Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System
Abstract
:1. Introduction
- The Lead II signals were normalized and filtered to reduce the coupled noise (Section 2.2).
- The positions of QRS-complexes in Lead II were detected and marked via a well-trained SVM. Two waveforms of each heartbeat in Lead II and V1 were individually extracted according the markers in Lead II (Section 2.3).
- The extracted waveform was used as a feature to recognize the arrhythmic type of a heartbeat. In this configuration, a self-constructing neural fuzzy inference network (SoNFIN) was used to recognize the arrhythmic type of the heartbeat using the raw Lead II and V1 signals (Section 2.4).
2. Experimental Section
2.1. Database
2.2. Filtering and Normalization
2.3. QR-Complexes Detection and Waveform Extraction
2.3.1. Support Vector Machine (SVM)
2.3.2. Training Phase of SVM
2.3.3. Test and Post-Processing Phases of SVM
2.4. Arrhythmia Classification
2.4.1. Self-Constructing Neural Fuzzy Inference Network (SoNFIN)
- Layer 1: No computation is performed in this layer. Each node in this layer corresponds to one input variable. Only transmitted input values are forwarded to the next layer directly:
- Layer 2: For fuzzy set Aij, a Gaussian membership function is used to describe the degree that the input variable lj belongs to the i-th fuzzy set. Its mathematical function is defined as follows:
- Layer 3: A node in this layer represents one fuzzy logic rule and performs precondition matching of a rule. Here we use the following product operation for each Layer-3 node:
- Layer 4: Nodes in this layer are called consequent nodes. Each node is linked to Layer-3 output, and the linear association of the weight in this layer is as follows:
- Layer 5: Each node in this layer corresponds to one output variable. The node integrates all the actions recommended by Layer 5 and acts as a defuzzifier with:
2.4.2. Training and Test Phases of SoNFIN
3. Results and Discussion
4. Conclusions
Acknowledgments
References
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N | V | R | L | A | total | |
---|---|---|---|---|---|---|
105 | 401 | 15 | 0 | 0 | 0 | 416 |
106 | 312 | 2 | 0 | 0 | 0 | 314 |
108 | 275 | 5 | 0 | 0 | 2 | 282 |
109 | 0 | 7 | 0 | 411 | 2 | 420 |
111 | 0 | 0 | 0 | 343 | 0 | 343 |
112 | 428 | 0 | 0 | 0 | 0 | 428 |
113 | 288 | 0 | 0 | 0 | 1 | 289 |
115 | 316 | 0 | 0 | 0 | 0 | 316 |
116 | 384 | 11 | 0 | 0 | 0 | 395 |
118 | 0 | 3 | 347 | 0 | 11 | 361 |
119 | 245 | 80 | 0 | 0 | 0 | 325 |
121 | 301 | 0 | 0 | 0 | 0 | 301 |
122 | 421 | 0 | 0 | 0 | 0 | 421 |
201 | 441 | 0 | 0 | 0 | 0 | 441 |
202 | 261 | 4 | 0 | 0 | 0 | 265 |
205 | 449 | 3 | 0 | 0 | 0 | 452 |
207 | 0 | 0 | 0 | 349 | 0 | 349 |
208 | 242 | 245 | 0 | 0 | 0 | 487 |
209 | 365 | 0 | 0 | 0 | 178 | 543 |
212 | 140 | 0 | 319 | 0 | 0 | 459 |
213 | 501 | 48 | 0 | 0 | 0 | 549 |
214 | 0 | 33 | 0 | 346 | 1 | 380 |
219 | 364 | 15 | 0 | 0 | 0 | 379 |
220 | 352 | 0 | 0 | 0 | 1 | 353 |
221 | 327 | 80 | 0 | 0 | 0 | 407 |
222 | 366 | 0 | 0 | 0 | 0 | 366 |
223 | 390 | 16 | 0 | 0 | 0 | 406 |
228 | 312 | 18 | 0 | 0 | 0 | 330 |
230 | 392 | 0 | 0 | 0 | 0 | 392 |
231 | 13 | 1 | 287 | 0 | 0 | 301 |
232 | 0 | 0 | 330 | 0 | 0 | 330 |
233 | 372 | 138 | 0 | 0 | 4 | 514 |
234 | 462 | 0 | 0 | 0 | 0 | 462 |
Total | 9,120 | 724 | 1,283 | 1,449 | 200 | 12,776 |
No. | TP | FN | FP |
