# Automated Arrhythmia Detection Based on RR Intervals

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## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Electrocardiogram Data

- 51–60 years representing 19.82%;
- 61–70 years representing 24.38%;
- 71–80 years representing 16.90%.

#### 2.2. QRS Detection

#### 2.3. Data Partitioning and Patient Scrambling

#### 2.4. Detrending

#### 2.5. Round Robin Windowing and Puncturing

#### 2.6. ResNet 10-Fold and Cross-Validation

#### 2.7. Result Analysis Methods

## 3. Results

## 4. Discussion

#### 4.1. Limitations

#### 4.2. Future Work

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

ACC | Accuracy |

AFL | Atrial Flutter |

AFIB | Atrial Fibrillation |

AI | Artificial Intelligence |

API | Application Programming Interface |

AVN | AtrioVentricular Node |

CAD | Computer-Aided-Diagnosis |

DL | Deep Learning |

DB | Database |

DL | Deep Learning |

ECG | Electrocardiogram |

FN | False Negative |

FP | False Positive |

LSTM | Long Short-Term Memory |

NSR | Normal Sinus Rhythm |

ResNet | Residual Neural Network |

ROC | Receiver Operating Characteristic |

SAN | SinoAtrial Node |

SEN | Sensitivity |

SPE | Specificity |

TN | True Negative |

TP | True Positive |

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**Figure 2.**Example plots from AFIB, AFL, and NSR signal classes. The ECG signal was measured with the aVL lead. The RR intervals, plotted as RR intervals over time, were derived from the ECG via QRS detection. The detrended RR intervals were plotted as RR_DT over time. Visual inspection indicates that the AFIB RR (s) signal shows an additional beat, which has been encircled with a dashed ellipse.

**Figure 3.**ResNet structure used for training and testing: (

**a**) Network super structure; (

**b**) Block structure.

**Table 1.**Data properties for the three signal classes. The ‘ECG Duration (s)’ column provides the time duration of all ECG signal blocks for each individual class. After that, the two columns to the right provide the number of RR intervals and the number of RR_DT samples, respectively. The last two columns on the right provide the number of blocks and number of patients for each signal class.

Property | ECG Duration (s) | RR Intervals | RR_DT Samples | Number of Blocks | Number of Patients | |
---|---|---|---|---|---|---|

Class | ||||||

NSR | 18,260 | 33,976 | 33,976 | 1826 | 1826 | |

AFIB | 17,800 | 25,995 | 25,995 | 1780 | 1780 | |

AFL | 4450 | 7536 | 7536 | 445 | 445 | |

Total | 40,510 | 67,507 | 67,507 | 4051 | 4051 |

**Table 2.**The number of RR intervals per signal class in each part. AFL${}_{\mathrm{SC}}$ denotes the scrambled AFL dataset.

Part | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|

Class | |||||||||||

NSR | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 | |

AFIB | 2651 | 2667 | 2584 | 2566 | 2633 | 2649 | 2594 | 2512 | 2604 | 2535 | |

AFL | 742 | 759 | 786 | 784 | 762 | 766 | 727 | 702 | 721 | 787 | |

AFL${}_{\mathrm{SC}}$ | 2226 | 2277 | 2358 | 2352 | 2286 | 2298 | 2181 | 2106 | 2163 | 2361 |

**Table 3.**The number of data vectors per signal class in each part. AFL${}_{\mathrm{P}}$ and AFIB${}_{\mathrm{P}}$ denote the punctured datasets for NSR and AFIB, respectively.

Part | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
---|---|---|---|---|---|---|---|---|---|---|---|

Class | |||||||||||

AFIB | 2651 | 2667 | 2584 | 2566 | 2633 | 2649 | 2594 | 2512 | 2604 | 2535 | |

AFL${}_{\mathrm{SC}}$ | 2226 | 2277 | 2358 | 2352 | 2286 | 2298 | 2181 | 2106 | 2163 | 2361 | |

NSR | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 | |

AFL${}_{\mathrm{P}}$ | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 | |

AFIB${}_{\mathrm{P}}$ | 2015 | 1980 | 1980 | 2029 | 2020 | 1973 | 1992 | 2017 | 1975 | 1975 |

Fold | Training Data | Testing Data | ||||||
---|---|---|---|---|---|---|---|---|

