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Real-time vectorcardiography simulator system.

Code ↔ Paper

3 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

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  1. [1] § Results › Technical validation ↔ Supporting_Jupyter_Info/DTW_ECG_Reproducibility_V1.ipynb, lines 75 to 192 · score 0.90 · accumulated cost matrix, normalized DTW distance, optimal warping, raw signals, 1500 ms, map · Report a mismatch
  2. [2] § Results › Technical validation ↔ Supporting_Jupyter_Info/DTW_ECG_Reproducibility_V1.ipynb, lines 75 to 192 · score 0.74 · Accumulated cost matrix, optimal warping, Normalized DTW, clarity, alignment, metrics · Report a mismatch
  3. [3] § Materials and methods › Hardware design and implementation › Technical validation. ↔ Supporting_Jupyter_Info/DTW_ECG_Reproducibility_V1.ipynb, lines 31 to 73 · score 0.65 · Norwegian endurance athlete, physical signal, ECG database, v1, Physionet, waveforms · Report a mismatch

Paper

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The authors' code

Jupyter notebook · 341 lines · 13 KB · MIT · 3 matches

  1. # %% [markdown]
  2. # # Supporting DTW_ECG Reproducibility
  3. # #### DTW analysis and figures, No additional analyses are introduced.
  4. # #### This program compares a synthetically generated signal and a real ECG signal for academic demonstration only. Not for clinical use.
  5. # ## Load simulated data
  7. # %%
  8. import numpy as np
  9. import matplotlib.pyplot as plt
  10. from scipy.signal import resample
  12. # 1. Load the data acquired from the graphic program (shape must be (n_samples, 3) LI, LII, LIII)
  13. sym_raw_data = np.load('mimic_ath16.npy')
  15. # 2. Resample data if is necessary. The shape of the array must be the same of the reference data array
  16. #single_wave_data = sym_raw_data[915:1220]
  17. #sym_data = resample(sym_raw_data, 360, axis=0)
  18. sym_data = sym_raw_data #Comment this line if a resaple was necessary
  20. # 3. test loaded data
  21. plt.figure(figsize=(7,2))
  22. plt.plot(sym_data[:, 0], linewidth=2)
  23. plt.grid(True, linestyle='--', alpha=0.7)
  24. plt.show()
  26. # 4. Check the shape to confirm
  27. print(f"Data shape: {sym_data.shape}")
  28. print(f"Data type: {sym_data.dtype}")
  29. print("This plot must show a single ECG wave")
  31. # %% [markdown]
  32. # ## Load reference data
  33. #
  34. # ##### ECG wave from Physionet (ath_016 snap from 850 to 2000 record) need (ath_016.hea, ath_016.dat files)
  35. # ##### SSingstad BJ. PhysioNet. 2022 [cited 2025 Mar 17]. Norwegian Endurance Athlete ECG Database v1.0.0. Available from: https://www.physionet.org/content/norwegian-athlete-ecg/1.0.0/
  37. # %%
  38. import wfdb
  39. from scipy.signal import butter, filtfilt
  41. # 1. Read the record
  42. # Note: 'record_name' should not include the extension (.hea or .dat) in the same jupyter nottebook folder
  43. record = wfdb.rdrecord('ath_016', sampfrom=850, sampto=2000, channels=[0, 1, 2])
  45. # 2. Extract the physical signal (the mV values)
  46. raw_data = record.p_signal
  47. ecg_data_b = raw_data[280:640] #sellect a single ECG wave
  49. # 3. remove the zero offsets if exist
  50. ### The ECG isoelectric line provides a zero-level reference that improves waveform comparison.
  51. ecg_data_b[:, 0] = ecg_data_b[:, 0]+0.02
  52. ecg_data_b[:, 1] = ecg_data_b[:, 1]+0.42
  53. ecg_data_b[:, 2] = ecg_data_b[:, 2]+0.33
  55. # 4. Filter the data
  56. sampling_rate = 500 #sampling_rate = 500.0 # Assume a sample rate of 1Hz, modify as needed
  57. num_samples = 300
  58. cutoff_frequency = 60 # Adjust as needed for desired cutoff frequency
  59. order = 4
  60. scale_factor = sampling_rate
  61. b, a = butter(order, cutoff_frequency, btype='low', fs=sampling_rate)
