iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM
Abstract
:1. Introduction
- Develop iCOR, an end-to-end DL model with a comprehensive ECG signal processing system incorporating noise removal, feature extraction, learning process, and morphology classification;
- Combine noise removal and feature extraction of ECG signals using multi-layer filters with DL-based signal delineation of normal and abnormal pathology to enhance generalization capabilities; and
- Evaluate with unseen data to accurately assess its performance and accuracy.
2. Materials and Methods
2.1. Data Preparation
2.2. Data Pre-Processing
2.3. iCOR Proposed Model
2.4. Performance Metric Evaluation
2.5. Platform Implementation
3. Results and Discussion
3.1. ECG Denoising
3.2. ECG Delineation
- This study was only concerned with BW noise for the ECG denoising process using an MLF;
- The testing of iCOR was only explored in noisy ECG signals.
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Property | NSTDB | QTDB | LUDB |
---|---|---|---|
Frequency sampling | 360 Hz | 250 Hz | 500 Hz |
Number of channels | 2 | 2 | 12 |
The length of the signal (number of nodes) | 650,000 | 224,999 | 5000 |
Unit | mV | mV | mV |
Channel name | Noise1 and Noise2 | V2, mod.V1, ECG1, D4, CM4, CM5, V2-V3, V1-V2, V5, MLII, V1, V4, ECG2, V3, CM2, ML5, D3, V4-V5, and CC5 | i, ii, iii, aVR, aVL, aVF, and V1–V6 |
Number of records | 6 | 105 | 200 |
Pathology | ECG signal recording with baseline wander | Normal sinus Arrhythmia ST-change Supraventricular arrhythmia European ST-T Sudden death | Sinus rhythm Sinus tachycardia Sinus bradycardia Sinus arrhythmia Irregular sinus rhythm Abnormal rhythm |
Function | Denoising ground truth | Denoising and delineation process | Delineation process |
Database | Aim | ECG Records | ||
---|---|---|---|---|
Training Set | Validation Set | Testing Set (Unseen) | ||
NSTDB | Denoising | Noise1 (BW) | - | Noise 2 (BW) |
QTDB | Denoising and delineation (unseen) | Remaining records | Remaining records | MIT-BIH Arrhythmia (sel123 and sel233), MIT-BIH ST Change (sel302 and sel307), MIT-BIH Supraventricular Arrhythmia (sel820 and sel853), MIT-BIH Normal Sinus Rhythm (sel16420 and sel1679), European ST-T database (sele0106 and sele0121), Sudden death patients (sel32 and sel49), MIT-BIH Long-Term ECG (sel14046 and sel15814). |
LUDB | Delineation | Remaining records | Remaining records | Sinus rhythm (2), Sinus bradycardia (1), Sinus arrhythmia (22), Sinus tachycardia (70), Irregular sinus rhythm (108), Atrial Fibrillation (8, 38, 44, 51, 83, 88, 93, 95, 96, 101, 110, 112, 129, 173), and Atrial Flutter (35, 52, 103). |
Data | FE | Metrics Evaluation | Mean ± Standard Deviation | ||
---|---|---|---|---|---|
Training | Validation | Testing | |||
Non-shuffled | Raw | SSD | 1665.42 ± 5994.15 | 1399.01 ± 2697.22 | 163.23 ± 370.85 |
SNR | 14.17 ± 10.91 | 15.83 ± 10.84 | 17 ± 9.61 | ||