NSR | AFIB | AFL | Total | NSR | AFIB | AFL | Total | |

1 | 17,941 | 17,941 | 17,941 | 53,823 | 2015 | 2651 | 2226 | 6892 |

2 | 17,976 | 17,976 | 17,976 | 53,928 | 1980 | 2667 | 2277 | 6924 |

3 | 17,976 | 17,976 | 17,976 | 53,928 | 1980 | 2584 | 2358 | 6922 |

4 | 17,927 | 17,927 | 17,927 | 53,781 | 2029 | 2566 | 2352 | 6947 |

5 | 17,936 | 17,936 | 17,936 | 53,808 | 2020 | 2633 | 2286 | 6939 |

6 | 17,983 | 17,983 | 17,983 | 53,949 | 1973 | 2649 | 2298 | 6920 |

7 | 17,964 | 17,964 | 17,964 | 53,892 | 1992 | 2594 | 2181 | 6767 |

8 | 17,939 | 17,939 | 17,939 | 53,817 | 2017 | 2512 | 2106 | 6635 |

9 | 17,981 | 17,981 | 17,981 | 53,943 | 1975 | 2604 | 2163 | 6742 |

10 | 17,981 | 17,981 | 17,981 | 53,943 | 1975 | 2535 | 2361 | 6871 |

Predicted Label | ||||
---|---|---|---|---|

AFIB | AFL | NSR | ||

AFIB | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | ${N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | |

True Label | AFL | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | ${N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ |

NSR | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ | ${N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ |

Predicted Label | |||
---|---|---|---|

Arrhythmia | Non-Arrhythmia | ||

Arrhythmia | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}+{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}$ | |

True Label | $+{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}+{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | $+{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}$ | |

Non-Arrhythmia | ${N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}+{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ | ${N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}$ |

**Table 7.**The average cross-validation confusion matrix. $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}$ indicates the sum over all Test Folds.

Predicted Label | ||||
---|---|---|---|---|

AFIB | AFL | NSR | ||

AFIB | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFIB}}\right\$ | |

True Label | AFL | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{AFL}}\right\$ |

NSR | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFIB},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{AFL},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}\right\$ | $\sum _{\langle \mathrm{Test}\phantom{\rule{4.pt}{0ex}}\mathrm{Fold}\rangle}}\left\{{N}_{\mathrm{NSR},\phantom{\rule{4.pt}{0ex}}\mathrm{NSR}}\right\$ |

Fold | $\mathit{cl}$ | ACC${}_{\mathit{cl}}$ (%) | SEN${}_{\mathit{cl}}$ (%) | SPE${}_{\mathit{cl}}$ (%) | Confusion Matrix | ||
---|---|---|---|---|---|---|---|

AFIB | 97.16 | 92.72 | 99.27 | 2064 | 162 | 0 | |

1 | AFL | 97.16 | 98.72 | 96.18 | 34 | 2617 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2015 | |

AFIB | 99.87 | 99.60 | 100.00 | 2268 | 9 | 0 | |

2 | AFL | 99.87 | 100.00 | 99.79 | 0 | 2667 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1980 | |

AFIB | 95.81 | 87.70 | 100.00 | 2068 | 290 | 0 | |

3 | AFL | 95.81 | 100.00 | 93.31 | 0 | 2584 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1980 | |

AFIB | 96.95 | 91.11 | 99.93 | 2143 | 209 | 0 | |

4 | AFL | 96.95 | 99.88 | 95.23 | 3 | 2563 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2029 | |

AFIB | 98.83 | 96.98 | 99.74 | 2217 | 69 | 0 | |

5 | AFL | 98.83 | 99.54 | 98.40 | 12 | 2621 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2020 | |

AFIB | 100.00 | 100.00 | 100.00 | 2298 | 0 | 0 | |

6 | AFL | 99.96 | 100.00 | 99.93 | 0 | 2649 | 0 |

NSR | 99.96 | 99.85 | 100.00 | 0 | 3 | 1970 | |

AFIB | 96.81 | 90.10 | 100.00 | 1965 | 216 | 0 | |

7 | AFL | 96.81 | 100.00 | 94.82 | 0 | 2594 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1992 | |

AFIB | 94.32 | 83.05 | 99.56 | 1749 | 357 | 0 | |

8 | AFL | 94.32 | 99.20 | 91.34 | 20 | 2492 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 2017 | |

AFIB | 98.28 | 95.42 | 99.63 | 2064 | 99 | 0 | |

9 | AFL | 98.15 | 99.35 | 97.39 | 17 | 2587 | 0 |

NSR | 99.86 | 99.54 | 100.00 | 0 | 9 | 1966 | |

AFIB | 100.00 | 100.00 | 100.00 | 2361 | 0 | 0 | |

10 | AFL | 100.00 | 100.00 | 100.00 | 0 | 2535 | 0 |

NSR | 100.00 | 100.00 | 100.00 | 0 | 0 | 1975 | |

AFIB | 97.82 | 93.76 | 99.81 | 21,197 | 1411 | 0 | |

All | AFL | 97.80 | 99.67 | 96.66 | 86 | 25,909 | 0 |

NSR | 99.98 | 99.94 | 100.00 | 0 | 12 | 19,944 |

ACC${}_{\mathit{cl}}$ (%) | SEN${}_{\mathit{cl}}$ (%) | SPE${}_{\mathit{cl}}$ (%) | Confusion Matrix | |
---|---|---|---|---|

