [R2,TR2] = findpeaks( ecg, t, ‘MinPeakHeight’, R_peak_th); 272 for i = 1:length(TR2)% starting from R-peak search for Q and S peaks 273 end_idx = find(t == TR2(i)); 274 if isempty(end_idx) 275 continue; % Skip if t doesn’t contain exact TR2(i) 276 end 277 start_idx = end_idx - 0.250*fs; 278 start_idx = max(start_idx, 1); % Ensure within bounds 279 start_idx1 = end_idx + 0.250*fs; 280 start_idx1 = min(start_idx1, length(t)); % Ensure within bounds 283 range = start_idx:end_idx; %range to left 284 range1 = end_idx:start_idx1;%range to right 286 if isempty(range) || isempty(range1) 287 continue; % Prevents findpeaks on empty vector 288 end 289 S_th = 0.2*max(-ecg); %threshold on S-peaks 290 range_time_span = t(range1(end)) - t(range1(1)); % time threshold for Q-peak search 291 min_peak_distance = min(0.05,0.9*range_time_span); % for safety 292 [Q2(i),TQ2(i)] = findpeaks(-ecg(range),t(range),’MinPeakHeight’, 0.1); %find Q-peak 293 [qs, ts] = findpeaks(-ecg(range1), t(range1), ... 294 ‘MinPeakHeight’, S_th, ‘MinPeakDistance’, min_peak_distance); |
3. Results
3.1. Extraction of the Systolic Times from PCG and ECG Processing
412 % Calculate systolic times 413 EMAT = (S1_start-TQ2)*1000; 414 PEP = (S1_time_-TQ2)*1000; 415 LVET = (S2_time_-S1_time_)*1000; 416 LVST = (S2_time_’-S1_start)*1000; 417 S1R = (S1_start-TR2)*1000; |
3.2. Validation of the Proposed Method for Systolic Time Estimation
4. Discussion
Analysis of Numerical Results for Validation of the Proposed Algorithm for Systolic Time Measurement
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ECG | Electrocardiography |
PCG | Phonocardiography |
EMAT | Electromechanical Activation Time |
PEP | Pre-Ejection Period |
LVET | Left Ventricular Ejection Time |
LVST | Left Ventricular Systolic Time |
S1 | First Heart Sound |
S2 | Second Heart Sound |
S3 | Third Heart Sound |
S4 | Fourth Heart Sound |
MEMS | Micro-Electromechanical Systems |
ECM | Electret Condenser Microphones |
MCU | Microcontroller Unit |
FPGA | Field-Programmable Gate Array |
FIR | Finite Impulse Response |
SNR | Signal-to-Noise Ratio |
PPG | Photoplethysmogram |
EICF | Exercised-Induced Cardiac Fatigue |
DL | Deep Learning |
DWT | Discrete Wavelet Transform |
LR-HSMM | Logistic Regression Hidden Semi-Markov Model |
FDC-FS | Feature-Based Fusion Model |
MFCC | Mel-Frequency Cepstrum Coefficient |
CNNs | Convolutional Neural Networks |
CAD | Coronary Artery Disease |
ResNet | Residual Neural Network |
AS | Aortic Stenosis |
MR | Mitral Regurgitation |
MS | Mitral Stenosis |
MVP | Mitral Valve Prolapse |
RF | Random Forest |
CVD | Cardiovascular Disease |
CHF | Chronic Heart Failure |
CTIs | Cardiac Time Intervals |
GKVDLNN | Gaussian Kaiming Variance-based Deep Learning Neural Network |
IEMD | Improved Empirical Mode Decomposition |
PEP | Pre-Ejection Period |
TDNN | Time Delay Neural Network |
CMRR | Common Mode Rejection Ratio |
LED | Light-Emitting Diode |
SoC | System on Chip |
IoT | Internet of Things |
BLE | Bluetooth Low Energy |
IVS | Interventricular Septum |
HFrEF | Heart Failure with Reduced Ejection Fraction |
AMI | Acute Myocardial Infarction |
LVEF | Left Ventricular Ejection Fraction |
MACE | Major Adverse Cardiovascular Events |
STEMI | ST-elevated myocardial infarction |
LVEDP | Left Ventricular End-Diastolic Pressure |
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Reference | Construction Technology | Materials Used | Application Area | Parameters/Extracted Signals | Sensitivity |
---|---|---|---|---|---|
J. Cui et al. [15] | Cantilever piezo-resistors | Si, SiO2, low B ion, high B ion, Au | Thorax | PCG signal voltage (V) | 189.5 dB @ 500 Hz |
T. Wang. et al. [16] | Capacitive electret microphone | Layer electret | Thorax | PCG signal array | N. A. |
S. Lee et al. [17] | MEMS microphone and PPG sensor | Silicon | Finger and chest | PPG and PCG signals simultaneously | N. A. |
Reference | Approach/Model | Output Classes | Input Features | Accuracy |
---|---|---|---|---|
C. Yin et al. [18] | ResNet and attention module | Resting/post-exercise | Durations of segments S1, S2, S3, S4 | 98% |
S. Behera et al. [19] | DT, RF, SVM, KNN | Severe, Mild AS, Mild, Moderate MS, Healthy | DWT, WPT, and PWPT features | 99.2% |
Z. Tariq et al. [20] | FDC-FS | Multi-classification CVD (normal, AS, MR, MS, MVP) | Multi-features images | 97% |
Y. Huang et al. [21] | Clustering, feature fusion, RF | Normal/CAD | MFCC and PCG signal segments | 96.05% |