MAD | 1.77 ± 2.48 | 1.75 ± 2.15 | 0.84 ± 0.79 | ||
PRD | 209.08 ± 208.99 | 130.09 ± 167.42 | 103.75 ± 120.34 | ||
COS_SIM | 0.58 ± 0.33 | 0.62 ± 0.33 | 0.68 ± 0.29 | ||
Amplitude | SSD | 4.79 ± 9.83 | 24.32 ± 7.96 | 6.22 ± 7.3 | |
SNR | 23.89 ± 7.94 | 37.01 ± 7.94 | 22.77 ± 7.3 | ||
MAD | 0.31 ± 0.22 | 0.35 ± 0.25 | 0.41 ± 0.29 | ||
PRD | 45.83 ± 33.9 | 43.86 ± 27.32 | 53.1 ± 30.21 | ||
COS_SIM | 0.91 ± 0.1 | 0.91 ± 0.1 | 0.89 ± 0.1 | ||
Amplitude–isoelectric | SSD | 3.86 ± 8.89 | 5.1 ± 9.28 | 5.03 ± 7.81 | |
SNR | 25.15 ± 7.77 | 25.39 ± 7.75 | 23.39 ± 7.06 | ||
MAD | 0.31 ± 0.22 | 0.35 ± 0.26 | 0.41 ± 0.3 | ||
PRD | 39.67 ± 28.3 | 38.98 ± 27.59 | 50.39 ± 28.85 | ||
COS_SIM | 0.93 ± 0.09 | 0.93 ± 0.1 | 0.91 ± 0.1 | ||
Shuffled | Raw | SSD | 878 ± 4158.58 | 843.78 ± 4547.76 | 83.47 ± 117.33 |
SNR | 11.66 ± 8.02 | 11.64 ± 8.01 | 12.16 ± 7.02 | ||
MAD | 1.76 ± 2.08 | 1.74 ± 2.02 | 1.09 ± 0.6 | ||
PRD | 114.03 ± 64.23 | 114.46 ± 65.08 | 118.86 ± 57.36 | ||
COS_SIM | 0.65 ± 0.28 | 0.65 ± 0.28 | 0.61 ± 0.25 | ||
Amplitude | SSD | 4.48 ± 8.26 | 4.32 ± 7.88 | 5.49 ± 7.32 | |
SNR | 24.34 ± 7.84 | 37.02 ± 7.88 | 22.77 ± 7.32 | ||
MAD | 0.32 ± 0.22 | 0.35 ± 0.25 | 0.43 ± 0.29 | ||
PRD | 44.78 ± 34.2 | 45.21 ± 35.38 | 56.98 ± 37.66 | ||
COS_SIM | 0.92 ± 0.09 | 0.91 ± 0.1 | 0.9 ± 0.1 | ||
Amplitude–isoelectric | SSD | 3.6 ± 7.15 | 3.6 ± 7.11 | 4.81 ± 7.29 | |
SNR | 25.55 ± 7.75 | 25.46 ± 7.81 | 23.46 ± 7.22 | ||
MAD | 0.31 ± 0.22 | 0.35 ± 0.26 | 0.43 ± 0.3 | ||
PRD | 37.72 ± 27.3 | 37.77 ± 27.2 | 53.05 ± 38.42 | ||
COS_SIM | 0.94 ± 0.08 | 0.94 ± 0.09 | 0.91 ± 0.1 |
AF Records | Class | Performance Results (%) | ||||
---|---|---|---|---|---|---|
Accuracy | Sensitivity | Specificity | Precision | F1 | ||
8 | P-wave | 99.7 | 0 | 99.7 | 0 | 0 |
QRS-complex | 96.5 | 77.8 | 99.6 | 96.9 | 86.3 | |
T-wave | 96.8 | 91.3 | 97.9 | 90.3 | 90.8 | |
38 | P-wave | 94.4 | 0 | 94.4 | 0 | 0 |
QRS-complex | 95.5 | 76 | 99.5 | 96.6 | 85 | |
T-wave | 84.7 | 16.8 | 99.7 | 91.4 | 28.3 | |
44 | P-wave | 97.8 | 0 | 97.8 | 0 | 0 |
QRS-complex | 96.9 | 80.2 | 99.6 | 97.2 | 87.9 | |
T-wave | 94 | 73.7 | 98 | 87.7 | 80.1 | |
51 | P-wave | 97.2 | 0 | 97.2 | 0 | 0 |
QRS-complex | 96.5 | 77.2 | 99.9 | 99.1 | 86.8 | |
T-wave | 95.6 | 76.4 | 98.8 | 91.6 | 83.3 | |
83 | P-wave | 96.3 | 0 | 96.3 | 0 | 0 |
QRS-complex | 89.1 | 40.3 | 100 | 99.8 | 57.5 | |
T-wave | 87.1 | 77 | 89.9 | 67.7 | 72 | |
88 | P-wave | 96.6 | 0 | 96.6 | 0 | 0 |
QRS-complex | 97.9 | 89.7 | 99.1 | 93.5 | 91.6 | |
T-wave | 81.4 | 24 | 95.6 | 57.7 | 33.9 | |
93 | P-wave | 97.1 | 0 | 97.1 | 0 | 0 |
QRS-complex | 94.3 | 62.5 | 99.7 | 97.7 | 76.3 | |
T-wave | 93.7 | 85.5 | 95.4 | 79.2 | 82.2 | |
95 | P-wave | 99.6 | 0 | 99.6 | 0 | 0 |
QRS-complex | 93.7 | 53.7 | 99.2 | 90.2 | 67.3 | |
T-wave | 89.4 | 73.8 | 93.3 | 73.8 | 73.8 | |
96 | P-wave | 96.1 | 0 | 96.1 | 0 | 0 |
QRS-complex | 98.2 | 92.8 | 99 | 92.5 | 92.6 | |