  62. filt_ref_data = filtfilt(b, a, ecg_data_b[:], axis=0) # Apply for denoising and improve the shape of the P signal
  64. # 5. test loaded data
  65. plt.figure(figsize=(7,2))
  66. plt.plot(filt_ref_data[:, 0], linewidth=2)
  67. plt.grid(True, linestyle='--', alpha=0.7)
  68. plt.show()
  70. # 6. Check the shape to confirm
  71. print(f"Data shape: {filt_ref_data.shape}")
  72. print(f"Data type: {filt_ref_data.dtype}")
  73. print("The reference data and the simulated data must match in dimensionality.")
  75. # %%
  76. import matplotlib.pyplot as plt
  77. import numpy as np
  78. from scipy import signal
  79. from scipy.spatial.distance import cdist
  81. # 1. Data Selection
  82. # Assuming lead index 1 (Lead II) for both
  83. s1 = filt_ref_data[:, 0] # Reference (Database)
  84. s2 = sym_data[:, 0] # User Generated
  86. # 2. Calculate Local Distance Matrix (Euclidean)
  87. # Compares every point in s1 with every point in s2
  88. cost_matrix = cdist(s1.reshape(-1, 1), s2.reshape(-1, 1), metric='euclidean')
  90. # 3. Compute Accumulated Cost Matrix
  91. # This is the core DTW logic to find the path of least resistance
  92. acc_cost_matrix = np.zeros(cost_matrix.shape)
  93. acc_cost_matrix[0, 0] = cost_matrix[0, 0]
  95. # Initialize first row and column
  96. for i in range(1, cost_matrix.shape[0]):
  97. acc_cost_matrix[i, 0] = cost_matrix[i, 0] + acc_cost_matrix[i-1, 0]
  98. for j in range(1, cost_matrix.shape[1]):
  99. acc_cost_matrix[0, j] = cost_matrix[0, j] + acc_cost_matrix[0, j-1]
  101. # Fill the matrix using the recursive rule
  102. for i in range(1, cost_matrix.shape[0]):
  103. for j in range(1, cost_matrix.shape[1]):
  104. acc_cost_matrix[i, j] = cost_matrix[i, j] + np.min([
  105. acc_cost_matrix[i-1, j], # Insertion
  106. acc_cost_matrix[i, j-1], # Deletion
  107. acc_cost_matrix[i-1, j-1] # Match
  108. ])
  110. # 4. Backtracking: Find the Optimal Warping Path
  111. path = []
  112. i, j = acc_cost_matrix.shape[0] - 1, acc_cost_matrix.shape[1] - 1
  113. path.append((i, j))
  115. while i > 0 or j > 0:
  116. options = []
  117. if i > 0: options.append((acc_cost_matrix[i-1, j], (i-1, j)))
  118. if j > 0: options.append((acc_cost_matrix[i, j-1], (i, j-1)))
  119. if i > 0 and j > 0: options.append((acc_cost_matrix[i-1, j-1], (i-1, j-1)))
  121. # Choose the neighbor with the lowest accumulated cost
  122. min_val, (i, j) = min(options, key=lambda x: x[0])
  123. path.append((i, j))
  125. path = np.array(path[::-1]) # Reverse to start from (0,0)
  127. # 5. Statistical Similarity Metrics
  128. # Calculate Cross-Correlation to find natural shift
  129. correlation = signal.correlate(s1 - np.mean(s1), s2 - np.mean(s2), mode='full')
  130. lags = signal.correlation_lags(len(s1), len(s2), mode='full')
  131. found_lag = lags[np.argmax(correlation)]
  133. # Calculate Similarity Percentage (Pearson Correlation) at Lag 0
  134. similarity_pct = np.corrcoef(s1, s2)[0, 1] * 100
  136. # Calculate DTW Metrics
  137. dtw_distance = acc_cost_matrix[-1, -1]
  138. norm_dtw = dtw_distance / (len(s1) + len(s2))
  140. # --- Visualization ---
  141. # --- Global font Settings ---
  142. plt.rcParams.update({
  143. 'font.size': 18, # Base font size
  144. 'axes.titlesize': 18, # Subplot titles
  145. 'axes.labelsize': 18, # X and Y labels
  146. 'xtick.labelsize': 14, # X-axis tick numbers
  147. 'ytick.labelsize': 14, # Y-axis tick numbers
  148. 'legend.fontsize': 14, # Legend text
  149. 'figure.titlesize': 24 # Main figure title
  150. })
  152. %config InlineBackend.figure_format = 'retina'
  153. plt.figure(figsize=(17, 5))
  155. # Plot A: Raw Signals
  156. plt.subplot(1, 3, 1)
  157. plt.plot(s1, label='Database (Ref)', color='blue', linewidth=1.5)
  158. plt.plot(s2, label='User (Query)', color='black', linewidth=1.5, alpha=0.8)