---|---|---|---|
105 | 416 | 0 | 24 |
106 | 312 | 2 | 26 |
108 | 282 | 0 | 37 |
109 | 419 | 1 | 10 |
111 | 343 | 0 | 186 |
112 | 428 | 0 | 81 |
113 | 289 | 0 | 0 |
115 | 316 | 0 | 0 |
116 | 395 | 0 | 4 |
118 | 361 | 0 | 11 |
119 | 325 | 0 | 12 |
121 | 301 | 0 | 20 |
122 | 421 | 0 | 0 |
201 | 441 | 0 | 0 |
202 | 265 | 0 | 1 |
205 | 452 | 0 | 0 |
207 | 349 | 0 | 13 |
208 | 480 | 7 | 11 |
209 | 541 | 2 | 52 |
212 | 455 | 4 | 9 |
213 | 548 | 1 | 4 |
214 | 376 | 4 | 8 |
219 | 378 | 1 | 9 |
220 | 353 | 0 | 0 |
221 | 407 | 0 | 0 |
222 | 366 | 0 | 5 |
223 | 406 | 0 | 0 |
228 | 330 | 0 | 1 |
230 | 392 | 0 | 0 |
231 | 301 | 0 | 41 |
232 | 330 | 0 | 3 |
233 | 514 | 0 | 4 |
234 | 462 | 0 | 0 |
Total | 12,754 | 22 | 572 |
N | Estimate | Sensitivity | Specificity | Accuracy | ||
---|---|---|---|---|---|---|
N | Non_N | |||||
Real | N | 9,189 | 107 | 98.8% | 99.2% | 98.9% |
Non_N | 25 | 3,433 | ||||
V | Estimate | 95.1% | 99.4% | 99.1% | ||
V | Non_V | |||||
Real | V | 684 | 35 | |||
Non_V | 72 | 11,998 | ||||
R | Estimate | 99.7% | 99.8% | 99.8% | ||
R | Non_R | |||||
Real | R | 1,287 | 3 | |||
Non_R | 20 | 11,444 | ||||
L | Estimate | 97.9% | 99.4% | 99.3% | ||
L | non_L | |||||
Real | L | 1,419 | 30 | |||
Non_L | 58 | 11,247 | ||||
Averaged accuracy | 98.8% |
N | Estimate | Sensitivity | Specificity | Accuracy | ||
---|---|---|---|---|---|---|
N | Non_N | |||||
Real | N | 9,189 | 107 | 98.8% | 96.9% | 98.2% |
Non_N | 121 | 3,909 | ||||
V | Estimate | 95.1% | 98.1% | 97.9% | ||
V | Non_V | |||||
Real | V | 684 | 35 | |||
Non_V | 239 | 12,368 | ||||
R | Estimate | 99.7% | 99.7% | 99.7% | ||
R | Non_R | |||||
Real | R | 1,287 | 3 | |||
Non_R | 31 | 12,005 | ||||
L | Estimate | 97.9% | 99.2% | 99.1% | ||
L | non_L | |||||
Real | L | 1,419 | 30 | |||
Non_L | 85 | 11,792 | ||||
noise | Estimate | 47.7% | 100% | 97.7% | ||
noise | Non_noise | |||||
Real | n | 271 | 301 | |||
Non_noise | 0 | 12,754 | ||||
Averaged accuracy | 96.4% |
Reference | Method | Accuracy (%) |
---|---|---|
Proposed algorithm | SVM | 97.5% |
J. Pan, and W. J. Tompkins [4] | Dynamic threshold | 99.3% |
P. E. Trahanias [5] | Mathematical morphology | 99.48% |
F. Gritzali [7] | Length and energy transformation | 99.6% |
Y. -C. Yeh, and W. -J. Wanga [8] | Difference operation method | 99.81 |
M. Adnane et al. [28] | Morphological features | 99.64% |
M. Paoletti and C. Marchesi [32] | Karhunen-Loève transform | 99.15% |
S. S. Mehta and N. S. Lingayat [9] | SVM | 98.12% |
S. S. Mehta et al. [17] | K-mean | 98.66% |
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Liu, S.-H.; Cheng, D.-C.; Lin, C.-M. Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System. Sensors 2013, 13, 813-828. https://doi.org/10.3390/s130100813
Liu S-H, Cheng D-C, Lin C-M. Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System. Sensors. 2013; 13(1):813-828. https://doi.org/10.3390/s130100813
Chicago/Turabian StyleLiu, Shing-Hong, Da-Chuan Cheng, and Chih-Ming Lin. 2013. "Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System" Sensors 13, no. 1: 813-828. https://doi.org/10.3390/s130100813
APA StyleLiu, S.-H., Cheng, D.-C., & Lin, C.-M. (2013). Arrhythmia Identification with Two-Lead Electrocardiograms Using Artificial Neural Networks and Support Vector Machines for a Portable ECG Monitor System. Sensors, 13(1), 813-828. https://doi.org/10.3390/s130100813