99.98 | 99.94 | 100.00 | 48,603 | 0 |

12 | 19,944 |

**Table 10.**Selected arrhythmia detection studies using RR intervals and ECG. pDB used were: MIT-BIH Atrial Fibrillation Database (afdb), MIT-BIH Arrhythmia Database (mitdb), MIT-BIH Malignant Ventricular Arrhythmia Database (vfdb), Creighton University Ventricular Tachyarrhythmia Database (cudb), MIT-BIH Normal Sinus Rhythm Database (nsrdb), MIT-BIH Long Term Database (ltdb), European ST-T Database (edb), and ecgdb. Hospital data come from non-publicly accessible databases.

Author Year | Method | Data | Performance | ||||
---|---|---|---|---|---|---|---|

Type | DB | Rhythm | ACC | SPE | SEN | ||

Current | Detrending, ResNet | RR | ecgdb | AFIB AFL NSR | 99.98 | 100.00 | 99.94 |

Faust and Acharya 2021 [30] | Detrending, ResNet | RR | ecgdb | SVT, ST, SB, AFIB, AFL, NSR | 98.55 | 94.30 | 99.40 |

Ivanovic et al. 2019 [29] | CNN, LSTM | RR | Hospital | NSR, AFIB AFL | 88 | 87.09 | |

Fujita et al. 2019 [31] | CNN with normalization | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 98.45 | 99.87 | 99.27 |

Faust et al. 2018 [32] | LSTM | RR | afdb | AFIB NSR | 98.39 | 98.32 | 98.51 |

Acharya et al. 2017 [33] | CNN with Z-score | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 92.50 | 98.09 | 93.13 |

Henzel et al. 2017 [34] | Statistical features with generalized Linear Model | RR | afdb | AFIB NSR | 93 | 95 | 90 |

Desai et al. 2016 [35] | RQA with DecisionTree, RandomForest, RotationForest | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 98.37 | ||

Acharya et al. 2016 [36] | Thirteen nonlinear features with ANOVA with KNN and DT | ECG | afdb, mitdb, vfdb | AFIB, AFL, VFIB, NSR | 97.78 | 99.76 | 98.82 |

Hamed et al. 2016 [37] | DWT, PCA and SVM | ECG | afdb | AFIB, AFL, NSR | 98.43 | 96.89 | 98.96 |

Xia et al. 2018 [38] | STFT/SWT with CNN | ECG | afdb | AFIB | 98.63 | 98.79 | 97.87 |

Petrenas et al. 2015 [39] | Median filter with threshold | RR | nsrdb, afdb | AFIB NSR | 98.3 | 97.1 | |

Zhou et al. 2014 [40] | Median filter & Shannon entropy with threshold | RR | ltafdb, afdb, nsrdb | AFIB NSR | 96.05 | 95.07 | 96.72 |

Muthuchudar et al. 2013 [41] | UWT NN | ECG | afdb | AFIB, VFIB, NSR | 96 | ||

Yuan et al. 2016 [42] | Unsupervised autoencoder NN Softmax regression | ECG | afdb, nsrdb, ltdb, hospital | AFIB | 98.18 | 98.22 | 98.11 |

Dinakarrao et al. 2018 [43] | Daubechies-6 with counters Anomaly detector | ECG | mitdb | AFIB, VFIB | 99.19 | 98.25 | 78.70 |

Salem et al. 2018 [44] | Spectogram with CNN | ECG | afdb nsrdb vfdb edb | AFIB, AFL VFIB NSR | 97.23 |

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**MDPI and ACS Style**

Faust, O.; Kareem, M.; Ali, A.; Ciaccio, E.J.; Acharya, U.R. Automated Arrhythmia Detection Based on RR Intervals. *Diagnostics* **2021**, *11*, 1446.
https://doi.org/10.3390/diagnostics11081446

**AMA Style**

Faust O, Kareem M, Ali A, Ciaccio EJ, Acharya UR. Automated Arrhythmia Detection Based on RR Intervals. *Diagnostics*. 2021; 11(8):1446.
https://doi.org/10.3390/diagnostics11081446

**Chicago/Turabian Style**

Faust, Oliver, Murtadha Kareem, Ali Ali, Edward J. Ciaccio, and U. Rajendra Acharya. 2021. "Automated Arrhythmia Detection Based on RR Intervals" *Diagnostics* 11, no. 8: 1446.
https://doi.org/10.3390/diagnostics11081446