  159. plt.title("a. Raw Signals (Natural State)")
  160. plt.xlabel("Samples")
  161. plt.ylabel("Amplitude (mV)")
  162. plt.legend()
  163. plt.grid(alpha=0.2)
  165. # Plot B: Alignment
  166. plt.subplot(1, 3, 2)
  167. for (map_x, map_y) in path[::15]: # Sample every 15 for visual clarity
  168. plt.plot([map_x, map_y], [s1[map_x], s2[map_y] + 1.5], color='gray', alpha=0.3)
  169. plt.plot(s1, label='Database', color='blue')
  170. plt.plot(s2 + 1.5, label='User (+1.5)', color='black')
  171. plt.title("b. Dynamic Time Warping Alignment")
  172. plt.xlabel("Samples")
  173. plt.legend(loc='lower right')
  175. # Plot C: Cost Matrix & Summary
  176. plt.subplot(1, 3, 3)
  177. plt.imshow(acc_cost_matrix.T, origin='lower', cmap='viridis', aspect='auto')
  178. plt.plot(path[:, 0], path[:, 1], 'w-', linewidth=2, label='Optimal Path')
  179. plt.title(f"c. Accumulated Cost\nSimilarity: {similarity_pct:.2f}% | Norm DTW: {norm_dtw:.4f}")
  180. plt.colorbar(label='Cost Value')
  181. plt.xlabel('Database Index')
  182. plt.ylabel('User Index')
  184. plt.tight_layout()
  185. #plt.savefig('ecg_similarity.svg', dpi=300, bbox_inches='tight') #This save an image of the plot
  186. plt.show()
  188. print(f"Summary Report:")
  189. print(f"----------------")
  190. print(f"Pearson Similarity: {similarity_pct:.2f}%")
  191. print(f"Natural Lag: {found_lag} samples")
  192. print(f"Normalized DTW: {norm_dtw:.4f}")
  194. # %% [markdown]
  195. # ## Original Vs Warped Alignment and Similarity
  197. # %% [markdown]
  198. # ##### This ensures the correlation measures morphology (the shape of the wave) rather than just timing, since the ECG signals often have slight phase shifts in the timing of the P, QRS, and T waves.
  199. # ##### (V. Tuzcu and S. Nas, 'Dynamic time warping as a novel tool in pattern recognition of ECG changes in heart rhythm disturbances,' 2005 IEEE International Conference on Systems, Man and Cybernetics, Waikoloa, HI, USA, 2005, pp. 182-186 Vol. 1, doi: 10.1109/ICSMC.2005.1571142.
  201. # %%
  202. %config InlineBackend.figure_format = 'retina'
  203. from fastdtw import fastdtw
  204. from scipy.stats import pearsonr
  205. from matplotlib.ticker import MultipleLocator, FuncFormatter
  207. threshold = 0.9 # Similarity threshold, a warped correlation > 0.85 is typically considered a "high fidelity"
  209. plt.rcParams.update({
  210. 'font.size': 14, # Base font size
  211. 'axes.titlesize': 14, # Subplot titles
  212. 'axes.labelsize': 10, # X and Y labels
  213. 'xtick.labelsize': 8, # X-axis tick numbers
  214. 'ytick.labelsize': 8, # Y-axis tick numbers
  215. 'legend.fontsize': 8, # Legend text
  216. 'figure.titlesize': 12 # Main figure title
  217. })
  219. fig, axs = plt.subplots(3, 2, figsize=(10, 7), sharex=True, sharey=True)
  220. summary_data = []
  222. leads = ['Lead I', 'Lead II', 'Lead III']
  223. colors = ['teal', 'darkorange', 'crimson']
  225. for i in range(3):
  226. # 1. Plot - Side-by-Side Comparison
  227. # --- Column 0: Synthetic Data (symu_data) ---
  228. axs[i, 0].plot(sym_data[:, i], label='Synthetic Data', color=colors[i], linewidth=1)
  229. axs[i, 0].plot(filt_ref_data[:, i], label='Real Data', color='black', linewidth=1)
  230. axs[i, 0].set_ylabel(f'{leads[i]}\n(mV)')
  231. axs[i, 0].grid(True, linestyle='--', alpha=0.6)
  232. axs[i, 0].legend(loc='lower right')
  234. # 2. Prepare data
  235. ref = filt_ref_data[:, i]
  236. query = sym_data[:, i]
  238. # 3. Compute DTW Alignment
  239. distance, path = fastdtw(ref, query, dist=lambda x, y: np.abs(x - y))
  241. # 4. "Warp" the signals using the path to align them in time
  242. ref_aligned = np.array([ref[p[0]] for p in path])
  243. query_aligned = np.array([query[p[1]] for p in path])
  245. # 5. Calculate Warped Correlation