H. Li et al. [22] | CNN-1D CNN-2D | Normal/CAD | Signals, spectral images, and time–frequency images of ECG and PCG | 96.51% |
S. Ma et al. [23] | CNN-1D | Normal/VHD | Features generated from convolutional layers | 99.4% |
H. K. Alkahtani [24] | CNN-1D | Normal/VHD | Features generated from convolutional layers | 98.56% |
Reference | PCG Acquisition Channels | Type of PCG Sensor | Application Area | Additional Signals | Algorithm | Type of Measurement/Classification |
---|---|---|---|---|---|---|
Y. Rong et al. [25] | 12 in total (6 heart-sensor microphones and 6 background noise microphones) | N. A. | Chest | ECG | CNN-based CVD classifier using both the PCG and ECG signals | Classification Normal/CVD |
N. Giordano et al. [26] | 48 | Electret microphone and MIMU | Chest | ECG | - | Classification Normal/CVD |
P. Jyothi et al. [28] | 1 | N. A. | N. A. | ECG | GKVDLNN | Classification arrhythmias, ischemic, prolapsed mitral valve, normal, valvular heart diseases |
S. Gnanapira-kasam et al. [29] | 1–4 | Littmann® Electronic Stethoscope Model 3200 | N. A. | ECG | CNN-LSTM | Abnormal/Normal ECG, PCG, or both ECG and PCG |
M. Klum et al. [30] | 1 | Digital stethoscope | Chest | ECG | TDNN | Regression Systolic times |
Pickup range | 0–20,000 Hz |
Sensitivity | −52 dB |
Noise Level | Less than −45 dB |
Magnification | 4× |
Bias voltage | DC 2.6–3 V |
Signal | Analog |
Signal range | 0–200 mV |
Sampling rate | 44.1 KHz |
Interface standard | National Standard or American Standard |
Systolic Times | Measured Systolic Times | Typical Range |
---|---|---|
EMAT [ms] | 74.35 | 50–120 [31] |
PEP [ms] | 89.00 | 50–120 [32] |
LVET [ms] | 244.39 | 150–350 [33] |
LVST [ms] | 258.60 | 150–350 [34] |
Pathology | EMAT [ms] | PEP [ms] | LVET [ms] | LVST [ms] | Clinical Insight/Thresholds |
---|---|---|---|---|---|
Acute myocardial infarction (AMI) [29] | Increased (EMAT% > 12.1%) | Increased (%PEP from 16.23 to 17.29) | Reduced (%LVET from 30.63 to 29.85) | Reduced (%LVST from 38.07 to 37.38 | Associated with lower LVEF, increased LVEDV/LVESV, and higher MACE risk. |
Depressed left ventricular ejection fraction (dLVEF) [38] | Increased (EMAT ≥ 104 ms) | - | - | - | EMAT ≥ 104 ms predicts LVEF < 50%. |
Heart failure with reduced EF (HFrEF) [39] | Increased (% EMAT > 13.8%) | - | - | - | % EMAT > 13.8% predicts LVEF < 50%. |
Left ventricular dysfunction [40] | ≥15 | - | - | - | EMAT/LVST ≥ 0.40 is an indicator of impaired systolic performance. |
Ischemic heart disease [41] | - | Increased (r = −0.69 with EF) | Reduced (r = 0.67 with EF) | - | PEP/LVET is correlated with the Ejection Fraction (EF) and Stroke index with correlation r = −0.77 and −0.72. |
Mitral valve disease [41] | - | Increased (r = −0.69 with EF) | Reduced (r = 0.67 with EF) | - | PEP/LVET is correlated with the Ejection Fraction (EF) with a correlation r = −0.87. |
Metric | S1–Q Interval | S1–R Interval |
---|---|---|
Mean difference [ms] | −4.85 | −1.20 |
LoAs [ms] | −3.42/−6.09 | −0.55/1.85 |
Mean absolute error [ms] | 4.85 | 1.20 |
Maximum absolute error [ms] | 6.62 | 2.20 |
Mean relative error [%] | 8.65 | 12.05 |
Maximum relative error [%] | 10.39 | 18.60 |
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De Fazio, R.; Cascella, I.; Yalçınkaya, Ş.E.; De Vittorio, M.; Patrono, L.; Velazquez, R.; Visconti, P. Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times’ Measurement in Heart Disease Diagnosis and Monitoring. Sensors 2025, 25, 4220. https://doi.org/10.3390/s25134220
De Fazio R, Cascella I, Yalçınkaya ŞE, De Vittorio M, Patrono L, Velazquez R, Visconti P. Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times’ Measurement in Heart Disease Diagnosis and Monitoring. Sensors. 2025; 25(13):4220. https://doi.org/10.3390/s25134220
Chicago/Turabian StyleDe Fazio, Roberto, Ilaria Cascella, Şule Esma Yalçınkaya, Massimo De Vittorio, Luigi Patrono, Ramiro Velazquez, and Paolo Visconti. 2025. "Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times’ Measurement in Heart Disease Diagnosis and Monitoring" Sensors 25, no. 13: 4220. https://doi.org/10.3390/s25134220
APA StyleDe Fazio, R., Cascella, I., Yalçınkaya, Ş. E., De Vittorio, M., Patrono, L., Velazquez, R., & Visconti, P. (2025). Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times’ Measurement in Heart Disease Diagnosis and Monitoring. Sensors, 25(13), 4220. https://doi.org/10.3390/s25134220