T-wave | 87.4 | 40.3 | 98.9 | 89.7 | 55.6 | |
101 | P-wave | 93.8 | 0 | 93.8 | 0 | 0 |
QRS-complex | 96.7 | 80 | 99.6 | 97.2 | 87.7 | |
T-wave | 89.4 | 63.8 | 96.6 | 83.8 | 72.4 | |
110 | P-wave | 98.9 | 0 | 98.9 | 0 | 0 |
QRS-complex | 95.2 | 69.6 | 99.6 | 96.4 | 80.8 | |
T-wave | 88.3 | 56.6 | 97.8 | 88.3 | 69 | |
112 | P-wave | 92.1 | 0 | 92.1 | 0 | 0 |
QRS-complex | 98.1 | 91.9 | 99.2 | 95 | 93.4 | |
T-wave | 87 | 37 | 97.2 | 73.5 | 49.2 | |
129 | P-wave | 97.7 | 0 | 97.7 | 0 | 0 |
QRS-complex | 97.8 | 77.3 | 99.7 | 95.9 | 85.6 | |
T-wave | 94.7 | 83.7 | 96.9 | 84.2 | 84 | |
173 | P-wave | 96.3 | 0 | 96.3 | 0 | 0 |
QRS-complex | 97.9 | 88.1 | 99.4 | 95.6 | 91.7 | |
T-wave | 93.4 | 73.4 | 98.7 | 93.9 | 82.4 |
AFL Records | Class | Accuracy | Sensitivity | Specificity | Precision | F1 |
---|---|---|---|---|---|---|
35 | P-wave | 92.3 | 0 | 92.3 | 0 | 0 |
QRS-complex | 95.1 | 83 | 98.9 | 96.1 | 89.1 | |
T-wave | 80.7 | 36.2 | 99.8 | 98.8 | 53 | |
52 | P-wave | 96.9 | 0 | 96.9 | 0 | 0 |
QRS-complex | 97.3 | 79.4 | 99.7 | 97.3 | 87.4 | |
T-wave | 90.5 | 72.8 | 94.2 | 71.8 | 72.3 | |
103 | P-wave | 86.2 | 0 | 86.2 | 0 | 0 |
QRS-complex | 96.9 | 91 | 97.9 | 87.8 | 89.4 | |
T-wave | 85.6 | 49.6 | 95.1 | 72.6 | 58.9 |
Authors | Method | Database | Accuracy (%) | |||
---|---|---|---|---|---|---|
ECG Denoising | ECG Delineation | P-Wave | QRS-Complex | T-Wave | ||
Tutuko et al. [21] | DAE | BiLSTM | QTDB (normal sinus records only) | 99.81 | 99.86 | 99.35 |
Chen et al. [40] | Regularized latent space (encoder–decoder) | ECGVEDNET | ICDIRS | - | 86.28 | 89.94 |
Present study | iCOR | LUDB | 98 | 98.8 | 98 | |
QTDB (normal and abnormal records) | 98.7 | 98.1 | 97 |
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Share and Cite
Nurmaini, S.; Jatmiko, W.; Mandala, S.; Tutuko, B.; Erwin, E.; Tondas, A.E.; Darmawahyuni, A.; Firdaus, F.; Rachmatullah, M.N.; Sapitri, A.I.; et al. iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM. Algorithms 2025, 18, 236. https://doi.org/10.3390/a18040236
Nurmaini S, Jatmiko W, Mandala S, Tutuko B, Erwin E, Tondas AE, Darmawahyuni A, Firdaus F, Rachmatullah MN, Sapitri AI, et al. iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM. Algorithms. 2025; 18(4):236. https://doi.org/10.3390/a18040236
Chicago/Turabian StyleNurmaini, Siti, Wisnu Jatmiko, Satria Mandala, Bambang Tutuko, Erwin Erwin, Alexander Edo Tondas, Annisa Darmawahyuni, Firdaus Firdaus, Muhammad Naufal Rachmatullah, Ade Iriani Sapitri, and et al. 2025. "iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM" Algorithms 18, no. 4: 236. https://doi.org/10.3390/a18040236
APA StyleNurmaini, S., Jatmiko, W., Mandala, S., Tutuko, B., Erwin, E., Tondas, A. E., Darmawahyuni, A., Firdaus, F., Rachmatullah, M. N., Sapitri, A. I., Islami, A., Arum, A. W., & Perwira, M. I. (2025). iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM. Algorithms, 18(4), 236. https://doi.org/10.3390/a18040236