  246. #Pearson correlation is inherently invariant to amplitude scaling.
  247. correlation, _ = pearsonr(ref_aligned, query_aligned)
  249. is_correlated = "Yes" if correlation >= threshold else "No"
  250. summary_data.append([leads[i], distance, correlation, is_correlated])
  252. # 6. Plotting
  253. # We plot against a generic index because the path changes the 'time' scale
  254. axs[i, 1].plot(ref_aligned, label=f'Real {leads[i]} (Aligned)', color='black', alpha=0.5)
  255. axs[i, 1].plot(query_aligned, label=f'Sym {leads[i]} (Aligned)', color=colors[i], linestyle='--')
  257. axs[i, 1].set_title(f"{leads[i]}: Warped Correlation = {correlation:.3f} ({is_correlated})")
  261. axs[i, 0].xaxis.set_major_locator(MultipleLocator(100)) # Major ticks every 100 points
  262. axs[i, 0].xaxis.set_minor_locator(MultipleLocator(20)) # Minor ticks every 20 points
  263. axs[i, 0].yaxis.set_major_locator(MultipleLocator(0.6)) # Major ticks every 5 units
  264. axs[i, 0].yaxis.set_minor_locator(MultipleLocator(0.15)) # Minor ticks every 1 unit
  266. axs[i, 0].grid(which='major', color='black', linestyle='--')
  267. axs[i, 0].grid(which='minor', color='#CCCCCC', linestyle=':')
  268. axs[i, 0].grid(True, which='both')
  271. axs[i, 1].legend(loc='lower right')
  272. #axs[i, 1].grid(True, alpha=0.3)
  274. # Set titles for the columns
  275. axs[0, 0].set_title('Original Signals')
  276. #axs[0, 1].set_title('Real Filtered Signal', fontweight='bold')
  278. # Set shared X-axis label
  279. axs[2, 0].set_xlabel('Samples')
  280. axs[2, 1].set_xlabel('Elastic time shifting, to compare P, QRS and T waves')
  282. plt.style.use('seaborn-v0_8-whitegrid')
  283. plt.savefig('ecg_plot_three_leads.svg', dpi=300, bbox_inches='tight')
  284. plt.tight_layout()
  285. plt.show()
  287. # 7. --- Print Final Analysis ---
  288. print(f"{'Lead':<10} | {'DTW Dist':<12} | {'Warped Corr':<12} | {'Status'}")
  289. print("-" * 55)
  290. correlated_count = 0
  291. for row in summary_data:
  292. print(f"{row[0]:<10} | {row[1]:<12.2f} | {row[2]:<12.4f} | {row[3]}")
  293. if row[3] == "Yes":
  294. correlated_count += 1
  296. print(f"\nResult: {correlated_count} out of 3 leads are highly correlated.")
  298. # %% [markdown]
  299. # ## Dynamic Time Warping Alignment of LI, LII, LII
  300. # #### Visualisation of the three leads using fastdtw 'Python' library
  302. # %%
  303. fig, axs = plt.subplots(1, 3, figsize=(15, 4.5), sharex=True, sharey=True)
  304. summary_data = []
  306. leads = ['Lead I', 'Lead II', 'Lead III']
  307. colors = ['teal', 'darkorange', 'crimson']
  309. for i in range(3):
  311. # 2. Prepare data
  312. ref = filt_ref_data[:, i]
  313. query = sym_data[:, i]
  314. # 3. Compute DTW Alignment
  315. distance, path = fastdtw(ref, query, dist=lambda x, y: np.abs(x - y))
  317. # 4. "Warp" the signals using the path to align them in time
  318. ref_aligned = np.array([ref[p[0]] for p in path])
  319. query_aligned = np.array([query[p[1]] for p in path])
  321. # Assuming you already ran: distance, path = fastdtw(ref_lead, made_lead)
  322. # Extract the path indices
  323. path_x = [p[0] for p in path]
  324. path_y = [p[1] for p in path]
  326. # 1. Plot the Warping Path
  327. axs[i].plot(path_x, path_y, color='blue', linewidth=2, label='Warping Path')
  329. # 2. Plot a Reference Diagonal (Perfect match line)
  330. axs[i].plot([0, 320], [0, 320], color='red', linestyle='--', alpha=0.5, label='Ideal Diagonal')
  332. # Labeling for your paper
  333. axs[i].set_title(f"{leads[i]}")
  334. axs[i].legend(loc='lower right')
  335. #axs[i].set_title("DTW Warping Path: Natural vs. Synthetic ECG")
  336. axs[i].set_xlabel("Reference Signal Index (PhysioNet)")
  338. axs[i].grid(True, alpha=0.3)
  339. axs[i].legend()
  340. axs[0].set_ylabel("Synthetic Signal Index")
  341. plt.show()

DTW_ECG_Reproducibility_V1.ipynb at commit 369ddbe, under MIT · at the source

Overview

Authors: William R Rodriguez1, Hernan A Bernal2, Diana Patricia Amador-Munoz3
ORCID iDs: Hernan A Bernal, Diana Patricia Amador-Munoz
  1. Department of Electronic Engineering, Faculty of Engineering, Pontificia Universidad Javeriana, Bogota, Colombia
  2. School of Medicine and Health Sciences, biomedical engineering. Universidad del Rosario, Bogota, Colombia
  3. School of Medicine and Health Sciences, Neurovitae research Center, Universidad del Rosario, Bogota, Colombia
Institutions: Pontificia Universidad Javeriana (Colombia); Universidad del Rosario (Colombia)
Journal: PloS one, volume 21, issue 3, article e0345612
Dates: received 22 October 2025; accepted 6 March 2026; published online 31 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pone.0345612 · PMID 41915705 · PMCID PMC13037978 · OpenAlex W7147582804
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: other (modality), human (organism), methods / tools (subfield)
Methods: Smoothing, state filtering, decompositions, Evoked potentials
MeSH: Computer Simulation*, Vectorcardiography*, Electrocardiography, Humans, Pilot Projects, Signal Processing, Computer-Assisted, Software (* major topic)
Topic: ECG Monitoring and Analysis (Cardiology and Cardiovascular Medicine, Medicine), according to OpenAlex
Funding: Universidad del Rosario; Pontificia Universidad Javeriana
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

Vectorcardiography provides a spatial orientation and magnitude of the electrical activity of the heart, but its complexity and lack of interactive resources have limited its application in medical and bioengineering education. To bridge this pedagogical gap, we developed a low-cost, real-time vectorcardiography simulator integrating a physical wet-lab interface and digital signal processing. The hardware setup consists of a conductive medium and electrodes, enabling users to manually simulate a cardiac vector and observe the resultant electrophysiological signal consequences in real time. The system was validated through three complementary methods: theoretical conformity analysis, emulation of real ECG data from database, and user-driven waveform generation tests. A pilot study with medical students and instructors provided empirical evidence of the educational value of the device, indicating that the active, hands-on nature of the system might foster deeper cognitive engagement and facilitates the integration of complex electrophysiological concepts. By providing open-source software and cost-effective hardware, this simulator offers a scalable solution to enhance cardiac electrophysiology education and promote the broader adoption of VCG in clinical practice.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Zenodo 18486986

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
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Found in: DataCite
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
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5 files
At the source: doi.org/10.5281/zenodo.18486986

hbprojects/vectorial-plot-rpi-pico

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: 369ddbe2412ce6beae17c29c457368a174ea957b, 23 March 2026
Languages: Python (3), Jupyter (1)
Size: 19 files, 4 scripts
Software Heritage: not archived
Found in: DataCite
Holds: README, license file, 1 notebook
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), SciPy (1 file), WFDB Python (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
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6 files
At the source: github.com/HBprojects/Vectorial-Plot-Rpi-Pico/tree/Vectocardiography

Zenodo 18445797

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Size: 1 file
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Found in: DataCite
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
5 files
At the source: doi.org/10.5281/zenodo.18445797

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Data

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All relevant data are within the manuscript and its Supporting Information files.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 7 MeSH terms, 2 funders, 34 references.

Cite

This paper

Rodriguez, W. R., Bernal, H. A., & Amador-Munoz, D. P. (2026). Real-time vectorcardiography simulator system. PloS one, 21(3), e0345612. https://doi.org/10.1371/journal.pone.0345612

BibTeX

@article{rodriguez2026real,
author = {Rodriguez, William R and Bernal, Hernan A and Amador-Munoz, Diana Patricia},
title = {{Real-time vectorcardiography simulator system}},
journal = {PloS one},
year = {2026},
month = mar,
volume = {21},
number = {3},
pages = {e0345612},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/journal.pone.0345612},
url = {https://doi.org/10.1371/journal.pone.0345612},
pmid = {41915705},
pmcid = {PMC13037978}
}

RIS

TY - JOUR
AU - Rodriguez, William R
AU - Bernal, Hernan A
AU - Amador-Munoz, Diana Patricia
TI - Real-time vectorcardiography simulator system
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/03/31
VL - 21
IS - 3
SP - e0345612
SN - 1932-6203
PB - PLOS
DO - 10.1371/journal.pone.0345612
UR - https://doi.org/10.1371/journal.pone.0345612
LA - en
ER -

CSL-JSON

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"family": "Amador-Munoz",
"given": "Diana Patricia"
}
],
"container-title-short": "PLoS One",
"volume": "21",
"issue": "3",
"page": "e0345612",
"DOI": "10.1371/journal.pone.0345612",
"PMID": "41915705",
"PMCID": "PMC13037978",
"ISSN": "1932-6203",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pone.0345612",
"language": "en",
"issued": {
"date-parts": [
[
2026,
3,
31
]
]
}
}

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